Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation | Paper Citation Record

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This paper cites Layer Normalization.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Layer Normalization

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This paper cites Grounding Everything: Emerging Localization Properties in Vision-Language Transformers.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Grounding Everything: Emerging Localization Properties in Vision-Language Transformers

Reference 2

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This paper cites Coco- stuff: Thing and stuff classes in context.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Coco- stuff: Thing and stuff classes in context

Reference 3

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This paper cites Cascade r-cnn: Delv- ing into high quality object detection.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Cascade r-cnn: Delv- ing into high quality object detection

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Emerg- ing properties in self-supervised vision transformers

Reference 5

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 6

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This paper cites Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only

Reference 7

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Observation 958a5d45-d0db-4db2-8a6d-c04114790529 · outbound

This paper cites A simple framework for contrastive learn- ing of visual representations.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation A simple framework for contrastive learn- ing of visual representations

Reference 8

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Observation 5b0f5a70-6349-4a51-9dcc-f9babf4c1aed · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Reproducible scal- ing laws for contrastive language-image learning

Reference 9

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This paper cites All at once: Temporally adaptive multi-frame interpolation with advanced motion modeling.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation All at once: Temporally adaptive multi-frame interpolation with advanced motion modeling

Reference 10

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This paper cites Test- time fast adaptation for dynamic scene deblurring via meta- auxiliary learning.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Test- time fast adaptation for dynamic scene deblurring via meta- auxiliary learning

Reference 11

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Observation e66c17d7-8f9d-44da-9760-51f451da07a9 · outbound

This paper cites Adapt- ing to distribution shift by visual domain prompt generation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Adapt- ing to distribution shift by visual domain prompt generation

Reference 12

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Observation 919d2c6c-5556-4503-af56-b14b3b72d9b1 · outbound

This paper cites Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation

Reference 13

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This paper cites Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark,.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark,

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 15

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Observation 8ae0564f-4689-4d09-a9df-c64dc7ae4086 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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This paper cites The pascal visual object classes challenge 2012 (voc2012) development kit.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation The pascal visual object classes challenge 2012 (voc2012) development kit

Reference 17

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This paper cites Bootstrap your own latent: A new ap- proach to self-supervised learning.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Bootstrap your own latent: A new ap- proach to self-supervised learning

Reference 18

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This paper cites On calibration of modern neural networks.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation On calibration of modern neural networks

Reference 19

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This paper cites How to train the teacher model for effective knowledge distillation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation How to train the teacher model for effective knowledge distillation

Reference 20

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This paper cites Masked autoencoders are scalable vision learners.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Masked autoencoders are scalable vision learners

Reference 21

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This paper cites Distilling the Knowledge in a Neural Network.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Distilling the Knowledge in a Neural Network

Reference 22

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 23

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This paper cites Weakly supervised ground- ing for vqa in vision-language transformers.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Weakly supervised ground- ing for vqa in vision-language transformers

Reference 24

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This paper cites Segment Anything.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Segment Anything

Reference 25

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This paper cites ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Reference 26

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This paper cites ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

Reference 27

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

Reference 28

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This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 29

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This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 30

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This paper cites Self-supervised spa- tiotemporal representation learning by exploiting video con- tinuity.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Self-supervised spa- tiotemporal representation learning by exploiting video con- tinuity

Reference 31

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This paper cites Refinenet: Multi-path refinement networks for high- resolution semantic segmentation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Refinenet: Multi-path refinement networks for high- resolution semantic segmentation

Reference 32

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This paper cites Few-shot class-incremental learning via entropy-regularized data-free replay.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Few-shot class-incremental learning via entropy-regularized data-free replay

Reference 33

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This paper cites Meta-auxiliary learning for future depth pre- diction in videos.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Meta-auxiliary learning for future depth pre- diction in videos

Reference 34

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

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This paper cites Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems , 34: 21808–21820, 2021.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems , 34: 21808–21820, 2021

Reference 36

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation The role of context for object detection and semantic segmentation in the wild

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This paper cites Open vocabulary semantic segmentation with patch aligned con- trastive learning.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Open vocabulary semantic segmentation with patch aligned con- trastive learning

Reference 38

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation DINOv2: Learning Robust Visual Features without Supervision

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Learning transferable visual models from natural language supervi- sion

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 41

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This paper cites Test- time prompt tuning for zero-shot generalization in vision- language models.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 42

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Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Test-time training with self- supervision for generalization under distribution shifts

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This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 44

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This paper cites Tent: Fully test-time adaptation by entropy minimization.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Tent: Fully test-time adaptation by entropy minimization

Reference 45

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This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 46

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This paper cites SAM-CLIP: Merging vision foundation mod- els towards semantic and spatial understanding.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation SAM-CLIP: Merging vision foundation mod- els towards semantic and spatial understanding

Reference 47

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This paper cites Distribution align- ment for fully test-time adaptation with dynamic online data streams.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Distribution align- ment for fully test-time adaptation with dynamic online data streams

Reference 48

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This paper cites Robust fine-tuning of zero-shot models.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Robust fine-tuning of zero-shot models

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This paper cites CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 50

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Observation 452a3e49-6ddb-4fe7-9bd4-21b489b43d46 · outbound

This paper cites Metagcd: Learning to continually learn in generalized cat- egory discovery.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Metagcd: Learning to continually learn in generalized cat- egory discovery

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:51.300948Z

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Observation 767de156-5785-431c-866f-2d0550357d28 · outbound

This paper cites Test-time domain adaptation by learning domain-aware batch normalization.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Test-time domain adaptation by learning domain-aware batch normalization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:51.105747Z

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Observation 32ddfacc-5600-41c8-9c72-85f9cbef18d3 · outbound

This paper cites CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation

Reference 53

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Observation 41eacf99-e32c-4ed0-9ead-b95f248e42be · outbound

This paper cites Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:50.873604Z

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Observation 8200cad9-845d-4632-8902-1b7b0eb19665 · outbound

This paper cites Demystifying CLIP Data.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Demystifying CLIP Data

Reference 55

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Observation f6c48f5f-4cae-4540-bd05-d69b88cdfe90 · outbound

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

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Groupvit: Semantic segmentation emerges from text supervision

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:50.620230Z

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Observation 5445e7df-5a99-4794-974e-3c6b94c3f27e · outbound

This paper cites Markov knowledge distil- lation: Make nasty teachers trained by self-undermining knowledge distillation fully distillable.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Markov knowledge distil- lation: Make nasty teachers trained by self-undermining knowledge distillation fully distillable

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:50.386358Z

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Observation e04b65be-2ab3-4623-ba98-de063c856119 · outbound

This paper cites Conditional mutual information con- strained deep learning: Framework and preliminary results.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Conditional mutual information con- strained deep learning: Framework and preliminary results

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:50.089977Z

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Observation d453b326-abd3-4c96-952d-b793ab74e514 · outbound

This paper cites Bayes conditional distribution estimation for knowledge distillation based on conditional mutual informa- tion.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Bayes conditional distribution estimation for knowledge distillation based on conditional mutual informa- tion

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:49.777746Z

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Observation 68faeeef-c03d-4e8f-b2aa-87c75fda512c · outbound

This paper cites Towards undistillable models by minimizing conditional mu- tual information.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Towards undistillable models by minimizing conditional mu- tual information

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:49.569716Z

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Observation 71e4a3e5-1462-4f26-a39b-c1299d645184 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 61

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Observation 632dfc9b-daa1-42d8-bbc5-a7939c911bd0 · outbound

This paper cites Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:18:47.960712Z

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Observation 0de2b30c-e202-4f2b-b83a-d71e8e3f6d49 · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:49.317783Z

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Observation d6ac0135-095d-4f49-b2b3-33a426c69a0c · outbound

This paper cites Self- distillation: Towards efficient and compact neural networks.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Self- distillation: Towards efficient and compact neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:49.081325Z

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Observation 9943b111-95f2-4d86-801c-61ae06673861 · outbound

This paper cites Meta-dmoe: Adapting to domain shift by meta- distillation from mixture-of-experts.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Meta-dmoe: Adapting to domain shift by meta- distillation from mixture-of-experts

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:48.831920Z

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Observation b821efcd-7313-4474-a892-5e669569f3c5 · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Semantic under- standing of scenes through the ade20k dataset

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:48.588304Z

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Observation b249469a-dfb8-4fc5-93c8-81469ebd2797 · outbound

This paper cites Extract free dense labels from clip.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Extract free dense labels from clip

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:18:48.387446Z

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