Distillation of Diffusion Features for Semantic Correspondence | Paper Citation Record

Observation be09a5fb-5527-4516-b6a1-5649e8772b4b · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Distillation of Diffusion Features for Semantic Correspondence Deep ViT Features as Dense Visual Descriptors

Reference 1

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Observation 7dd3006f-bd6c-4e21-b71c-3bd55a7b6f92 · outbound

This paper cites Segdiff: Image segmentation with diffusion proba- bilistic models, 2022.

Distillation of Diffusion Features for Semantic Correspondence Segdiff: Image segmentation with diffusion proba- bilistic models, 2022

Reference 2

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Observation c5c61ad1-4f54-4f4e-8755-8110f109fba6 · outbound

This paper cites Parameter efficient fine-tuning of self- supervised vits without catastrophic forgetting, 2024.

Distillation of Diffusion Features for Semantic Correspondence Parameter efficient fine-tuning of self- supervised vits without catastrophic forgetting, 2024

Reference 3

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Observation f469bf3a-e1c6-4588-b93e-e961a0a7a112 · outbound

This paper cites Label-efficient semantic segmentation with diffusion models.

Distillation of Diffusion Features for Semantic Correspondence Label-efficient semantic segmentation with diffusion models

Reference 4

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This paper cites Surf: Speeded up robust features.

Distillation of Diffusion Features for Semantic Correspondence Surf: Speeded up robust features

Reference 5

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Observation 43cfa5eb-e0bf-44ec-a28b-86b1d5186341 · outbound

This paper cites Courville, and Pascal Vincent.

Distillation of Diffusion Features for Semantic Correspondence Courville, and Pascal Vincent

Reference 6

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Observation 3812dc25-8d98-4a60-84aa-5a169d086f29 · outbound

This paper cites LoRA Learns Less and Forgets Less.

Distillation of Diffusion Features for Semantic Correspondence LoRA Learns Less and Forgets Less

Reference 7

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Observation 1b965d40-f8a7-48f6-9378-42682d9bd1b8 · outbound

This paper cites Subpixel heatmap regression for facial landmark local- ization.

Distillation of Diffusion Features for Semantic Correspondence Subpixel heatmap regression for facial landmark local- ization

Reference 8

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Observation 582d872a-72a3-4932-9490-69c18ddeb079 · outbound

This paper cites Diffu- siondet: Diffusion model for object detection.

Distillation of Diffusion Features for Semantic Correspondence Diffu- siondet: Diffusion model for object detection

Reference 9

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Observation 73ad56ae-1c8b-4805-857c-94141ad69ae1 · outbound

This paper cites Hinton, and David J.

Distillation of Diffusion Features for Semantic Correspondence Hinton, and David J

Reference 10

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Observation 45e4a267-d7b8-42db-b351-f748913e2287 · outbound

This paper cites Cats: Cost aggregation transformers for visual correspondence.

Distillation of Diffusion Features for Semantic Correspondence Cats: Cost aggregation transformers for visual correspondence

Reference 11

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Observation 63912e09-4ca4-4980-91eb-3d262ac4ab0b · outbound

This paper cites CATs++: Boosting Cost Aggregation with Convolutions and Transformers.

Distillation of Diffusion Features for Semantic Correspondence CATs++: Boosting Cost Aggregation with Convolutions and Transformers

Reference 12

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Observation d4ee3ce6-ed39-4471-ad1d-49c2767551d4 · outbound

This paper cites Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models.

Distillation of Diffusion Features for Semantic Correspondence Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models

Reference 13

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Observation 9fe64cc7-e90f-495c-9d18-8173cce57e5b · outbound

This paper cites Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Krishna Chandraker.

Distillation of Diffusion Features for Semantic Correspondence Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Krishna Chandraker

Reference 14

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Observation 1badf69e-fa84-4fce-9841-0062f6541009 · outbound

This paper cites Diffedit: Diffusion-based semantic image editing with mask guidance.

Distillation of Diffusion Features for Semantic Correspondence Diffedit: Diffusion-based semantic image editing with mask guidance

Reference 15

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Observation 25731bb4-6bf4-45f4-aa56-c1fbc8bd7c2b · outbound

This paper cites Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery.

Distillation of Diffusion Features for Semantic Correspondence Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery

Reference 16

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Observation 2c9899f0-7bfd-42f0-bd5a-5eb71a3dc5a7 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Distillation of Diffusion Features for Semantic Correspondence Diffusion models beat gans on image synthesis

Reference 17

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This paper cites An im- age is worth 16x16 words: Transformers for image recog- nition at scale.

Distillation of Diffusion Features for Semantic Correspondence An im- age is worth 16x16 words: Transformers for image recog- nition at scale

Reference 18

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Observation 59835a1d-f249-47c1-b970-1751bfc9eacd · outbound

This paper cites DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation.

Distillation of Diffusion Features for Semantic Correspondence DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation

Reference 19

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This paper cites Diffusion Models and Representation Learning: A Survey.

Distillation of Diffusion Features for Semantic Correspondence Diffusion Models and Representation Learning: A Survey

Reference 20

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Observation eb5fb8ac-8210-41a8-a5f2-85902c6dde62 · outbound

This paper cites Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

Distillation of Diffusion Features for Semantic Correspondence Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

Reference 21

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This paper cites Aiatrack: Attention in attention 9 for transformer visual tracking.

Distillation of Diffusion Features for Semantic Correspondence Aiatrack: Attention in attention 9 for transformer visual tracking

Reference 22

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This paper cites Do semantic parts emerge in convolutional neural net- works? Int.

Distillation of Diffusion Features for Semantic Correspondence Do semantic parts emerge in convolutional neural net- works? Int

Reference 23

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Observation b1fc8274-df9f-44e8-8972-e2f5e7dabf25 · outbound

This paper cites Generative Adversarial Networks.

Distillation of Diffusion Features for Semantic Correspondence Generative Adversarial Networks

Reference 24

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Observation 98d01253-c4e4-4b44-a93f-838c78d3744a · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Distillation of Diffusion Features for Semantic Correspondence Bootstrap your own latent-a new approach to self-supervised learning

Reference 25

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Observation 10d43d9a-100c-496d-bbe8-c455a295c182 · outbound

This paper cites BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping.

Distillation of Diffusion Features for Semantic Correspondence BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping

Reference 26

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This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Distillation of Diffusion Features for Semantic Correspondence MiniLLM: On-Policy Distillation of Large Language Models

Reference 27

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Observation 379c0acc-0628-4953-8fd6-2585f8f043aa · outbound

This paper cites DepthFM: Fast Monocular Depth Estimation with Flow Matching.

Distillation of Diffusion Features for Semantic Correspondence DepthFM: Fast Monocular Depth Estimation with Flow Matching

Reference 28

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This paper cites ASIC: aligning sparse in-the-wild image collec- tions.

Distillation of Diffusion Features for Semantic Correspondence ASIC: aligning sparse in-the-wild image collec- tions

Reference 29

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This paper cites Proposal flow.

Distillation of Diffusion Features for Semantic Correspondence Proposal flow

Reference 30

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This paper cites Rezende, Bumsub Ham, Kwan-Yee K.

Distillation of Diffusion Features for Semantic Correspondence Rezende, Bumsub Ham, Kwan-Yee K

Reference 31

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This paper cites Unsupervised semantic correspondence using stable diffusion.

Distillation of Diffusion Features for Semantic Correspondence Unsupervised semantic correspondence using stable diffusion

Reference 32

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This paper cites Prompt-to-prompt im- age editing with cross-attention control.

Distillation of Diffusion Features for Semantic Correspondence Prompt-to-prompt im- age editing with cross-attention control

Reference 33

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This paper cites Distilling the Knowledge in a Neural Network.

Distillation of Diffusion Features for Semantic Correspondence Distilling the Knowledge in a Neural Network

Reference 34

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Distillation of Diffusion Features for Semantic Correspondence Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 35

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 36

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This paper cites Twigg, Po-Chen Wu, Junsong Yuan, Cem Keskin, and Robert Wang.

Distillation of Diffusion Features for Semantic Correspondence Twigg, Po-Chen Wu, Junsong Yuan, Cem Keskin, and Robert Wang

Reference 37

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 38

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This paper cites Difnet: Semantic segmentation by diffu- sion networks.

Distillation of Diffusion Features for Semantic Correspondence Difnet: Semantic segmentation by diffu- sion networks

Reference 39

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This paper cites COTR: correspondence transformer for matching across images.

Distillation of Diffusion Features for Semantic Correspondence COTR: correspondence transformer for matching across images

Reference 40

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This paper cites Imagic: Text-based real image editing with diffusion mod- els.

Distillation of Diffusion Features for Semantic Correspondence Imagic: Text-based real image editing with diffusion mod- els

Reference 41

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This paper cites Scherer, K.

Distillation of Diffusion Features for Semantic Correspondence Scherer, K

Reference 42

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This paper cites Recurrent transformer net- works for semantic correspondence.

Distillation of Diffusion Features for Semantic Correspondence Recurrent transformer net- works for semantic correspondence

Reference 43

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Observation 0cbd6594-ec27-4ab7-b3cf-8698d277a24a · outbound

This paper cites FCSS: fully convolutional self- similarity for dense semantic correspondence.

Distillation of Diffusion Features for Semantic Correspondence FCSS: fully convolutional self- similarity for dense semantic correspondence

Reference 44

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This paper cites Transfor- matcher: Match-to-match attention for semantic correspon- dence.

Distillation of Diffusion Features for Semantic Correspondence Transfor- matcher: Match-to-match attention for semantic correspon- dence

Reference 45

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 46

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Observation a4ae2e79-e12a-415e-addf-b086b5b06049 · outbound

This paper cites To the point: Correspondence-driven monocular 3d category reconstruc- tion.

Distillation of Diffusion Features for Semantic Correspondence To the point: Correspondence-driven monocular 3d category reconstruc- tion

Reference 47

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This paper cites Sfnet: Learning object-aware semantic correspon- dence.

Distillation of Diffusion Features for Semantic Correspondence Sfnet: Learning object-aware semantic correspon- dence

Reference 48

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 49

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This paper cites Li, Mihir Prabhudesai, Shivam Duggal, El- lis Brown, and Deepak Pathak.

Distillation of Diffusion Features for Semantic Correspondence Li, Mihir Prabhudesai, Shivam Duggal, El- lis Brown, and Deepak Pathak

Reference 50

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This paper cites Costain, Henry Howard- Jenkins, and Victor Prisacariu.

Distillation of Diffusion Features for Semantic Correspondence Costain, Henry Howard- Jenkins, and Victor Prisacariu

Reference 51

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Observation 1cd50be7-d0d8-4045-affb-49d0e439b935 · outbound

This paper cites Probabilistic model distillation for se- mantic correspondence.

Distillation of Diffusion Features for Semantic Correspondence Probabilistic model distillation for se- mantic correspondence

Reference 52

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Observation 7c018949-9e27-4f7c-8f8b-2a8c2b3a3e08 · outbound

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Distillation of Diffusion Features for Semantic Correspondence SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning

Reference 53

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This paper cites SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching.

Distillation of Diffusion Features for Semantic Correspondence SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching

Reference 54

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This paper cites Data Distillation for Text Classification.

Distillation of Diffusion Features for Semantic Correspondence Data Distillation for Text Classification

Reference 55

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This paper cites Cycle-consistency based hierarchical dense semantic cor- respondence.

Distillation of Diffusion Features for Semantic Correspondence Cycle-consistency based hierarchical dense semantic cor- respondence

Reference 56

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This paper cites Microsoft COCO: Common Objects in Context.

Distillation of Diffusion Features for Semantic Correspondence Microsoft COCO: Common Objects in Context

Reference 57

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This paper cites Scale Invariant Feature Transform, vol- ume 7.

Distillation of Diffusion Features for Semantic Correspondence Scale Invariant Feature Transform, vol- ume 7

Reference 58

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This paper cites Sift flow: Dense correspondence across scenes and its applications.

Distillation of Diffusion Features for Semantic Correspondence Sift flow: Dense correspondence across scenes and its applications

Reference 59

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This paper cites Structured knowledge dis- tillation for semantic segmentation.

Distillation of Diffusion Features for Semantic Correspondence Structured knowledge dis- tillation for semantic segmentation

Reference 60

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This paper cites Do con- vnets learn correspondence? In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D.

Distillation of Diffusion Features for Semantic Correspondence Do con- vnets learn correspondence? In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.167820Z

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Observation 3f05e4b5-c657-48b2-96ae-3fbd418c907f · outbound

This paper cites Diffusion hyperfeatures: Searching through time and space for semantic correspon- dence.

Distillation of Diffusion Features for Semantic Correspondence Diffusion hyperfeatures: Searching through time and space for semantic correspon- dence

Reference 62

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This paper cites Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps.

Distillation of Diffusion Features for Semantic Correspondence Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps

Reference 63

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Observation b13790e7-ca1d-4c12-8958-2120ed278b3d · outbound

This paper cites Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans.

Distillation of Diffusion Features for Semantic Correspondence Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans

Reference 64

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This paper cites Con- ditional teacher-student learning.

Distillation of Diffusion Features for Semantic Correspondence Con- ditional teacher-student learning

Reference 65

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Observation c501aa75-1cee-4245-b38c-442ba2024efd · outbound

This paper cites SPair-71k: A Large-scale Benchmark for Semantic Correspondence.

Distillation of Diffusion Features for Semantic Correspondence SPair-71k: A Large-scale Benchmark for Semantic Correspondence

Reference 66

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This paper cites Learning to compose hypercolumns for visual correspon- dence.

Distillation of Diffusion Features for Semantic Correspondence Learning to compose hypercolumns for visual correspon- dence

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.118236Z

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Observation 4d935ffe-8d88-4229-9c24-1d19b09a54a8 · outbound

This paper cites Coordgan: Self-supervised dense correspondences emerge from gans.

Distillation of Diffusion Features for Semantic Correspondence Coordgan: Self-supervised dense correspondences emerge from gans

Reference 68

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Observation 1b5f4e4f-15ee-429a-ae31-304972c1cc8e · outbound

This paper cites Diffusion Models Beat GANs on Image Classification.

Distillation of Diffusion Features for Semantic Correspondence Diffusion Models Beat GANs on Image Classification

Reference 69

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Observation c384e28a-dfc7-4b2c-a56f-769cff374d20 · outbound

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

Distillation of Diffusion Features for Semantic Correspondence DINOv2: Learning Robust Visual Features without Supervision

Reference 70

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Observation 836d8776-e4ca-49bb-abf8-0241a6759eb9 · outbound

This paper cites FEED: Feature-level Ensemble for Knowledge Distillation.

Distillation of Diffusion Features for Semantic Correspondence FEED: Feature-level Ensemble for Knowledge Distillation

Reference 71

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Observation 3458e8d6-7e9f-42b6-ac59-262047e545f3 · outbound

This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

Distillation of Diffusion Features for Semantic Correspondence Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.092637Z

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This paper cites Con- volutional neural network architecture for geometric match- ing.

Distillation of Diffusion Features for Semantic Correspondence Con- volutional neural network architecture for geometric match- ing

Reference 73

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This paper cites Effi- cient neighbourhood consensus networks via submanifold sparse convolutions.

Distillation of Diffusion Features for Semantic Correspondence Effi- cient neighbourhood consensus networks via submanifold sparse convolutions

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.067015Z

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Distillation of Diffusion Features for Semantic Correspondence Neighbourhood consensus networks

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.053885Z

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Observation fae757bb-b2cc-4ed2-966d-7e06bbbe69c1 · outbound

This paper cites High-resolution im- age synthesis with latent diffusion models.

Distillation of Diffusion Features for Semantic Correspondence High-resolution im- age synthesis with latent diffusion models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.041179Z

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Observation 418170c8-e0bd-4d4e-9ccb-33afe50a6b19 · outbound

This paper cites Fit- nets: Hints for thin deep nets.

Distillation of Diffusion Features for Semantic Correspondence Fit- nets: Hints for thin deep nets

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.028502Z

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Observation 5ee73d86-d6d8-4a72-9675-5eeb27dc7845 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

Distillation of Diffusion Features for Semantic Correspondence Progressive distillation for fast sampling of diffusion models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.015809Z

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Observation 7e770fb1-be8a-46a2-b99c-155c6b47c2d3 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Distillation of Diffusion Features for Semantic Correspondence DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 79

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Observation 78f4ba49-9c17-4c52-9779-cafa20334bd7 · outbound

This paper cites Deep Model Compression: Distilling Knowledge from Noisy Teachers.

Distillation of Diffusion Features for Semantic Correspondence Deep Model Compression: Distilling Knowledge from Noisy Teachers

Reference 80

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This paper cites Monocular Depth Estimation using Diffusion Models.

Distillation of Diffusion Features for Semantic Correspondence Monocular Depth Estimation using Diffusion Models

Reference 81

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Observation e652ef8a-05ea-43f7-ad70-1a2775d1c74a · outbound

This paper cites Baumann, Vincent Tao Hu, and Bj ¨orn Ommer.

Distillation of Diffusion Features for Semantic Correspondence Baumann, Vincent Tao Hu, and Bj ¨orn Ommer

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:45.003108Z

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Observation f76fc88a-3cb9-44ed-82bc-c106e2341fdb · outbound

This paper cites MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model.

Distillation of Diffusion Features for Semantic Correspondence MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

Reference 83

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 84

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Observation 9d115557-9d5d-4f45-887d-8a9bed66a54a · outbound

This paper cites Dpodv2: Dense correspondence-based 6 dof pose estima- tion.

Distillation of Diffusion Features for Semantic Correspondence Dpodv2: Dense correspondence-based 6 dof pose estima- tion

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.978330Z

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Observation 7972dbd5-4949-4e89-938e-0dbb3f50413b · outbound

This paper cites Consistency models.

Distillation of Diffusion Features for Semantic Correspondence Consistency models

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.966079Z

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This paper cites Visual correspondence-based explanations improve AI ro- bustness and human-ai team accuracy.

Distillation of Diffusion Features for Semantic Correspondence Visual correspondence-based explanations improve AI ro- bustness and human-ai team accuracy

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.953874Z

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This paper cites Semantic Diffusion Network for Semantic Segmentation.

Distillation of Diffusion Features for Semantic Correspondence Semantic Diffusion Network for Semantic Segmentation

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:24:44.406861Z

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Observation 21f70711-19b5-4ce6-bcb8-cbfd5cadc773 · outbound

This paper cites DifFSS: Diffusion Model for Few-Shot Semantic Segmentation.

Distillation of Diffusion Features for Semantic Correspondence DifFSS: Diffusion Model for Few-Shot Semantic Segmentation

Reference 89

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This paper cites Emergent correspondence from image diffusion.

Distillation of Diffusion Features for Semantic Correspondence Emergent correspondence from image diffusion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.941677Z

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This paper cites Splicing vit features for semantic appearance trans- fer.

Distillation of Diffusion Features for Semantic Correspondence Splicing vit features for semantic appearance trans- fer

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.929363Z

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Observation d1d38909-e921-4629-bbd1-983dbc5c64a2 · outbound

This paper cites Plug-and-play diffusion features for text-driven image-to-image translation.

Distillation of Diffusion Features for Semantic Correspondence Plug-and-play diffusion features for text-driven image-to-image translation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.917027Z

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 93

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This paper cites Learning feature descriptors using cam- era pose supervision.

Distillation of Diffusion Features for Semantic Correspondence Learning feature descriptors using cam- era pose supervision

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.892289Z

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Observation 3ef7bf07-18c9-4dfd-837b-f9335929eca7 · outbound

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 95

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Distillation of Diffusion Features for Semantic Correspondence Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:24:44.866660Z

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Observation cea42e17-ab5a-4aff-ad23-5c55048b07a2 · outbound

This paper cites Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using dif- fusion models.

Distillation of Diffusion Features for Semantic Correspondence Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using dif- fusion models

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.853148Z

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Observation 6f43dddc-4244-4ef7-a87c-89915bd7d42f · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

Distillation of Diffusion Features for Semantic Correspondence Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.840648Z

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Observation 6cd1c0c8-da7d-4036-8d1c-fbcd0b8f09c1 · outbound

This paper cites A gift from knowledge distillation: Fast optimization, net- work minimization and transfer learning.

Distillation of Diffusion Features for Semantic Correspondence A gift from knowledge distillation: Fast optimization, net- work minimization and transfer learning

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.827472Z

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Observation 1332a207-15b6-43bf-9e9c-021354066a76 · outbound

This paper cites Paying more at- tention to attention: Improving the performance of convo- lutional neural networks via attention transfer.

Distillation of Diffusion Features for Semantic Correspondence Paying more at- tention to attention: Improving the performance of convo- lutional neural networks via attention transfer

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:44.812953Z

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