Decouple before Align: Visual Disentanglement Enhances Prompt Tuning | Paper Citation Record

Observation 8632558a-6c95-4da2-a4a8-bc576b5a62a1 · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning transferable visual models from natural language supervision,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.509893Z

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Observation 5091dcff-1478-4b3a-bb80-0cd17ec039b8 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.496240Z

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Observation 7a6abef0-88a1-45b9-ae4d-982f8a17477d · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Open-vocabulary object detection via vision and language knowledge distillation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.481344Z

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Observation a36114ee-e4ad-4964-a24e-18cf1af3f39c · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Denseclip: Language-guided dense prediction with context-aware prompting,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.467285Z

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Observation c9d6aed8-9ae1-4fe8-94d5-e13d4f1f16ed · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.321912Z

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Observation 90f8933b-f5ac-48ae-8d4f-95003421b66f · outbound

This paper cites Language models are few-shot learners,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Language models are few-shot learners,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.453312Z

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Observation 58e08433-a596-4abc-97d1-4fbd70a97bf5 · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning to prompt for vision-language models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.438717Z

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Observation 91a357cc-5361-4042-9cab-0aae4796dabf · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Conditional prompt learning for vision-language models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.424974Z

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Observation 5055badf-2fd0-4831-a8f8-0efad9fa4632 · outbound

This paper cites Visual prompt tuning,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Visual prompt tuning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.339259Z

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Observation 3228aebb-f75e-4380-a41e-cbe3867d2096 · outbound

This paper cites Prompt- aligned gradient for prompt tuning,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Prompt- aligned gradient for prompt tuning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.402770Z

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Observation db157fd6-4d06-43c0-8d8b-40fef442ee9c · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Maple: Multi-modal prompt learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.389577Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7290177-dbcc-48bf-afe5-da6924556643 · outbound

This paper cites What does CLIP know about a red circle? Visual prompt engineering for VLMs.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning What does CLIP know about a red circle? Visual prompt engineering for VLMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.350951Z

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Observation 329963e4-2f87-4401-b2b4-9ef2f7e2a7f6 · outbound

This paper cites Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.354989Z

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Observation 843cadba-1961-4b0e-a955-8b8ff549fdb1 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.377214Z

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Observation e24704fb-5804-4974-9e33-866322ec62e6 · outbound

This paper cites Segment everything everywhere all at once,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Segment everything everywhere all at once,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.364420Z

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Observation 35825a5e-64f2-4e44-9fd9-7bdab9f24535 · outbound

This paper cites Plot: Prompt learning with optimal transport for vision- language models,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Plot: Prompt learning with optimal transport for vision- language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.352031Z

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Observation 46f1fbb1-31d4-44f8-af18-fda37a12369d · outbound

This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Visual-language prompt tuning with knowledge-guided context optimization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.338638Z

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Observation 49d9db32-b6f5-43aa-9917-b4505847b026 · outbound

This paper cites Texts as images in prompt tuning for multi-label image recognition,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Texts as images in prompt tuning for multi-label image recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.324937Z

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Observation 82635ffc-243f-4c9b-82f4-47c61e036c57 · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Self-regulating prompts: Foundational model adaptation without forgetting,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.312631Z

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Observation b0e24e3e-a9ee-4426-a704-b1ff1a652e5f · outbound

This paper cites Promptkd: Unsupervised prompt distil- lation for vision-language models,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Promptkd: Unsupervised prompt distil- lation for vision-language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.299961Z

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Observation 6d88f0fe-861a-4fb3-8a2f-a4e6626333a8 · outbound

This paper cites Domain prompt learning with quaternion networks,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Domain prompt learning with quaternion networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.287465Z

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Observation 90f0c0a7-e27a-4721-9507-23202a36aa46 · outbound

This paper cites Generalized domain prompt learning for accessible scientific vision-language models,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Generalized domain prompt learning for accessible scientific vision-language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.275125Z

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Observation c204adb9-9f26-4c90-af62-1bd3c7c52e26 · outbound

This paper cites Segment Anything.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Segment Anything

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.396669Z

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Observation 4af2ae64-23b7-4343-b49c-c0478581c7c7 · outbound

This paper cites Complementary patch for weakly supervised semantic segmentation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Complementary patch for weakly supervised semantic segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.261312Z

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Observation e413f948-1327-4e4b-a2ac-03f051e01a0f · outbound

This paper cites Uncovering prototyp- ical knowledge for weakly open-vocabulary semantic JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, OCTOBER 2024 14 segmentation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Uncovering prototyp- ical knowledge for weakly open-vocabulary semantic JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, OCTOBER 2024 14 segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.248356Z

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Observation 9fa6da40-f9fb-423f-acfe-880aa7eeeee8 · outbound

This paper cites Attrseg: open-vocabulary semantic segmentation via at- tribute decomposition-aggregation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Attrseg: open-vocabulary semantic segmentation via at- tribute decomposition-aggregation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.235429Z

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Observation 92e5a3c8-82ef-46e7-af4d-a3929db0af43 · outbound

This paper cites DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.412924Z

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Observation b6620bff-3b9f-424f-a783-64faa8d8b597 · outbound

This paper cites AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:17:04.780664Z

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Observation ecd0fce9-c3ec-473a-87c9-d5c271454a58 · outbound

This paper cites Probabilistic conformal distillation for enhanc- ing missing modality robustness,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Probabilistic conformal distillation for enhanc- ing missing modality robustness,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.222713Z

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Observation 67d5aa8e-f874-4373-bc2a-3b5bcc26b4d2 · outbound

This paper cites ConText: Driving In-context Learning for Text Removal and Segmentation.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning ConText: Driving In-context Learning for Text Removal and Segmentation

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:17:04.762107Z

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Observation 1a1a3170-d693-448e-9ce7-18397ec3bc8c · outbound

This paper cites G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:17:04.743285Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b82e79ae-eca6-4195-8f4e-81fa959d533a · outbound

This paper cites Fine- grained visual prompting,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Fine- grained visual prompting,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.210154Z

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Observation 33492d0b-5933-4532-80aa-5a9c1a96a1d7 · outbound

This paper cites Alpha-clip: A clip model focusing on wherever you want,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Alpha-clip: A clip model focusing on wherever you want,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.197312Z

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Observation 8542b140-649b-4055-9c84-073e34113362 · outbound

This paper cites Vip-llava: Making large mul- timodal models understand arbitrary visual prompts,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Vip-llava: Making large mul- timodal models understand arbitrary visual prompts,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.184585Z

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Observation b8b1a979-c9c9-496f-93cc-c05b53b18b6d · outbound

This paper cites Learning deep features for discriminative localization,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning deep features for discriminative localization,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.172186Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8fb1669c-1190-4642-84eb-bd258fe69637 · outbound

This paper cites Weakly-supervised semantic segmentation by iteratively mining common object features,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Weakly-supervised semantic segmentation by iteratively mining common object features,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.159498Z

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Observation 4cda64fe-3a06-4736-bdd4-f7f7f7211c48 · outbound

This paper cites Learning affinity from attention: end-to-end weakly-supervised semantic segmentation with transformers,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning affinity from attention: end-to-end weakly-supervised semantic segmentation with transformers,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.146563Z

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Observation 62873a9b-c56b-4f7f-98bf-49240d3e6394 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning SegGPT: Segmenting Everything In Context

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.461068Z

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Observation 6ab70f0f-1fca-49a2-89c9-53fae3069442 · outbound

This paper cites Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.133890Z

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Observation f897479b-681a-4a5c-b383-0389db5b1d4b · outbound

This paper cites Learning to rank in person re-identification with metric ensembles,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning to rank in person re-identification with metric ensembles,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.120661Z

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Observation db7a3e62-8145-44e6-ad75-38f22b6a476f · outbound

This paper cites Large-scale unsupervised semantic segmenta- tion,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Large-scale unsupervised semantic segmenta- tion,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.108147Z

Source-reported events for the cited work

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Observation 51d94a14-c22e-4cf8-8d4e-fb8564735adc · outbound

This paper cites Learning gener- ative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning gener- ative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.095104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 96dfb658-8e52-4658-9f1f-fa98c5429d56 · outbound

This paper cites Cats and dogs,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Cats and dogs,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.082054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cca6fbb5-1cef-48ef-bde5-d3b86b3ea342 · outbound

This paper cites 3d object representations for fine-grained categorization,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning 3d object representations for fine-grained categorization,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.069197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0ee894b3-fc2d-445d-ad8e-cf3b15845764 · outbound

This paper cites Automated flower classification over a large number of classes,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Automated flower classification over a large number of classes,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.055872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76fce46b-8fed-46d9-abb2-da05f5de353f · outbound

This paper cites Food- 101–mining discriminative components with random forests,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Food- 101–mining discriminative components with random forests,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.043246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83a4b08d-0b25-40ff-98b6-030db720cfd3 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Fine-Grained Visual Classification of Aircraft

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.498073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61aa026d-264a-4c8e-8270-78b07e5d8c37 · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Sun database: Large-scale scene recognition from abbey to zoo,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.029654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 15b3c7cf-32bf-4421-a999-6458a82b8657 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.506212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b19a19f-c70a-4145-a876-69db582470c6 · outbound

This paper cites Describing textures in the wild,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Describing textures in the wild,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.016654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20d35196-5701-4bce-a800-efb767c63838 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:05.003783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5545c0cd-1845-46b0-b809-fb4eab9fe644 · outbound

This paper cites Do imagenet classifiers generalize to imagenet?.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Do imagenet classifiers generalize to imagenet?

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.991215Z

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Observation 3449f01d-c8dd-409c-ae09-de7eeeb76181 · outbound

This paper cites Learning robust global representations by penalizing local predic- tive power,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Learning robust global representations by penalizing local predic- tive power,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.978756Z

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Observation 9b1975b8-52e7-4d5d-8696-db0ea5345977 · outbound

This paper cites Natural adversarial examples,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Natural adversarial examples,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.965947Z

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Observation 3a502d18-e5fc-4cfc-ae0d-415ddd33f752 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning The many faces of robustness: A critical analysis of out-of-distribution generalization,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.952874Z

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Observation 3c2b402b-3c24-4b85-a875-0c30f15ac9d9 · outbound

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

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Blip-2: Bootstrap- ping language-image pre-training with frozen image encoders and large language models,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.940251Z

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Observation 7bec0db0-b627-4e0c-8a1b-958301d1a790 · outbound

This paper cites Deepcore: A comprehen- sive library for coreset selection in deep learning,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Deepcore: A comprehen- sive library for coreset selection in deep learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.926640Z

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Observation 55cd18a0-5646-4caa-96ee-c4dce0946636 · outbound

This paper cites A combinatorial strongly polynomial algorithm for minimizing submod- ular functions,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning A combinatorial strongly polynomial algorithm for minimizing submod- ular functions,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.911820Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d154144-7ada-4e10-bd63-a14f107b3b9b · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.546588Z

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Observation 903a19b4-b084-4cf0-92df-b3a8b943b177 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Glister: Generalization based data subset selection for efficient and robust learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.898942Z

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Observation 99b34c44-5825-411c-b2f1-7b5efc6b5924 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Deep learning on a data diet: Finding important examples early in training,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.886054Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 601c9ab7-57e0-48d3-b90f-955fa93aa16d · outbound

This paper cites Active Learning by Acquiring Contrastive Examples.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Active Learning by Acquiring Contrastive Examples

Reference 62

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Observation 2402a63d-9b96-4615-8a1b-c9b39f7476be · outbound

This paper cites A survey on label- efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning A survey on label- efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.872609Z

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Observation e7e731e8-eed8-44f6-9dd0-1ce37b11096b · outbound

This paper cites FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:17:04.659799Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0276194c-b1d0-4698-9f5e-68a15d521121 · outbound

This paper cites Fine-Grained Visual Prompting.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Fine-Grained Visual Prompting

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.571789Z

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Observation 95fb7e37-6ce4-43ab-8e00-532a4ecd17e2 · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.575798Z

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Observation 8f8c706e-e85d-4188-81c3-27907f1e66ae · outbound

This paper cites A compre- hensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes,.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning A compre- hensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:17:04.859296Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b4cec6f-1885-4c62-aeb6-936a2a2d803c · outbound

This paper cites Noise or Signal: The Role of Image Backgrounds in Object Recognition.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning Noise or Signal: The Role of Image Backgrounds in Object Recognition

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T10:17:04.584054Z

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