Paper Citation Record · LEDGER

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis

As of 10 August 2026, this Paper Citation Record lists 100 of 146 outbound references and 3 inbound Pith citation observations for arXiv:2502.01785.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01785 v1

Coverage vector

measured 100 of 146 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:32:36.915707Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T18:28:55.336300Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T02:06:58.769990Z

Reference resolution

100 of 146 outbound references displayed

verified exact1
verified fuzzy17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7623565-8729-4681-a66c-db3503da4085 · outbound

This paper cites an unresolved cited work.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Unresolved cited work

Reference 1

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Observation c87b2a3c-41eb-4dfb-ac1e-e75e00c57c5a · outbound

This paper cites an unresolved cited work.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Unresolved cited work

Reference 2

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Observation a3efb222-2be9-4a49-b967-a47dac57022c · outbound

This paper cites Aquarium dataset,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Aquarium dataset,

Reference 3

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Observation 8800c6c8-56ff-4aa8-a823-22f9e0836bda · outbound

This paper cites Hk reef fish Images.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Hk reef fish Images

Reference 6

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Observation 94fa62e8-fe2e-4dab-9fde-1417a961572f · outbound

This paper cites Marine Animal Images,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Marine Animal Images,

Reference 7

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Observation 23e42243-6f4a-4a70-a598-a95a63d4caf3 · outbound

This paper cites Sea Animals Image Dataset,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Sea Animals Image Dataset,

Reference 8

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Observation c952f362-ade7-47d3-a003-d0d858e0164c · outbound

This paper cites Shutterstock Image.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Shutterstock Image

Reference 9

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Observation 47a556ea-647a-4411-9271-1fa0bf902a6a · outbound

This paper cites Underwater trash detection dataset,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Underwater trash detection dataset,

Reference 10

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Observation 180749ba-659e-41cc-90ee-97d79edf866e · outbound

This paper cites an unresolved cited work.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Unresolved cited work

Reference 11

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Observation b8ccc24f-67eb-4e13-a3da-e4e649ade122 · outbound

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

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Ozfish dataset - machine learning dataset for baited remote underwater video stations,

Reference 12

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This paper cites URPC dataset,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis URPC dataset,

Reference 13

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Observation 51654ac3-bd56-4c84-97a1-2eb7f19ca817 · outbound

This paper cites Oceanic life dataset,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Oceanic life dataset,

Reference 14

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Observation 1a73677d-6521-408f-9fa6-6a1e7973d005 · outbound

This paper cites A novel coral reef classification method combining radiative transfer model with deep learning,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A novel coral reef classification method combining radiative transfer model with deep learning,

Reference 15

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Observation cec7effd-60be-4200-b6dc-9b7c8dbd6b91 · outbound

This paper cites Utb180: A high-quality benchmark for underwater tracking,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Utb180: A high-quality benchmark for underwater tracking,

Reference 16

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Observation 9300b776-b3d7-44e9-b2dd-a602e9963a90 · outbound

This paper cites Computer vision in aquaculture: a case study of juvenile fish counting,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Computer vision in aquaculture: a case study of juvenile fish counting,

Reference 18

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This paper cites BEiT: BERT Pre-Training of Image Transformers.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis BEiT: BERT Pre-Training of Image Transformers

Reference 19

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Observation bfcd744d-174f-40b8-b0a2-0931af034881 · outbound

This paper cites Deep learning for detection and counting of nephrops norvegicus from underwater videos,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Deep learning for detection and counting of nephrops norvegicus from underwater videos,

Reference 20

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Observation 59fd532a-a257-45db-bfb0-2094e82de18a · outbound

This paper cites Semi-supervised visual tracking of marine animals using autonomous underwater vehicles,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Semi-supervised visual tracking of marine animals using autonomous underwater vehicles,

Reference 21

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Observation 0b00b0e6-bfed-4e58-8d84-6149038bfb76 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Emerging properties in self-supervised vision transformers,

Reference 22

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Observation 348a7bb7-4057-4279-b001-1d827671b5c3 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 23

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Observation 0991f7f5-7d7d-441e-a956-30e4d119329c · outbound

This paper cites You only look one-level feature,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis You only look one-level feature,

Reference 24

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Observation b9717a93-9b5d-4982-aa5e-e08f42101cf9 · outbound

This paper cites A simple frame- work for contrastive learning of visual representations,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A simple frame- work for contrastive learning of visual representations,

Reference 25

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Masked-attention mask transformer for universal image segmenta- tion,

Reference 26

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This paper cites Flow: A dataset and benchmark for floating waste detection in inland waters,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Flow: A dataset and benchmark for floating waste detection in inland waters,

Reference 27

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Observation 762cf6b8-571b-4312-bb0d-d0ba5db4837e · outbound

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Marine fouling images,

Reference 28

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Observation 1a0e60f8-7863-401d-88f0-2ca8392e82ed · outbound

This paper cites Underwater image dehazing via unpaired image-to-image translation,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Underwater image dehazing via unpaired image-to-image translation,

Reference 29

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Pdffigures 2.0: Mining figures from research papers,

Reference 30

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Class-balanced loss based on effective number of samples,

Reference 31

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Ocean acidification’s impact on marine ecosystems,

Reference 32

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Imagenet: A large-scale hierarchical image database,

Reference 33

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Aquagan: Restoration of underwater images,

Reference 34

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Corals classification,

Reference 35

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The wonders of coral reefs,

Reference 36

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Climate change impacts on marine ecosystems,

Reference 37

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 38

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Rebuilding marine life,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The decline of australia’s great barrier reef,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Instances as queries,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Tood: Task- aligned one-stage object detection,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Robotic detection of marine litter using deep visual detection models,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A Cookbook of Self-Supervised Learning

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A survey on underwater computer vision,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The mpa guide: A framework to achieve global goals for the ocean,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Coral species classification dataset,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis MarineDet: Towards Open-Marine Object Detection

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A global map of human impact on marine ecosystems,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Umotma: Underwa- ter multiple object tracking with memory aggregation,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Deep residual learning for image recognition,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Usod10k: A new benchmark dataset for underwater salient object detection,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Underwater image restoration based on convolutional neural network,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Contrastive semi- supervised learning for underwater image restoration via reliable bank,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Semantic Segmentation of Underwater Imagery: Dataset and Benchmark,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Fast underwater image enhance- ment for improved visual perception,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Svam: Saliency-guided visual attention modeling by autonomous underwater robots,

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Fish detection and species classification in underwater environments using deep learning with temporal information,

Reference 59

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The effects of fishing on marine ecosystems,

Reference 60

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis FathomNet: A global image database for enabling artificial intelligence in the ocean,

Reference 61

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries

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This paper cites Mask transfiner for high-quality instance segmentation,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Mask transfiner for high-quality instance segmentation,

Reference 63

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

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Fishnet: A large- scale dataset and benchmark for fish recognition, detection, and func- tional trait prediction,

Reference 64

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This paper cites Maple: Multi-modal prompt learning,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Maple: Multi-modal prompt learning,

Reference 65

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This paper cites Segment anything,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Segment anything,

Reference 66

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This paper cites Pointrend: Image segmentation as rendering,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Pointrend: Image segmentation as rendering,

Reference 67

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This paper cites Detection and tracking of underwater fish using the fair multi-object tracking model: A comparative analysis 28 of yolov5s and dla-34 backbone models,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Detection and tracking of underwater fish using the fair multi-object tracking model: A comparative analysis 28 of yolov5s and dla-34 backbone models,

Reference 68

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This paper cites Watergan: Unsupervised generative network to enable real-time color correction of monocular underwater images,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Watergan: Unsupervised generative network to enable real-time color correction of monocular underwater images,

Reference 69

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This paper cites Deep learning for visual recognition and detection of aquatic animals: A review,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Deep learning for visual recognition and detection of aquatic animals: A review,

Reference 70

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This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 71

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This paper cites Citetracker: Correlating image and text for visual tracking,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Citetracker: Correlating image and text for visual tracking,

Reference 72

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This paper cites Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Reference 73

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This paper cites Underwater object tracker: Uostrack for marine organism grasping of underwater vehicles,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Underwater object tracker: Uostrack for marine organism grasping of underwater vehicles,

Reference 74

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

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Watermask: Instance segmentation for underwater imagery,

Reference 75

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This paper cites An end-to-end transformer model for crowd localization,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis An end-to-end transformer model for crowd localization,

Reference 76

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This paper cites Focal loss for dense object detection,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Focal loss for dense object detection,

Reference 77

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This paper cites Microsoft coco: Common objects in context,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Microsoft coco: Common objects in context,

Reference 78

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This paper cites A new dataset, poisson gan and aquanet for underwater object grabbing,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A new dataset, poisson gan and aquanet for underwater object grabbing,

Reference 79

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This paper cites Cffi-vit: Enhanced vision transformer for the accurate classification of fish feeding intensity in aquaculture,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Cffi-vit: Enhanced vision transformer for the accurate classification of fish feeding intensity in aquaculture,

Reference 80

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This paper cites Dp-fishnet: Dual-path pyramid vision transformer-based underwater fish detection network,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Dp-fishnet: Dual-path pyramid vision transformer-based underwater fish detection network,

Reference 81

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This paper cites A convnet for the 2020s,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A convnet for the 2020s,

Reference 82

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This paper cites Decoupled Weight Decay Regularization.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Decoupled Weight Decay Regularization

Reference 83

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This paper cites Multi-scale adversarial network for underwater image restoration,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Multi-scale adversarial network for underwater image restoration,

Reference 84

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This paper cites Enhancing clip with gpt-4: Harnessing visual de- scriptions as prompts,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Enhancing clip with gpt-4: Harnessing visual de- scriptions as prompts,

Reference 85

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This paper cites Australasian freshwater fish faunas: diversity, interrela- tionships, radiations and conservation,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Australasian freshwater fish faunas: diversity, interrela- tionships, radiations and conservation,

Reference 86

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This paper cites The rich biodiversity of ocean ecosystems,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis The rich biodiversity of ocean ecosystems,

Reference 87

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This paper cites Coral identification and counting with an autonomous underwater vehicle,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Coral identification and counting with an autonomous underwater vehicle,

Reference 88

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AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Innovative approaches to coral conservation,

Reference 89

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This paper cites Embodiedgpt: Vision-language pre-training via embodied chain of thought,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Embodiedgpt: Vision-language pre-training via embodied chain of thought,

Reference 90

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This paper cites I2mvformer: Large language model generated multi-view document supervision for zero-shot image clas- sification,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis I2mvformer: Large language model generated multi-view document supervision for zero-shot image clas- sification,

Reference 91

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This paper cites Dinov2: Learning robust visual features without supervision,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Dinov2: Learning robust visual features without supervision,

Reference 92

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This paper cites Comprehensive underwater object tracking benchmark dataset and underwater image enhancement with gan,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Comprehensive underwater object tracking benchmark dataset and underwater image enhancement with gan,

Reference 93

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This paper cites Detection of marine animals in a new underwater dataset with varying visibility,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Detection of marine animals in a new underwater dataset with varying visibility,

Reference 94

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This paper cites U-shape transformer for underwater image enhancement,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis U-shape transformer for underwater image enhancement,

Reference 95

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This paper cites Detecting and recognizing marine animals using advanced deep learning models,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Detecting and recognizing marine animals using advanced deep learning models,

Reference 96

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This paper cites Learning transferable visual models from natural language supervision,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Learning transferable visual models from natural language supervision,

Reference 97

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This paper cites Language models are unsupervised multitask learners,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Language models are unsupervised multitask learners,

Reference 98

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

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 99

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

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis,

Reference 100

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This paper cites Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach

Reference 101

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This paper cites Computer vision and deep learning for fish classification in underwater habitats: A survey,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Computer vision and deep learning for fish classification in underwater habitats: A survey,

Reference 102

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This paper cites Nlx-gpt: A model for natural language explanations in vision and vision-language tasks,.

AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis Nlx-gpt: A model for natural language explanations in vision and vision-language tasks,

Reference 103

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Pith citing papers

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AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis

Reference 31

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Reference 17

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Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG cites this paper.

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