Repurposing CLIP to Localize at Pixel Level | Paper Citation Record

Observation 14718327-0b98-423e-a3b2-c086f7aa3291 · outbound

This paper cites Segment anything,.

Repurposing CLIP to Localize at Pixel Level Segment anything,

Reference 1

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Observation 9b5793b1-cb30-45da-95d6-8c9754649999 · outbound

This paper cites Exploiting efficientsam and temporal coher- ence for audio-visual segmentation,.

Repurposing CLIP to Localize at Pixel Level Exploiting efficientsam and temporal coher- ence for audio-visual segmentation,

Reference 2

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Observation 2efe077a-0bb5-44e0-a15c-68dc9aac671c · outbound

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

Repurposing CLIP to Localize at Pixel Level Learning transferable visual models from natural language supervision,

Reference 3

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Observation b033ea74-40d8-4948-9f2a-1d4bbb3ea9cb · outbound

This paper cites Visual and textual prior guided mask assemble for few-shot segmentation and beyond,.

Repurposing CLIP to Localize at Pixel Level Visual and textual prior guided mask assemble for few-shot segmentation and beyond,

Reference 4

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Observation 5f5e2f1c-e0a4-4efe-8f2c-bdd43a75dd3d · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

Repurposing CLIP to Localize at Pixel Level Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 5

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Observation 8d9a1320-a603-4264-a3f1-944b4855c0a1 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Repurposing CLIP to Localize at Pixel Level Personalize Segment Anything Model with One Shot

Reference 6

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Observation 28b69c20-24cf-4658-87be-b2466cfabf23 · outbound

This paper cites Vrp-sam: Sam with visual reference prompt,.

Repurposing CLIP to Localize at Pixel Level Vrp-sam: Sam with visual reference prompt,

Reference 7

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Observation eae2f266-84f5-40e4-93cf-81c5d1861e02 · outbound

This paper cites Denseclip: Language-guided dense prediction with context-aware prompting,.

Repurposing CLIP to Localize at Pixel Level Denseclip: Language-guided dense prediction with context-aware prompting,

Reference 8

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Observation 57744544-306e-441b-bfc5-6e7f784ccb1d · outbound

This paper cites SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network.

Repurposing CLIP to Localize at Pixel Level SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network

Reference 9

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Observation 42344664-6dd5-4e2c-ae7f-df960cdcc86e · outbound

This paper cites Spklip: Aligning spike video streams with natural language,.

Repurposing CLIP to Localize at Pixel Level Spklip: Aligning spike video streams with natural language,

Reference 10

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Observation 55f1498e-0be9-45bc-bd69-053cf0f031e1 · outbound

This paper cites Self-supervised high-order information bottleneck learning of spiking neural network for robust event-based optical flow estimation,.

Repurposing CLIP to Localize at Pixel Level Self-supervised high-order information bottleneck learning of spiking neural network for robust event-based optical flow estimation,

Reference 11

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Observation 86682367-75ab-4b96-8c2f-8b78fe57dd3b · outbound

This paper cites Language-driven Semantic Segmentation.

Repurposing CLIP to Localize at Pixel Level Language-driven Semantic Segmentation

Reference 12

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Observation 8f5065c8-df6d-4a8f-8841-f5de3d60bb08 · outbound

This paper cites Delving into shape-aware zero-shot semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Delving into shape-aware zero-shot semantic segmentation,

Reference 13

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Observation 31e9f569-da6c-4c6d-bd49-637d346b8c8b · outbound

This paper cites Decoupling zero-shot semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Decoupling zero-shot semantic segmentation,

Reference 14

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Observation c8d8f5a9-341f-4389-8189-1e9363931378 · outbound

This paper cites Vision-language models for vision tasks: A survey,.

Repurposing CLIP to Localize at Pixel Level Vision-language models for vision tasks: A survey,

Reference 15

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Observation 59512194-b341-43c1-9a1d-9083d34749eb · outbound

This paper cites Vqgan-clip: Open domain image generation and editing with natural language guidance,.

Repurposing CLIP to Localize at Pixel Level Vqgan-clip: Open domain image generation and editing with natural language guidance,

Reference 16

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Observation 84a7dd8b-d1ce-46e9-969c-e39209e44795 · outbound

This paper cites Clip-forge: Towards zero-shot text-to-shape generation,.

Repurposing CLIP to Localize at Pixel Level Clip-forge: Towards zero-shot text-to-shape generation,

Reference 17

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Observation f8cc38ae-43b6-4e17-8eaf-008fdc32a794 · outbound

This paper cites Clip-driven semantic discovery network for visible-infrared person re-identification,.

Repurposing CLIP to Localize at Pixel Level Clip-driven semantic discovery network for visible-infrared person re-identification,

Reference 18

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Observation 166668c0-3673-486a-a43b-c8539882f25d · outbound

This paper cites Clip-based modality compensation for visible-infrared image re-identification,.

Repurposing CLIP to Localize at Pixel Level Clip-based modality compensation for visible-infrared image re-identification,

Reference 19

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Observation 211fc036-8cf8-4845-8f34-9645e4586f24 · outbound

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

Repurposing CLIP to Localize at Pixel Level Maple: Multi-modal prompt learning,

Reference 20

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Observation 9b47e5cd-68af-420d-a50e-7a3232733538 · outbound

This paper cites Clip-kd: An empirical study of clip model distillation,.

Repurposing CLIP to Localize at Pixel Level Clip-kd: An empirical study of clip model distillation,

Reference 21

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Observation c95bc355-050a-4418-a845-fa093cd58487 · outbound

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

Repurposing CLIP to Localize at Pixel Level Learning to prompt for vision- language models,

Reference 22

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Observation 8c2cff21-0360-46fd-8568-fdeddcaf20d2 · outbound

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

Repurposing CLIP to Localize at Pixel Level Conditional prompt learning for vision-language models,

Reference 23

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Observation 094528d3-353b-492b-834a-039a87a63391 · outbound

This paper cites Zero-shot semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Zero-shot semantic segmentation,

Reference 24

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Observation 4d472a74-20a4-4041-9816-dfb8e285528e · outbound

This paper cites Dual-guided fre- quency prototype network for few-shot semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Dual-guided fre- quency prototype network for few-shot semantic segmentation,

Reference 25

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Observation 1ac944bc-8ffb-49e8-83e9-a99d9fac7c8d · outbound

This paper cites Semantic projection network for zero-and few-label semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Semantic projection network for zero-and few-label semantic segmentation,

Reference 26

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Observation 0579c29f-da5c-45b5-acc8-0fe80f7deda4 · outbound

This paper cites Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels.

Repurposing CLIP to Localize at Pixel Level Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels

Reference 27

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Observation baec2f08-2ce5-4c33-a067-ed7148e6b50a · outbound

This paper cites Hypercorrelation squeeze for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Hypercorrelation squeeze for few-shot segmentation,

Reference 28

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Observation f035bacd-01bd-4b57-8f4a-a3c5e5b65434 · outbound

This paper cites Self-support few-shot semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Self-support few-shot semantic segmentation,

Reference 29

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Observation 0063a34b-0436-4733-bf0a-770299fc405f · outbound

This paper cites Learning what not to segment: A new perspective on few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Learning what not to segment: A new perspective on few-shot segmentation,

Reference 30

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Observation 0ab055c7-da43-484b-a60e-46b5e38bfe03 · outbound

This paper cites Mianet: Aggregating unbiased instance and general information for few-shot semantic seg- mentation,.

Repurposing CLIP to Localize at Pixel Level Mianet: Aggregating unbiased instance and general information for few-shot semantic seg- mentation,

Reference 31

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Observation 7b652a10-1360-4546-8885-b1067eda886a · outbound

This paper cites Hierarchical dense correlation distillation for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Hierarchical dense correlation distillation for few-shot segmentation,

Reference 32

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Observation ea4743f0-9896-4032-adef-76c7e55b7b7c · outbound

This paper cites Hybrid mamba for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Hybrid mamba for few-shot segmentation,

Reference 33

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Observation cb3aa4c2-f229-4ff7-aacf-4d894cd369aa · outbound

This paper cites Eliminating feature ambiguity for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Eliminating feature ambiguity for few-shot segmentation,

Reference 34

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Observation 9d9cc63c-06f7-4964-9e02-fea185e6bad2 · outbound

This paper cites Addressing background context bias in few-shot segmentation through iterative modulation,.

Repurposing CLIP to Localize at Pixel Level Addressing background context bias in few-shot segmentation through iterative modulation,

Reference 35

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Observation fbe6019f-22a0-4d0a-9e3d-ed6a6b6b5b7a · outbound

This paper cites Rethinking prior information generation with clip for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Rethinking prior information generation with clip for few-shot segmentation,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-11T07:30:42.555053Z

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Observation d5b4e010-7aa0-4cc1-8823-ce73af8d7098 · outbound

This paper cites Llafs++: Few-shot image segmentation with large language models,.

Repurposing CLIP to Localize at Pixel Level Llafs++: Few-shot image segmentation with large language models,

Reference 37

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Observation 3c1f8ffc-769e-4f29-9ca5-80a52f1e5305 · outbound

This paper cites Dsv-lfs: Unifying llm-driven semantic cues with visual features for robust few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Dsv-lfs: Unifying llm-driven semantic cues with visual features for robust few-shot segmentation,

Reference 38

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Observation ba6938f1-2519-4a2f-a058-4df965febc78 · outbound

This paper cites Prior guided feature enrichment network for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Prior guided feature enrichment network for few-shot segmentation,

Reference 39

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Observation 4905a857-1d18-4540-be00-dd65e33c91f8 · outbound

This paper cites Holistic prototype activation for few- shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Holistic prototype activation for few- shot segmentation,

Reference 40

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Observation 1b34b495-84f3-494e-b817-5b49c33b8403 · outbound

This paper cites Base and meta: A new perspective on few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Base and meta: A new perspective on few-shot segmentation,

Reference 41

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Observation b4d766d5-569e-43a5-8a9a-0d2082cca5a0 · outbound

This paper cites Image segmentation using text and image prompts,.

Repurposing CLIP to Localize at Pixel Level Image segmentation using text and image prompts,

Reference 42

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Observation bf93ba5e-f418-437e-9b38-bdfdd26dc68b · outbound

This paper cites UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks.

Repurposing CLIP to Localize at Pixel Level UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks

Reference 43

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Observation f59638c1-b918-4ecc-b757-02be25c64f59 · outbound

This paper cites Prompt-and-transfer: Dynamic class-aware enhancement for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Prompt-and-transfer: Dynamic class-aware enhancement for few-shot segmentation,

Reference 44

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Observation 87038aeb-4953-4e41-8944-67a1d64ff2ca · outbound

This paper cites Doubly deformable aggregation of covariance matrices for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Doubly deformable aggregation of covariance matrices for few-shot segmentation,

Reference 45

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Observation 1c9466d6-532b-40b6-89b1-4b9367614a5a · outbound

This paper cites Semantic projection network for zero-and few-label semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Semantic projection network for zero-and few-label semantic segmentation,

Reference 46

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Observation e6ff44bb-708b-4cdb-a7c8-3868eff4b1e9 · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Repurposing CLIP to Localize at Pixel Level One-Shot Learning for Semantic Segmentation

Reference 47

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Observation d4678575-cc1a-4da3-9cb9-e8b967c5083b · outbound

This paper cites Feature weighting and boosting for few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Feature weighting and boosting for few-shot segmentation,

Reference 48

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Observation 593500bf-2f5c-4dd8-90da-3f51717f70d8 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Repurposing CLIP to Localize at Pixel Level Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 49

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Observation 7f102401-0cf0-4507-a020-f4dca11edb89 · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

Repurposing CLIP to Localize at Pixel Level Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 50

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Observation 40ddd1e2-42be-4c98-88e3-908d1a870003 · outbound

This paper cites Improved baselines with visual instruction tuning,.

Repurposing CLIP to Localize at Pixel Level Improved baselines with visual instruction tuning,

Reference 51

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Observation 70896c48-5cf3-4ae4-ace0-3b9e2c85c1e6 · outbound

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

Repurposing CLIP to Localize at Pixel Level BLIP-2: bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 52

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Observation 247b87e5-5555-41a4-a39d-e62060a81dbe · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning,.

Repurposing CLIP to Localize at Pixel Level Images speak in images: A generalist painter for in-context visual learning,

Reference 53

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Observation 50103b13-64b9-49ac-af6a-ea672018cdae · outbound

This paper cites Seggpt: Towards segmenting everything in context,.

Repurposing CLIP to Localize at Pixel Level Seggpt: Towards segmenting everything in context,

Reference 54

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Observation 7a5064c0-4473-4c7b-971d-588fea545268 · outbound

This paper cites Llafs: When large language models meet few-shot segmentation,.

Repurposing CLIP to Localize at Pixel Level Llafs: When large language models meet few-shot segmentation,

Reference 55

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Observation f5417dd9-83bb-41be-b564-c398d5e5ecff · outbound

This paper cites Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation.

Repurposing CLIP to Localize at Pixel Level Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation

Reference 56

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Observation e4a39697-9ee0-4fda-aa1b-323116a116b6 · outbound

This paper cites Sclip: Rethinking self-attention for dense vision-language inference,.

Repurposing CLIP to Localize at Pixel Level Sclip: Rethinking self-attention for dense vision-language inference,

Reference 57

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Observation 13b40b65-1105-4392-856f-aeb78a4fb01c · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild,.

Repurposing CLIP to Localize at Pixel Level The role of context for object detection and semantic segmentation in the wild,

Reference 58

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Observation a2e128c0-aa69-42b9-8d1c-1d7f4f1384f3 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

Repurposing CLIP to Localize at Pixel Level The cityscapes dataset for semantic urban scene understanding,

Reference 59

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Observation b929c15b-e710-4462-ba85-b6b41f4694d4 · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset,.

Repurposing CLIP to Localize at Pixel Level Semantic understanding of scenes through the ade20k dataset,

Reference 60

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Observation ce6252fd-1920-4f7b-a4e6-c1747b8977e1 · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision,.

Repurposing CLIP to Localize at Pixel Level Groupvit: Semantic segmentation emerges from text supervision,

Reference 61

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Observation 635e8d53-160b-46cf-8474-9e69ca25969e · outbound

This paper cites Clip-dinoiser: Teaching clip a few dino tricks for open-vocabulary semantic segmentation,.

Repurposing CLIP to Localize at Pixel Level Clip-dinoiser: Teaching clip a few dino tricks for open-vocabulary semantic segmentation,

Reference 62

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Observation 269ca8c7-0048-41ec-91d0-1a8749bd1501 · outbound

This paper cites Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs,.

Repurposing CLIP to Localize at Pixel Level Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs,

Reference 63

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Observation 6b5a36ba-2e76-4552-a424-3b5820b45669 · outbound

This paper cites Clearclip: Decomposing clip representations for dense vision-language inference,.

Repurposing CLIP to Localize at Pixel Level Clearclip: Decomposing clip representations for dense vision-language inference,

Reference 64

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Observation 94e91cfb-a81a-45f8-b8e9-fdf82f1a6ef4 · outbound

This paper cites Resclip: Residual attention for training-free dense vision-language inference,.

Repurposing CLIP to Localize at Pixel Level Resclip: Residual attention for training-free dense vision-language inference,

Reference 65

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