FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains | Paper Citation Record

Observation 292828dc-0373-4e1e-83c7-540f92fda9b7 · outbound

This paper cites Fish- finder: A robust small target detection method for aquaculture fish in low-quality underwater images,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish- finder: A robust small target detection method for aquaculture fish in low-quality underwater images,

Reference 1

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This paper cites Real-time fish detection in complex backgrounds using probabilistic background modelling,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Real-time fish detection in complex backgrounds using probabilistic background modelling,

Reference 2

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This paper cites Underwater object detection: architectures and algorithms – a comprehensive review,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Underwater object detection: architectures and algorithms – a comprehensive review,

Reference 3

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This paper cites Fish detection under occlusion using modified you only look once v8 integrating real- time detection transformer features,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish detection under occlusion using modified you only look once v8 integrating real- time detection transformer features,

Reference 4

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This paper cites Automated underwater fish species recognition using deep learning-based techniques,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Automated underwater fish species recognition using deep learning-based techniques,

Reference 5

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This paper cites Underwater fish detection and classification using deep learning,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Underwater fish detection and classification using deep learning,

Reference 6

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Observation acd04dde-e724-4f94-afc8-d7ccdd721095 · outbound

This paper cites Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning,

Reference 7

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Observation 84f471a0-dc63-49c0-82cd-4eaf2aa39bc7 · outbound

This paper cites Depondfi’23 challenge on real-time pond environ- ment: Methods and results,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Depondfi’23 challenge on real-time pond environ- ment: Methods and results,

Reference 8

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Observation a706aa59-b8b4-4fb1-9297-8f9e96c471b5 · outbound

This paper cites A benchmark dataset and ensemble yolo method for enhanced underwater fish detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A benchmark dataset and ensemble yolo method for enhanced underwater fish detection,

Reference 9

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Observation d68a1e63-e9b3-41f8-8c38-e1852cbf3c8f · outbound

This paper cites A feature- enhanced and adaptive routing framework for fish school detection on auvs for degraded underwater imaging environments,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A feature- enhanced and adaptive routing framework for fish school detection on auvs for degraded underwater imaging environments,

Reference 10

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This paper cites Fishdet-yolo: Enhanced underwater fish detection with richer gradient flow and long-range dependency capture through mamba-c2f,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fishdet-yolo: Enhanced underwater fish detection with richer gradient flow and long-range dependency capture through mamba-c2f,

Reference 11

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This paper cites Yolov8-tf: Transformer-enhanced yolov8 for underwater fish species recognition with class imbalance handling,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Yolov8-tf: Transformer-enhanced yolov8 for underwater fish species recognition with class imbalance handling,

Reference 12

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Observation 8e306cf5-3a9e-453b-ae1e-56b1709959e7 · outbound

This paper cites Intelligent deep learning based automated fish detection model for uwsn,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Intelligent deep learning based automated fish detection model for uwsn,

Reference 13

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This paper cites Deep fish: An approach to fish species identification through deep learning techniques,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Deep fish: An approach to fish species identification through deep learning techniques,

Reference 14

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Observation 7e473bee-0f20-46c9-b84a-57b435416f4c · outbound

This paper cites A 3d occlusion tracking model of the underwater fish targets,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A 3d occlusion tracking model of the underwater fish targets,

Reference 15

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This paper cites A multitask model for realtime fish detection and segmentation based on YOLOv5,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A multitask model for realtime fish detection and segmentation based on YOLOv5,

Reference 16

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Observation 23c6efc5-f1e6-4dea-94e0-b56f420420e7 · outbound

This paper cites Fishtrack23: An ensemble underwater dataset for multi- object tracking,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fishtrack23: An ensemble underwater dataset for multi- object tracking,

Reference 17

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This paper cites Ultralytics yolov8,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Ultralytics yolov8,

Reference 18

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Observation 2747b846-16c4-46c1-9aaa-ee9bcfb949f6 · outbound

This paper cites Cascade r-cnn: High quality object detection and instance segmentation,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Cascade r-cnn: High quality object detection and instance segmentation,

Reference 19

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This paper cites End-to-end object detection with transformers,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains End-to-end object detection with transformers,

Reference 20

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This paper cites Automated fish detection in underwater environments: Performance analysis of yolov8 and yolo- nas,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Automated fish detection in underwater environments: Performance analysis of yolov8 and yolo- nas,

Reference 21

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Observation fe302f10-de5b-4eb7-a8e0-945d93121a47 · outbound

This paper cites Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis,

Reference 22

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This paper cites Real-time and resource-efficient multi-scale adaptive robotics vision for underwater object detection and domain generalization,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Real-time and resource-efficient multi-scale adaptive robotics vision for underwater object detection and domain generalization,

Reference 23

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Observation e8f206be-73dc-492f-bc90-e969da8f61ce · outbound

This paper cites Enhanced fish species detection and classification using a novel deep learning approach,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Enhanced fish species detection and classification using a novel deep learning approach,

Reference 24

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Observation 276d1619-3cb5-4265-bde3-93afc5e2d8e2 · outbound

This paper cites Overview of the lifeclef 2014 fish task,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Overview of the lifeclef 2014 fish task,

Reference 25

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Observation 81a7cbd7-97aa-4961-a637-70d460d1ef48 · outbound

This paper cites Underwater detection: A brief survey and a new multitask dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Underwater detection: A brief survey and a new multitask dataset,

Reference 26

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This paper cites Take good care of your fish: fish re-identification with synchronized multi-view camera system,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Take good care of your fish: fish re-identification with synchronized multi-view camera system,

Reference 27

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This paper cites A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis,

Reference 28

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Observation ec2d834a-80b2-4970-a1cb-25c7b9f95552 · outbound

This paper cites Ozfish dataset - machine learning dataset for baited remote underwater video stations,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Ozfish dataset - machine learning dataset for baited remote underwater video stations,

Reference 29

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This paper cites Faster r-cnn: towards real-time object detection with region proposal networks,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Faster r-cnn: towards real-time object detection with region proposal networks,

Reference 30

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Observation e470cd12-0501-4a59-9279-a10cc8b17934 · outbound

This paper cites Seeing through the haze: A comprehensive review of underwater image enhancement techniques,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Seeing through the haze: A comprehensive review of underwater image enhancement techniques,

Reference 31

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Observation 60839c29-73df-4dbb-b85c-29eb3a4c5817 · outbound

This paper cites Brackishmot: The brackish multi-object tracking dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Brackishmot: The brackish multi-object tracking dataset,

Reference 32

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Observation be0af5b9-b563-4958-b11c-42db4e51a8af · outbound

This paper cites TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris

Reference 33

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This paper cites Watermask: Instance segmentation for underwater imagery,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Watermask: Instance segmentation for underwater imagery,

Reference 34

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Observation e734b1f8-97ec-470d-83e6-b0417aee2630 · outbound

This paper cites Eornet: An improved rotating box detection model for counting juvenile fish under occlusion and overlap,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Eornet: An improved rotating box detection model for counting juvenile fish under occlusion and overlap,

Reference 35

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Observation 19d3c3ba-40c5-4da2-8944-e79e1a58704d · outbound

This paper cites Research on realizing the 3d occlusion tracking location method of fish’s school target,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Research on realizing the 3d occlusion tracking location method of fish’s school target,

Reference 36

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Observation 7a3f4311-e695-4325-a8b3-17593629919b · outbound

This paper cites Fish classification using deep learning on small scale and low-quality images,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish classification using deep learning on small scale and low-quality images,

Reference 37

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Observation a6faa17c-2869-4f2f-8267-e5f4de7cd406 · outbound

This paper cites Improving transfer learning and squeeze- and-excitation networks for small-scale fine-grained fish image classification,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Improving transfer learning and squeeze- and-excitation networks for small-scale fine-grained fish image classification,

Reference 38

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Observation e681c96c-a8e0-4732-ac93-7afd6b342232 · outbound

This paper cites Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review,

Reference 39

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Observation ff9f4804-e8aa-42f8-b2c9-85effac53503 · outbound

This paper cites Fine-grained fish classification from small to large datasets with vision transformers,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fine-grained fish classification from small to large datasets with vision transformers,

Reference 40

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Observation 45612ae9-7203-4434-acbd-e5c6bdd6cc39 · outbound

This paper cites A dual-branch feature fusion neural network for fish image fine-grained recognition,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A dual-branch feature fusion neural network for fish image fine-grained recognition,

Reference 41

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Observation 1eb2fcbf-5524-467e-a156-a0ac15fe7ea1 · outbound

This paper cites Open-ocean fish reveal an omnidirectional solution to camouflage in polarized environments,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Open-ocean fish reveal an omnidirectional solution to camouflage in polarized environments,

Reference 42

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Observation cfada5e4-54de-48f6-8527-a8fef5dbbb1a · outbound

This paper cites Active detection for fish species recognition in underwater environments,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Active detection for fish species recognition in underwater environments,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.930055Z

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Observation 7df8d1d6-f759-4bc9-89f6-6abeb3fc881a · outbound

This paper cites Enhanced fish species identification using transfer learning on balanced datasets,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Enhanced fish species identification using transfer learning on balanced datasets,

Reference 44

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Observation 596907f8-0fe1-4642-85fd-e34dfa1541e7 · outbound

This paper cites Few-shot fine-grained fish species classification via sandwich attention covamnet,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Few-shot fine-grained fish species classification via sandwich attention covamnet,

Reference 45

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Observation e6722b4f-41a9-4815-bf8c-f429f0a117a4 · outbound

This paper cites Comparative analysis of neural architectures for underwater object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Comparative analysis of neural architectures for underwater object detection,

Reference 46

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Observation ca3a8ec8-d2a0-44b7-85bb-972ed9c34af8 · outbound

This paper cites Enhanced detection and classification of underwater objects using rov and computer vision,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Enhanced detection and classification of underwater objects using rov and computer vision,

Reference 47

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Observation bcb22180-a0ca-4de0-8428-1bf04378650c · outbound

This paper cites Underwater image quality evaluation: A comprehensive review,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Underwater image quality evaluation: A comprehensive review,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.885687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e939cfb-7bec-47f7-867a-03387a4a12f8 · outbound

This paper cites Weighted multi-error information entropy based you only look once network for underwater object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Weighted multi-error information entropy based you only look once network for underwater object detection,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.877192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9202f416-6378-489b-ad7d-a405e60941b4 · outbound

This paper cites A novel underwater marine dataset with diverse scenarios for robust object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A novel underwater marine dataset with diverse scenarios for robust object detection,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.868380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63f25472-ff25-49e1-8266-f19826772af1 · outbound

This paper cites A performance evaluation method for distant early warning sonar mobile area search,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A performance evaluation method for distant early warning sonar mobile area search,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.859478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f8d9298-969c-47af-ab8b-1e36d067ccd2 · outbound

This paper cites An underwater image quality assessment metric,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains An underwater image quality assessment metric,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.849331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9d4d8fb4-883d-4c15-ba4e-237c87e71bb6 · outbound

This paper cites Toward dimension- enriched underwater image quality assessment,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Toward dimension- enriched underwater image quality assessment,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.840199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7e3b84d-bab5-4be9-94e5-75f59efdeed2 · outbound

This paper cites Advancing underwater vision: A survey of deep learning models for underwater object recognition and tracking,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Advancing underwater vision: A survey of deep learning models for underwater object recognition and tracking,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.831193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1bc3a8b0-cb7c-4b08-9125-6db9e27c9347 · outbound

This paper cites A visual servoing scheme for autonomous aquaculture net pens inspection using rov,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A visual servoing scheme for autonomous aquaculture net pens inspection using rov,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.822765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9228e302-b881-4f7d-b396-93f2a9afd1ff · outbound

This paper cites Aquaculture defects recognition via multi-scale semantic segmentation,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Aquaculture defects recognition via multi-scale semantic segmentation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.814423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34ec6c3e-6205-4695-8e8b-06c0a4607d4e · outbound

This paper cites Aquayolo: Advanced yolo-based 12 ABUJABAL et al. : FISHDET-M: A UNIFIED LARGE-SCALE BENCHMARK FOR ROBUST FISH DETECTION fish detection for optimized aquaculture pond monitoring,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Aquayolo: Advanced yolo-based 12 ABUJABAL et al. : FISHDET-M: A UNIFIED LARGE-SCALE BENCHMARK FOR ROBUST FISH DETECTION fish detection for optimized aquaculture pond monitoring,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.805741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14e19a33-56bc-492c-91f9-f012f9df4277 · outbound

This paper cites Research on improved lightweight fish detection algorithm based on yolov8n,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Research on improved lightweight fish detection algorithm based on yolov8n,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.796657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41d695c4-8565-4e03-93c0-7780a947e8f7 · outbound

This paper cites Feedfirst: Intelligent monitoring system for indoor aquaculture tanks,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Feedfirst: Intelligent monitoring system for indoor aquaculture tanks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.788559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61ca7a7f-1552-4bd0-8183-92c301f14288 · outbound

This paper cites A school of robotic fish for mariculture monitoring in the sea coast,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains A school of robotic fish for mariculture monitoring in the sea coast,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.779431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d1e40160-accc-48dd-9074-c57bd460e5c2 · outbound

This paper cites Research on robotic fish swarm network technology based on underwater acoustic communication,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Research on robotic fish swarm network technology based on underwater acoustic communication,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.770588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c07b4317-ef32-415c-8e41-af3717bec5cb · outbound

This paper cites Vision-based autonomous navigation for unmanned surface vessel in extreme marine conditions,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Vision-based autonomous navigation for unmanned surface vessel in extreme marine conditions,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.762046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ce3845a-91b5-41f2-843a-f5bfcd9d80a5 · outbound

This paper cites Marine X: Design and implementation of unmanned surface vessel for vision guided navigation,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Marine X: Design and implementation of unmanned surface vessel for vision guided navigation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.754073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f538781-b60c-4d4d-be95-313b9856d9ac · outbound

This paper cites Enhancing aquaculture net pen inspection: A benchmark study on detection and semantic segmentation,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Enhancing aquaculture net pen inspection: A benchmark study on detection and semantic segmentation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.745783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d11f49e-e49b-426a-92d0-137b04166c84 · outbound

This paper cites Recognition and calculation of fish rafts in mariculture on the basis of artificial intelligence,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Recognition and calculation of fish rafts in mariculture on the basis of artificial intelligence,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.737358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e79bd7b7-68c6-49a0-8433-202906235913 · outbound

This paper cites Marine aquaculture sites have huge potential as data providers for climate change assessments,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Marine aquaculture sites have huge potential as data providers for climate change assessments,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.727904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7ddc6ff-d50e-4b07-9eb0-0813de050749 · outbound

This paper cites Beyond observation: Deep learning for animal behavior and ecological conservation,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Beyond observation: Deep learning for animal behavior and ecological conservation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.717677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52b43331-8e98-4e0a-9656-450dbc680830 · outbound

This paper cites Aasnet: A novel image instance segmentation framework for fine-grained fish recognition via linear correlation attention and dynamic adaptive focal loss,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Aasnet: A novel image instance segmentation framework for fine-grained fish recognition via linear correlation attention and dynamic adaptive focal loss,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.707942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d8af8d0-f88f-4da8-9718-b64bdf0fe1ee · outbound

This paper cites High-accuracy real-time fish detection based on self-build dataset and rird-yolov3,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains High-accuracy real-time fish detection based on self-build dataset and rird-yolov3,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.697597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49fb4472-371d-49db-a04c-bfe2b233638a · outbound

This paper cites Benchmarking vision-based object tracking for usvs in complex maritime environments,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Benchmarking vision-based object tracking for usvs in complex maritime environments,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.688601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00e3440c-4c6f-4f73-bfa1-2fb437dc219b · outbound

This paper cites Usod10k: A new benchmark dataset for underwater salient object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Usod10k: A new benchmark dataset for underwater salient object detection,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.680125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a2caef74-c71a-4d36-93da-2145c3e4b5be · outbound

This paper cites Coco - common objects in context,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Coco - common objects in context,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.671482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7b043f6-1431-49e5-9491-39325b678e77 · outbound

This paper cites Fishnet: A large-scale dataset and benchmark for fish recognition, detection, and functional trait prediction,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fishnet: A large-scale dataset and benchmark for fish recognition, detection, and functional trait prediction,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.663209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 272a5a60-4c1b-4df8-ace9-9c4721a4eb82 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains YOLOv10: Real-Time End-to-End Object Detection

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:12.330297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6db983a5-7744-4703-9b01-d0cccd3833da · outbound

This paper cites Ultralytics yolo11,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Ultralytics yolo11,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:12.333122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12d8b71-b984-4d6e-a347-20d5b0eb6b86 · outbound

This paper cites Yolov12: Attention-centric real-time object detectors,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Yolov12: Attention-centric real-time object detectors,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.648620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9326659c-832b-442a-b321-9fa10c0e1817 · outbound

This paper cites Super-gradients,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Super-gradients,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.639482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8f73a3b-cdc7-42ab-aabb-63af25689e53 · outbound

This paper cites Sparse r-cnn: An end-to-end framework for object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Sparse r-cnn: An end-to-end framework for object detection,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.630230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63572f91-9835-4d99-85ce-c240df5fadf5 · outbound

This paper cites Detrs beat yolos on real-time object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Detrs beat yolos on real-time object detection,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.621206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9903d843-039b-4906-8716-e1ff8a0871bc · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:12.345587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c944c48-be55-4821-a389-3a2fbdea5c56 · outbound

This paper cites Focal loss for dense object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Focal loss for dense object detection,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.612840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 894a39ae-3cb2-47e3-9bbd-7203ad232fca · outbound

This paper cites Ssd: Single shot multibox detector,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Ssd: Single shot multibox detector,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.604712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd0548ab-f3b8-4a19-afad-98e0fa4936d6 · outbound

This paper cites Paszke, S.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Paszke, S

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.596819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed538c37-a093-462a-8f53-1e77e4580c07 · outbound

This paper cites Ultralytics YOLO,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Ultralytics YOLO,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:12.355665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad6d7809-1039-4b73-ac97-39433303ac36 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T14:42:12.358407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3001cb5d-1b85-47d0-b2b4-ad37f1faaa04 · outbound

This paper cites pycocotools: Coco api for python,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains pycocotools: Coco api for python,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.583736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b621e9bc-9b96-4f4c-99de-a42f741fb895 · outbound

This paper cites Fish4knowledge dataset dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish4knowledge dataset dataset,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.576072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e55f122-ed0b-4d4c-9175-67d0fa808f43 · outbound

This paper cites Fish video dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish video dataset,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.568215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61316a34-3d1d-4972-9e7b-463dc2543256 · outbound

This paper cites Available: https://universe.roboflow.com/aarjoo-murme/ fish-video-ls42k.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Available: https://universe.roboflow.com/aarjoo-murme/ fish-video-ls42k

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.560318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af5f4d77-81ac-4a75-8792-c98dc54dc78a · outbound

This paper cites Fish-video dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish-video dataset,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.552015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 29d4e2fc-d2c8-4b3f-b0fa-d42b41667923 · outbound

This paper cites Automatic detection and classification of coastal mediterranean fish from underwater images: Good practices for robust training,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Automatic detection and classification of coastal mediterranean fish from underwater images: Good practices for robust training,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.544209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab1f848d-08ef-4a5c-a2d2-a19e72a7b301 · outbound

This paper cites detect aqurium dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains detect aqurium dataset,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.536302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 16bc28a4-f6c1-494e-b408-1580698bdf1a · outbound

This paper cites Aquatic animal species (aas),.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Aquatic animal species (aas),

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.527949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c045ae6-3c02-421c-a9aa-d89eb7451449 · outbound

This paper cites Fish clean dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fish clean dataset,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.519595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4d58e0b-33dc-4041-80df-a45a315bef60 · outbound

This paper cites Fcos: Fully convolutional one- stage object detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Fcos: Fully convolutional one- stage object detection,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.510951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 018310b2-767d-4c9c-968c-0e904ef64dfb · outbound

This paper cites fish_dataset_florence_1 dataset,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains fish_dataset_florence_1 dataset,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.502985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 45856f24-2263-4263-a6d9-074225b8adcd · outbound

This paper cites Eba-ai: Ethics-guided bias-aware ai for efficient underwater image enhancement and coral reef monitoring,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Eba-ai: Ethics-guided bias-aware ai for efficient underwater image enhancement and coral reef monitoring,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.495099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c390bc01-59de-4968-8c43-c0e47128e8f5 · outbound

This paper cites Deepfins: Capturing dynamics in underwater videos for fish detection,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Deepfins: Capturing dynamics in underwater videos for fish detection,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.478432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed98e118-a4c4-4d89-9ba4-7d69634dd76c · outbound

This paper cites Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system,.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:42:12.486629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7436821d-83e4-45e3-bacd-45efb36a16d6 · outbound

This paper cites EBA-AI: Ethics-Guided Bias-Aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring.

FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains EBA-AI: Ethics-Guided Bias-Aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:42:12.437516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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