Visual Textualization for Image Prompted Object Detection | Paper Citation Record

Observation cf10745e-c8e4-4e84-81dc-bf97e68189d9 · outbound

This paper cites Lung image database consor- tium: developing a resource for the medical imaging research community.

Visual Textualization for Image Prompted Object Detection Lung image database consor- tium: developing a resource for the medical imaging research community

Reference 1

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Observation a8a31ae4-53dd-4930-936c-bc8d7851bbba · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Visual Textualization for Image Prompted Object Detection Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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Observation 494e3f34-7810-4fca-94fa-730bd867c9cc · outbound

This paper cites Fs-detr: Few-shot detection transformer with prompting and without re-training.

Visual Textualization for Image Prompted Object Detection Fs-detr: Few-shot detection transformer with prompting and without re-training

Reference 3

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Observation c10b563c-4060-489f-b9b8-d1a0a9c4ea95 · outbound

This paper cites Apollo: Unified adapter and prompt learning for vision language models.

Visual Textualization for Image Prompted Object Detection Apollo: Unified adapter and prompt learning for vision language models

Reference 4

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Observation f4129d0f-d7d2-41ab-ab60-dc6c1fc394c2 · outbound

This paper cites Coarse-to-fine vision-language pre-training with fusion in the backbone.NeurIPS, 35:32942–32956, 2022.

Visual Textualization for Image Prompted Object Detection Coarse-to-fine vision-language pre-training with fusion in the backbone.NeurIPS, 35:32942–32956, 2022

Reference 5

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Observation b47ad70b-726a-4417-8822-148ba1e39462 · outbound

This paper cites s- adaptive decoupled prototype for few-shot object detection.

Visual Textualization for Image Prompted Object Detection s- adaptive decoupled prototype for few-shot object detection

Reference 6

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Observation 02332cf4-9657-440c-b52a-6f5b0fca1064 · outbound

This paper cites Learning to prompt for open-vocabulary object detection with vision-language model.

Visual Textualization for Image Prompted Object Detection Learning to prompt for open-vocabulary object detection with vision-language model

Reference 7

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Observation fc7a5f35-5371-4a1f-8b5f-125bf38d7737 · outbound

This paper cites The Turking Test: Can Language Models Understand Instructions?.

Visual Textualization for Image Prompted Object Detection The Turking Test: Can Language Models Understand Instructions?

Reference 8

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Observation 5d4286b5-c630-43ea-91e4-2648e1275036 · outbound

This paper cites The pascal visual object classes (voc) challenge.

Visual Textualization for Image Prompted Object Detection The pascal visual object classes (voc) challenge

Reference 9

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Observation da991085-3e35-48f9-92af-c7c2e6ba7bef · outbound

This paper cites Few- shot object detection with attention-rpn and multi-relation detector.

Visual Textualization for Image Prompted Object Detection Few- shot object detection with attention-rpn and multi-relation detector

Reference 10

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Observation d5dc14cd-cb45-4eec-8789-e2f26e00a6e2 · outbound

This paper cites Nuclei grading of clear cell renal cell carcinoma in histopatho- logical image by composite high-resolution network.

Visual Textualization for Image Prompted Object Detection Nuclei grading of clear cell renal cell carcinoma in histopatho- logical image by composite high-resolution network

Reference 11

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Observation 0c33a867-fc55-40ee-9428-acedea4ca5b9 · outbound

This paper cites Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.

Visual Textualization for Image Prompted Object Detection Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Reference 12

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Observation 482ae711-fa47-4911-ab40-5dd7c88d34a5 · outbound

This paper cites A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

Visual Textualization for Image Prompted Object Detection A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Reference 13

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Observation 966a79fa-1baa-48e3-bde5-e59e1b4c9e28 · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

Visual Textualization for Image Prompted Object Detection Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 14

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Observation d248f254-2407-4685-a5a7-ce97d22d99b7 · outbound

This paper cites Dp-ddcl: A discriminative prototype with dual decou- pled contrast learning method for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Dp-ddcl: A discriminative prototype with dual decou- pled contrast learning method for few-shot object detection

Reference 15

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Observation 2c1e0f48-be20-4d20-b198-254041fd36b0 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Visual Textualization for Image Prompted Object Detection Lvis: A dataset for large vocabulary instance segmentation

Reference 16

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Observation 75988dd5-13ac-45a7-8487-7f4ad5410f54 · outbound

This paper cites Few-shot object detection with foundation models.

Visual Textualization for Image Prompted Object Detection Few-shot object detection with foundation models

Reference 17

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Observation e848ea3a-8a08-4488-8694-157cb8550f99 · outbound

This paper cites Query adaptive few-shot object detec- tion with heterogeneous graph convolutional networks.

Visual Textualization for Image Prompted Object Detection Query adaptive few-shot object detec- tion with heterogeneous graph convolutional networks

Reference 18

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Observation 0eb56578-ed90-408c-b085-e6eee5a18312 · outbound

This paper cites Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting.

Visual Textualization for Image Prompted Object Detection Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting

Reference 19

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Observation 0b083cbe-5052-4226-b5da-d752dfbf10b3 · outbound

This paper cites Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment.

Visual Textualization for Image Prompted Object Detection Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment

Reference 20

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Observation 68627033-c1b9-4d89-9ce7-a487d2873efb · outbound

This paper cites Few-shot object detection with fully cross- transformer.

Visual Textualization for Image Prompted Object Detection Few-shot object detection with fully cross- transformer

Reference 22

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Observation a3f2ac37-d0a0-4ebb-8c91-099a6c3c8f29 · outbound

This paper cites Few-shot object detection via variational feature aggregation.

Visual Textualization for Image Prompted Object Detection Few-shot object detection via variational feature aggregation

Reference 23

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Observation 02f4ff15-dfd7-4c74-ba03-bcff5b1ea601 · outbound

This paper cites Visual prompt tuning.

Visual Textualization for Image Prompted Object Detection Visual prompt tuning

Reference 24

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Observation d8e8e50f-2e4b-4c46-af8e-5097595a48d5 · outbound

This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding.

Visual Textualization for Image Prompted Object Detection Bert: Pre-training of deep bidirectional transform- ers for language understanding

Reference 25

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Observation 8fb616bd-3570-45dc-8811-9f2bf38cb355 · outbound

This paper cites Maple: Multi- modal prompt learning.

Visual Textualization for Image Prompted Object Detection Maple: Multi- modal prompt learning

Reference 26

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Observation 5b792927-2702-4896-8eec-86b34a410cca · outbound

This paper cites A dataset and a technique for generalized nuclear segmentation for computa- tional pathology.

Visual Textualization for Image Prompted Object Detection A dataset and a technique for generalized nuclear segmentation for computa- tional pathology

Reference 27

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Observation d737176b-afa7-4a34-9af7-dd51e16057d7 · outbound

This paper cites F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models.

Visual Textualization for Image Prompted Object Detection F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models

Reference 28

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Observation 7be32b2c-81c1-47a8-b5ac-15dbca57af3b · outbound

This paper cites Elevater: A benchmark and toolkit for evaluating language-augmented visual models.

Visual Textualization for Image Prompted Object Detection Elevater: A benchmark and toolkit for evaluating language-augmented visual models

Reference 29

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Observation 002258a5-4867-4e66-936b-65487eb0445c · outbound

This paper cites Disentangle and remerge: interventional knowledge distillation for few-shot object detection from a conditional causal perspective.

Visual Textualization for Image Prompted Object Detection Disentangle and remerge: interventional knowledge distillation for few-shot object detection from a conditional causal perspective

Reference 30

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Observation e4c5e956-6447-4a4a-9cc8-20fcf753f8c8 · outbound

This paper cites Grounded language- image pre-training.

Visual Textualization for Image Prompted Object Detection Grounded language- image pre-training

Reference 31

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Observation 9edc8e46-7db7-4c90-9e95-dd493c8605c8 · outbound

This paper cites Microsoft coco: Common objects in context.

Visual Textualization for Image Prompted Object Detection Microsoft coco: Common objects in context

Reference 32

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Observation b2801c4e-e958-4df3-9fca-ba4b3f05eab5 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Visual Textualization for Image Prompted Object Detection Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 33

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Observation 01277d58-5a56-4099-b94e-764c9deffc45 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Visual Textualization for Image Prompted Object Detection Swin transformer: Hierarchical vision transformer using shifted windows

Reference 34

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Observation 739b0451-22e2-4f15-a6aa-48fd1e79a463 · outbound

This paper cites Breaking immutable: Information-coupled prototype elaboration for few-shot ob- ject detection.

Visual Textualization for Image Prompted Object Detection Breaking immutable: Information-coupled prototype elaboration for few-shot ob- ject detection

Reference 35

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Observation c39a64d9-a64f-48d1-99da-e3a2469eb890 · outbound

This paper cites Image segmentation us- ing text and image prompts.

Visual Textualization for Image Prompted Object Detection Image segmentation us- ing text and image prompts

Reference 36

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Observation f0c2cc35-7f82-4ae8-af0e-e8d608ccaf31 · outbound

This paper cites Digeo: Discriminative geometry-aware learning for generalized few-shot object de- tection.

Visual Textualization for Image Prompted Object Detection Digeo: Discriminative geometry-aware learning for generalized few-shot object de- tection

Reference 37

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Observation 5f89670f-611c-4991-91f4-bc0e8c45a54a · outbound

This paper cites Simple open-vocabulary object detection.

Visual Textualization for Image Prompted Object Detection Simple open-vocabulary object detection

Reference 38

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Observation e0af7dcd-d85c-45f1-b10d-0fdc4ff3770f · outbound

This paper cites Scal- ing open-vocabulary object detection.

Visual Textualization for Image Prompted Object Detection Scal- ing open-vocabulary object detection

Reference 39

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Observation a907e535-54c4-4178-8f1b-7ead7169f35e · outbound

This paper cites Defrcn: Decoupled faster r-cnn for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Defrcn: Decoupled faster r-cnn for few-shot object detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.659393Z

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Observation 60171d71-81d6-4b39-a81b-16cc0df5ae2a · outbound

This paper cites Language models are unsuper- vised multitask learners.

Visual Textualization for Image Prompted Object Detection Language models are unsuper- vised multitask learners

Reference 41

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Observation 3de2fc5b-5b78-44e4-a450-e5c16d79a758 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Visual Textualization for Image Prompted Object Detection Learning transferable visual models from natural language supervi- sion

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.611623Z

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Observation 6157ef5e-b882-49eb-a9d0-f5d86407f505 · outbound

This paper cites Adaptive multi-task learning for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Adaptive multi-task learning for few-shot object detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.588564Z

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Observation 9042d0fc-ffe6-4c22-afa7-39b41f8cacbf · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

Visual Textualization for Image Prompted Object Detection Objects365: A large-scale, high-quality dataset for object detection

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T21:37:06.980275Z

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Observation 9b62f9e8-eb53-4952-bd87-25f58b822722 · outbound

This paper cites Few- shot adaptive faster r-cnn.

Visual Textualization for Image Prompted Object Detection Few- shot adaptive faster r-cnn

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.551444Z

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Observation 86f89577-7cb6-453c-810b-285054f4d9b3 · outbound

This paper cites Frustratingly Simple Few-Shot Object Detection.

Visual Textualization for Image Prompted Object Detection Frustratingly Simple Few-Shot Object Detection

Reference 47

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Observation 3e24ebcb-cddf-4047-90bd-acd227207b64 · outbound

This paper cites Snida: Unlocking few-shot object detection with non- linear semantic decoupling augmentation.

Visual Textualization for Image Prompted Object Detection Snida: Unlocking few-shot object detection with non- linear semantic decoupling augmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.530773Z

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Observation dfa01c67-5c5f-4333-b46e-b41aa717ed20 · outbound

This paper cites Multi- scale positive sample refinement for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Multi- scale positive sample refinement for few-shot object detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.516661Z

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Observation c7e19578-69fe-445c-abf4-20e2d9dc6474 · outbound

This paper cites Multi-faceted distillation of base-novel commonality for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Multi-faceted distillation of base-novel commonality for few-shot object detection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.501311Z

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Observation 1767531d-f860-461e-b608-09d2122bdc64 · outbound

This paper cites Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching.

Visual Textualization for Image Prompted Object Detection Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.485161Z

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Observation 2feb1fc3-d790-4a12-aaf4-a92cecd2fceb · outbound

This paper cites Generating fea- tures with increased crop-related diversity for few-shot ob- ject detection.

Visual Textualization for Image Prompted Object Detection Generating fea- tures with increased crop-related diversity for few-shot ob- ject detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.469861Z

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Observation a057a633-5693-40a9-82c8-13bf30038f4f · outbound

This paper cites Multi-modal queried object detection in the wild.

Visual Textualization for Image Prompted Object Detection Multi-modal queried object detection in the wild

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.454659Z

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Observation 0c0f5449-7811-4090-ba43-158692c4fef9 · outbound

This paper cites DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations.

Visual Textualization for Image Prompted Object Detection DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T21:37:07.600258Z

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Observation 73132bc0-6d32-4dd3-b3f0-56030f5c197d · outbound

This paper cites Meta r-cnn: Towards general solver for instance-level low-shot learning.

Visual Textualization for Image Prompted Object Detection Meta r-cnn: Towards general solver for instance-level low-shot learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.438524Z

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Observation e0e62d53-abc9-42a2-90d8-3c4e5d8d3c68 · outbound

This paper cites Meta-detr: Image-level few-shot detection with inter-class correlation exploitation.

Visual Textualization for Image Prompted Object Detection Meta-detr: Image-level few-shot detection with inter-class correlation exploitation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.421162Z

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Observation e6ad7f42-c487-4313-b2c1-295f968ec37e · outbound

This paper cites Detect Everything with Few Examples.

Visual Textualization for Image Prompted Object Detection Detect Everything with Few Examples

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:37:08.017364Z

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Observation 42ad3728-b973-43cf-af2f-2cb443f79a27 · outbound

This paper cites Vlm-guided explicit-implicit complementary novel class semantic learning for few-shot object detection.

Visual Textualization for Image Prompted Object Detection Vlm-guided explicit-implicit complementary novel class semantic learning for few-shot object detection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.399983Z

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Observation a5d45fcf-aa5b-47a6-8f2a-2ad0baa3c275 · outbound

This paper cites Scene-adaptive and region-aware multi-modal prompt for open vocabulary object detection.

Visual Textualization for Image Prompted Object Detection Scene-adaptive and region-aware multi-modal prompt for open vocabulary object detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.377603Z

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Observation 08905e76-b956-41e3-930d-b50160e36131 · outbound

This paper cites Regionclip: Region-based language-image pretraining.

Visual Textualization for Image Prompted Object Detection Regionclip: Region-based language-image pretraining

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.355023Z

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Observation 5cd787c3-1d4a-4e53-82d9-7dfe83fd4d3c · outbound

This paper cites Conditional prompt learning for vision-language models.

Visual Textualization for Image Prompted Object Detection Conditional prompt learning for vision-language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.336727Z

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Observation f5b47bc4-f299-46af-a8da-1e0ec522b2e6 · outbound

This paper cites Learning to prompt for vision-language models.

Visual Textualization for Image Prompted Object Detection Learning to prompt for vision-language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.322179Z

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Observation 0e5d6e64-5eae-4609-bc1c-f0d391d14a4d · outbound

This paper cites fully connected (fc) + ReLU.

Visual Textualization for Image Prompted Object Detection fully connected (fc) + ReLU

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.304934Z

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Observation 08b86e82-9e0c-4100-8457-b110e286e90a · outbound

This paper cites 4.3 of the main text, we provide detailed transfer results on the ODinW13 subsets [ 31] in Tab.

Visual Textualization for Image Prompted Object Detection 4.3 of the main text, we provide detailed transfer results on the ODinW13 subsets [ 31] in Tab

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.283734Z

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Observation d24f22c8-c160-4757-98b8-9f389092278b · outbound

This paper cites an unresolved cited work.

Visual Textualization for Image Prompted Object Detection Unresolved cited work

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:37:09.267762Z

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Observation 089e77eb-2e4a-4a55-8010-4c8199a976db · outbound

This paper cites 8, we report the computational overhead for process- ing one image using GLIP-L on RTX3090 with one support image, comparing it to MQ-Det and GLIP-FF.

Visual Textualization for Image Prompted Object Detection 8, we report the computational overhead for process- ing one image using GLIP-L on RTX3090 with one support image, comparing it to MQ-Det and GLIP-FF

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.247972Z

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Observation 254e521a-4c25-470c-9c16-6511e3952a71 · outbound

This paper cites BG blur" technique performs best. It high- lights the target object while preserving some background, unlike.

Visual Textualization for Image Prompted Object Detection BG blur" technique performs best. It high- lights the target object while preserving some background, unlike

Reference 67

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:37:09.227510Z

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Observation a9ddc82e-4ae4-4035-b9c4-0517e3f24bc1 · outbound

This paper cites Base- line.

Visual Textualization for Image Prompted Object Detection Base- line

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:09.203949Z

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