Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation | Paper Citation Record

Observation a18391a9-f22c-473a-a2b8-6b730dc2e87a · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Understanding intermediate layers using linear classifier probes

Reference 1

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 2

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Observation 167278ec-c250-4ce2-aa64-0d2bcba7f776 · outbound

This paper cites Can Visual Foundation Models Achieve Long-term Point Tracking?.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Can Visual Foundation Models Achieve Long-term Point Tracking?

Reference 3

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This paper cites Guibas, Justin Johnson, and Varun Jampani.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Guibas, Justin Johnson, and Varun Jampani

Reference 4

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This paper cites Large- scale interactive object segmentation with human annotators.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Large- scale interactive object segmentation with human annotators

Reference 5

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Observation 692d1212-32d6-436f-8b01-af085d2643c7 · outbound

This paper cites Better plain ViT baselines for ImageNet-1k.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Better plain ViT baselines for ImageNet-1k

Reference 6

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Observation 8fab37f9-27e9-4c6b-badd-fc26b1f93b03 · outbound

This paper cites Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images

Reference 7

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This paper cites An experimental comparison of min-cut/max-flow algorithms for energy min- imization in vision.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation An experimental comparison of min-cut/max-flow algorithms for energy min- imization in vision

Reference 8

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Observation 076272ef-98b9-49a0-ab2f-176372a933bf · outbound

This paper cites Ledits++: Limitless image editing us- ing text-to-image models.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Ledits++: Limitless image editing us- ing text-to-image models

Reference 9

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Observation dcea4419-e5eb-47f5-8e77-3169fdbe466b · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 10

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Observation b5c697d1-1ac9-4d83-a5d3-5bc8e6a157d4 · outbound

This paper cites Focalclick: Towards practical interactive image segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Focalclick: Towards practical interactive image segmentation

Reference 11

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Observation f38a78c7-abfc-4caf-ae7e-0d8b95b49b9b · outbound

This paper cites Feat2gs: Probing visual foundation models with gaussian splatting.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Feat2gs: Probing visual foundation models with gaussian splatting

Reference 12

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Observation 9bc044f0-7ea8-42df-8381-ef3d531ec272 · outbound

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

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Diffedit: Diffusion-based semantic image editing with mask guidance

Reference 13

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Observation 7a4dca7e-e283-4196-b80c-5ff2abf63a76 · outbound

This paper cites Surgical-dino: adapter learning of foundation models for depth estimation in endoscopic surgery.International Journal of Computer Assisted Radiology and Surgery, 19:1013 – 1020,.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Surgical-dino: adapter learning of foundation models for depth estimation in endoscopic surgery.International Journal of Computer Assisted Radiology and Surgery, 19:1013 – 1020,

Reference 14

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Observation a19ae8fa-938a-4ba5-8f80-a5fc1d9cb0dd · outbound

This paper cites Learning affinity- aware upsampling for deep image matting.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Learning affinity- aware upsampling for deep image matting

Reference 15

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Observation 54af8306-9e4c-40fb-acd0-c77708a7e311 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

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Observation 3fcc8e40-05cd-4f83-bb63-ffc4c43168c1 · outbound

This paper cites A guide to convolution arithmetic for deep learning.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation A guide to convolution arithmetic for deep learning

Reference 17

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This paper cites Brandt, Axel Feld- mann, Zhoutong Zhang, and William T.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Brandt, Axel Feld- mann, Zhoutong Zhang, and William T

Reference 18

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Observation 10a0c1ff-5bbf-42cc-9543-570a0edaab4b · outbound

This paper cites Bros- tow.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Bros- tow

Reference 19

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 20

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Observation 5f5f93ac-dfab-47a7-a700-8fa135f4e339 · outbound

This paper cites Geodesic star convexity for interactive image segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Geodesic star convexity for interactive image segmentation

Reference 21

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Observation 940cacf9-a669-4fdb-82b8-071f399969c7 · outbound

This paper cites Bourdev, Subhransu Maji, and Jitendra Malik.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Bourdev, Subhransu Maji, and Jitendra Malik

Reference 22

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

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 23

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This paper cites Learning implicit feature alignment function for semantic segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Learning implicit feature alignment function for semantic segmentation

Reference 24

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This paper cites Renovating names in open-vocabulary segmentation benchmarks.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Renovating names in open-vocabulary segmentation benchmarks

Reference 25

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This paper cites Loftup: Learning a coordinate-based feature upsampler for vision foundation models, 2025.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Loftup: Learning a coordinate-based feature upsampler for vision foundation models, 2025

Reference 26

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Observation c64e7e78-9741-404f-b5d3-5bf4646b0678 · outbound

This paper cites Gir- shick.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Gir- shick

Reference 27

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This paper cites Segment Anything.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Segment Anything

Reference 28

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This paper cites User-centric learning and evaluation of interactive segmentation systems.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation User-centric learning and evaluation of interactive segmentation systems

Reference 29

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This paper cites Cohen, Dani Lischinski, and Matthew Uyttendaele.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Cohen, Dani Lischinski, and Matthew Uyttendaele

Reference 30

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This paper cites Controlnet++: Improv- ing conditional controls with efficient consistency feedback.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Controlnet++: Improv- ing conditional controls with efficient consistency feedback

Reference 31

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This paper cites Girshick, and Kaiming He.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Girshick, and Kaiming He

Reference 32

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This paper cites Interactive image segmentation with latent diversity.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Interactive image segmentation with latent diversity

Reference 33

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This paper cites Interactive image segmentation with first click attention.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Interactive image segmentation with first click attention

Reference 34

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This paper cites Focuscut: Diving into a focus view in interactive segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Focuscut: Diving into a focus view in interactive segmentation

Reference 35

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This paper cites iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

Reference 36

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This paper cites Simpleclick: Interactive image segmentation with simple vi- sion transformers.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Simpleclick: Interactive image segmentation with simple vi- sion transformers

Reference 37

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

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 38

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This paper cites Index networks.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Index networks

Reference 39

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Observation 367d3cdc-f0d7-4741-98ff-3ae612565677 · outbound

This paper cites FADE: fusing the assets of decoder and encoder for task-agnostic upsampling.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation FADE: fusing the assets of decoder and encoder for task-agnostic upsampling

Reference 40

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Observation ca3fc26c-f9cc-4d5d-b08c-4aaf563c3dad · outbound

This paper cites SAPA: similarity-aware point affiliation for feature upsampling.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation SAPA: similarity-aware point affiliation for feature upsampling

Reference 41

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Observation 0e6201fc-0903-4205-bac2-c0c4e83aee29 · outbound

This paper cites Deep interactive segmentation of medical images: A systematic review and taxonomy.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Deep interactive segmentation of medical images: A systematic review and taxonomy

Reference 42

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This paper cites Martin, Charless C.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Martin, Charless C

Reference 43

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Observation 35fde2f9-3380-4930-83d4-0b625246f67f · outbound

This paper cites Learn- ing deconvolution network for semantic segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Learn- ing deconvolution network for semantic segmentation

Reference 44

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Observation 95d068de-dd91-4d82-a9e4-38e7d536e8ed · outbound

This paper cites Decon- volution and checkerboard artifacts.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Decon- volution and checkerboard artifacts

Reference 45

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Observation 0b0e52a4-17f9-4def-ad4d-e60a2d03939d · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 46

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Observation 64ef1e89-b7b5-4608-b3e7-ab1dcba3926f · outbound

This paper cites Taglab: Ai-assisted annotation for the fast and accurate semantic segmentation of coral reef 9 orthoimages.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Taglab: Ai-assisted annotation for the fast and accurate semantic segmentation of coral reef 9 orthoimages

Reference 47

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Observation a7f667b8-7f95-401f-98df-4b231e3118ab · outbound

This paper cites Gross, and Alexander Sorkine- Hornung.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Gross, and Alexander Sorkine- Hornung

Reference 48

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Observation 64e1145f-2b9e-41cf-bfe9-1f26471ffe4e · outbound

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

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Learning transferable visual models from natural language supervision

Reference 49

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Observation 3c25fe50-3140-41eb-b392-5f937a67567e · outbound

This paper cites Using goms and nasa-tlx to evaluate human–computer inter- action process in interactive segmentation.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Using goms and nasa-tlx to evaluate human–computer inter- action process in interactive segmentation

Reference 50

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Observation 5548f322-4d6d-43ee-bc56-afe3a16e1710 · outbound

This paper cites Am-radio: Agglomerative vision foundation model reduce all domains into one.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Am-radio: Agglomerative vision foundation model reduce all domains into one

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.537249Z

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Observation f10efcb2-8004-45e6-bcc2-496f92c5582c · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation SAM 2: Segment Anything in Images and Videos

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T01:07:19.068846Z

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This paper cites High-resolution image syn- thesis with latent diffusion models.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation High-resolution image syn- thesis with latent diffusion models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.524882Z

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation ”grabcut”: interactive foreground extraction using iterated graph cuts

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.513495Z

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This paper cites Is the deconvolution layer the same as a convolutional layer?.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Is the deconvolution layer the same as a convolutional layer?

Reference 55

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Petrov, and Anton Konushin

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.501903Z

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Lift: A surprisingly simple lightweight feature transform for dense vit descriptors

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.489003Z

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Observation 0f882ab3-8fc9-420d-906f-15e52740acd8 · outbound

This paper cites Winoground: Probing vision and language models for visio- linguistic compositionality.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Winoground: Probing vision and language models for visio- linguistic compositionality

Reference 58

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 59

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Observation 8eff323b-2c71-4a5b-9777-dd9704846038 · outbound

This paper cites Stewart, Zhitong Xiong, Xiao Xiang Zhu, Stefan Bauer, and John Chuang.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Stewart, Zhitong Xiong, Xiao Xiang Zhu, Stefan Bauer, and John Chuang

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.457486Z

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Observation 557e4afb-476b-4b58-8c9e-d4c2625afac1 · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation CARAFE: content-aware reassembly of features

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.445027Z

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Observation 7e38b290-f07c-421b-bd56-0152bc9a04e7 · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Fouhey, and Abhinav Gupta

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.433712Z

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Observation 6e7b4e1f-f503-4ecb-ae71-32e3be75deaa · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation ´Alvarez, and Ping Luo

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.421317Z

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Observation 1c25b2a4-1497-4a78-88bc-f587a8d837b7 · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Price, Scott Cohen, Jimei Yang, and Thomas S

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.410434Z

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Observation 2baa1592-7182-4b77-9804-55cded988563 · outbound

This paper cites Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional CLIP.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional CLIP

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.399076Z

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Observation 97db39c8-9fc8-4a62-9859-503ee6bcfe2f · outbound

This paper cites Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T01:07:19.125148Z

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This paper cites Adding conditional control to text-to-image diffusion models.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Adding conditional control to text-to-image diffusion models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T01:07:19.128696Z

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Observation 169ae5c8-54a2-4c68-ad4f-71a59dc0c3ca · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Graco: Granularity-controllable interactive segmentation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.373410Z

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Observation 10ea6ed7-0cc5-4721-8b6a-1292f41ea5ad · outbound

This paper cites The 1 14 resolution is derived directly through a convolutional layer, while the 1 28 resolution is obtained by applying a 2× 2 max pooling operation prior to convolution.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation The 1 14 resolution is derived directly through a convolutional layer, while the 1 28 resolution is obtained by applying a 2× 2 max pooling operation prior to convolution

Reference 72

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T01:07:19.362771Z

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Observation 174fe7de-4292-49db-9a03-7840d06c8881 · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 112

Resolution: unresolved; raw_fallback, observed 2026-08-16T01:07:19.984251Z

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Observation 94d77904-e524-4371-bd4d-55dc999caa99 · outbound

This paper cites 1, 3, 4, 5.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation 1, 3, 4, 5

Reference 128

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:07:19.477469Z

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Observation 4d88be2f-af29-4042-bdd9-ca103f38d3f5 · outbound

This paper cites Finally, a classifi- cation layer is applied.

Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Finally, a classifi- cation layer is applied

Reference 256

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T01:07:19.351427Z

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Observation f46cce8d-e1a4-4b95-9386-cfb67e044f47 · outbound

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Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation Unresolved cited work

Reference 585

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Source-reported events for the cited work

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