Paper Citation Record · LEDGER

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models

As of 23 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 2 inbound Pith citation observations for arXiv:2504.14032.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.14032 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:03:26.439718Z

measured 66 of 66 standing notices

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:21.060148Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T16:44:21.321080Z

Reference resolution

64 of 64 outbound references displayed

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External citation measurements

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Outbound references

Observation d0ffb65c-58df-4d85-bbc0-3b1021f6e9d5 · outbound

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Coco- stuff: Thing and stuff classes in context

Reference 1

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Observation 01f35811-148b-41cb-b54e-8ffb14b462e7 · outbound

This paper cites Learning continuous image representation with local implicit image function.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Learning continuous image representation with local implicit image function

Reference 2

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Observation 4e9c0e46-3301-4ed2-857b-ecc69915721d · outbound

This paper cites Schwing, Alexan- der Kirillov, and Rohit Girdhar.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 3

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Observation e56aec69-b1e2-4620-94ae-0e2809f1937a · outbound

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models The cityscapes dataset for semantic urban scene understanding

Reference 4

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Observation 022968e6-3d6d-46b1-82ef-2922d1d0438a · outbound

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Learning affinity- aware upsampling for deep image matting

Reference 5

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Observation ca019ac8-593c-4049-9663-0a2d7ce38b8d · outbound

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Observation 505ff348-5e1b-4845-8cb2-46dabc56123a · outbound

This paper cites Lanczos filtering in one and two dimen- sions.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Lanczos filtering in one and two dimen- sions

Reference 7

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Observation 5e150a97-d669-448f-b0c6-319e472ba37b · outbound

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models A guide to convolution arithmetic for deep learning

Reference 8

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This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Depth map prediction from a single image using a multi-scale deep net- work

Reference 9

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Observation db8a2fdb-888a-4d9f-8712-ba01dc5645e7 · outbound

This paper cites Prob- ing the 3d awareness of visual foundation models.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Prob- ing the 3d awareness of visual foundation models

Reference 10

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Observation bd70ed5a-d8d0-4be5-9f14-d16827fad094 · outbound

This paper cites Single image 3d without a single 3d image.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Single image 3d without a single 3d image

Reference 11

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Brandt, Axel Feld- mann, Zhoutong Zhang, and William T

Reference 12

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Observation c35f94f4-6859-442a-a8bc-92cd0f0b0cbc · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 13

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This paper cites Semantic contours from inverse detectors.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Semantic contours from inverse detectors

Reference 14

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Observation c6a9d133-f79a-4a4b-a8b6-46e09aa0bf5a · outbound

This paper cites Guided image fil- tering.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Guided image fil- tering

Reference 15

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Observation 6d06c215-6ed2-42f5-b217-da2748f066e8 · outbound

This paper cites Renovating names in open-vocabulary segmenta- tion benchmarks.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Renovating names in open-vocabulary segmenta- tion benchmarks

Reference 16

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This paper cites Space-time correspondence as a contrastive random walk.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Space-time correspondence as a contrastive random walk

Reference 17

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This paper cites NA VI: Category- agnostic image collections with high-quality 3d shape and pose annotations.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models NA VI: Category- agnostic image collections with high-quality 3d shape and pose annotations

Reference 18

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This paper cites Adam: A Method for Stochastic Optimization.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Adam: A Method for Stochastic Optimization

Reference 19

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This paper cites Pointrend: Image segmentation as rendering.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Pointrend: Image segmentation as rendering

Reference 20

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This paper cites Segment any- thing.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Segment any- thing

Reference 21

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Joint bilateral upsampling

Reference 22

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation

Reference 23

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This paper cites Exploring plain vision transformer backbones for object de- tection.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Exploring plain vision transformer backbones for object de- tection

Reference 24

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Vision transformer for nerf-based view synthesis from a single input image

Reference 25

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Microsoft coco: Common objects in context

Reference 26

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This paper cites Simpleclick: Interactive image segmentation with sim- ple vision transformers.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Simpleclick: Interactive image segmentation with sim- ple vision transformers

Reference 27

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Decoupled Weight Decay Regularization

Reference 28

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Index networks

Reference 29

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Fade: Fusing the assets of decoder and encoder for task-agnostic upsampling

Reference 30

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Sapa: Similarity-aware point affiliation for feature upsampling

Reference 31

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This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 32

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Cubic spline interpola- tion

Reference 33

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 34

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Learning deconvolution network for semantic segmentation

Reference 35

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models De- convolution and checkerboard artifacts

Reference 36

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Unresolved cited work

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This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models A benchmark dataset and evaluation methodology for video object segmentation

Reference 38

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models The 2017 DAVIS Challenge on Video Object Segmentation

Reference 39

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Three pillars improving vision foundation model distillation for lidar

Reference 40

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Learn- ing transferable visual models from natural language super- vision

Reference 41

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Vi- sion transformers for dense prediction

Reference 42

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Am-radio: Agglomerative vision foundation model reduce all domains into one

Reference 43

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Glamm: Pixel grounding large multimodal model

Reference 44

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models SAM 2: Segment Anything in Images and Videos

Reference 45

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Grounded sam: Assembling open-world models for diverse visual tasks,

Reference 46

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models U- net: Convolutional networks for biomedical image segmen- tation

Reference 47

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models ” grabcut” interactive foreground extraction using iterated graph cuts

Reference 48

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Is the deconvolution layer the same as a convolutional layer?

Reference 49

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Adaptis: Adaptive instance selection network

Reference 50

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This paper cites Re- viving iterative training with mask guidance for interactive segmentation.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Re- viving iterative training with mask guidance for interactive segmentation

Reference 51

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LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Lift: A surprisingly simple lightweight feature transform for dense vit descriptors

Reference 52

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This paper cites Splatter image: Ultra-fast single-view 3d recon- struction.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Splatter image: Ultra-fast single-view 3d recon- struction

Reference 53

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This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 55

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This paper cites Dino-tracker: Taming dino for self-supervised point tracking in a single video.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Dino-tracker: Taming dino for self-supervised point tracking in a single video

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.972172Z

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This paper cites Carafe: Content-aware reassembly of fea- tures.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Carafe: Content-aware reassembly of fea- tures

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.736781Z

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Observation 05ad44e8-54c2-43d7-a1c5-6ca5e87281bf · outbound

This paper cites Self-supervised trans- formers for unsupervised object discovery using normalized cut.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Self-supervised trans- formers for unsupervised object discovery using normalized cut

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.719209Z

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation

Reference 59

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This paper cites Featuren- erf: Learning generalizable nerfs by distilling foundation models.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Featuren- erf: Learning generalizable nerfs by distilling foundation models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.689662Z

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This paper cites pixelnerf: Neural radiance fields from one or few images.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models pixelnerf: Neural radiance fields from one or few images

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.670225Z

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

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip

Reference 62

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This paper cites Sigmoid loss for language image pre-training.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models Sigmoid loss for language image pre-training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.641626Z

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This paper cites LoftUp: earning a Coordinate-Based Feature Upsampler for Vi- sion Foundation Models.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models LoftUp: earning a Coordinate-Based Feature Upsampler for Vi- sion Foundation Models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:26.622513Z

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This paper cites 1, 2, 3, 4, 6, 7, 12.

LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models 1, 2, 3, 4, 6, 7, 12

Reference 128

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:03:27.163802Z

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Pith citing papers

Observation 2a3c4294-09b6-4365-b009-02afd379bed9 · inbound

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation cites this paper.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models

Reference 33

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Observation 7fad0f2a-d470-45f4-aa9d-30d5c9227b70 · inbound

UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders cites this paper.

UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models

Reference 18

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