Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning | Paper Citation Record

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This paper cites Improving language understanding by generative pre-training.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Improving language understanding by generative pre-training

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This paper cites Language models are unsupervised multitask learners.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Language models are unsupervised multitask learners

Reference 2

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This paper cites Language models are few-shot learners.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Language models are few-shot learners

Reference 3

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This paper cites Transformers in vision: A survey.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Transformers in vision: A survey

Reference 4

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This paper cites A survey of visual transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A survey of visual transformers

Reference 7

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This paper cites Stand-alone self-attention in vision models.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Stand-alone self-attention in vision models

Reference 8

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

Reference 9

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This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 10

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This paper cites A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking

Reference 12

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This paper cites Neural window fully-connected crfs for monocular depth estimation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Neural window fully-connected crfs for monocular depth estimation

Reference 13

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

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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This paper cites A survey on vision transformer.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A survey on vision transformer

Reference 16

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This paper cites Lstm: A search space odyssey.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Lstm: A search space odyssey

Reference 17

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This paper cites Learned token pruning for transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Learned token pruning for transformers

Reference 18

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This paper cites Baseg: Boundary aware semantic segmentation for autonomous driving.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Baseg: Boundary aware semantic segmentation for autonomous driving

Reference 19

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This paper cites SaiT: Sparse Vision Transformers through Adaptive Token Pruning.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning SaiT: Sparse Vision Transformers through Adaptive Token Pruning

Reference 20

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This paper cites Cross-image pixel contrasting for semantic segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Cross-image pixel contrasting for semantic segmentation

Reference 21

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This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 22

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This paper cites VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation

Reference 24

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This paper cites A survey on instance segmentation: state of the art.International journal of multimedia information retrieval, 9(3):171–189, 2020.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A survey on instance segmentation: state of the art.International journal of multimedia information retrieval, 9(3):171–189, 2020

Reference 25

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This paper cites Yolact: Real-time instance segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Yolact: Real-time instance segmentation

Reference 26

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This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 27

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This paper cites Decoupling foreground and background with siamese vit networks for weakly-supervised semantic segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Decoupling foreground and background with siamese vit networks for weakly-supervised semantic segmentation

Reference 29

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This paper cites Segvit v2: Exploring efficient and continual semantic segmentation with plain vision transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Segvit v2: Exploring efficient and continual semantic segmentation with plain vision transformers

Reference 31

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Observation 036c8e1f-c1ce-446d-a9cf-09f928e8a983 · outbound

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

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning SAM 2: Segment Anything in Images and Videos

Reference 32

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This paper cites Semantic layering in room segmentation via llms.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Semantic layering in room segmentation via llms

Reference 33

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This paper cites Post-training quantization or quantization-aware training? that is the question.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Post-training quantization or quantization-aware training? that is the question

Reference 34

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This paper cites Q-bert: Hessian based ultra low precision quantization of bert.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Q-bert: Hessian based ultra low precision quantization of bert

Reference 35

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This paper cites Hawq: Hessian aware quan- tization of neural networks with mixed-precision.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Hawq: Hessian aware quan- tization of neural networks with mixed-precision

Reference 36

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Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Post-training quantization for vision transformer

Reference 37

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Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Towards accurate post-training quantization for vision transformer

Reference 39

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This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A Simple and Effective Pruning Approach for Large Language Models

Reference 40

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This paper cites Prune and tune: Improving e fficient pruning 18 techniques for massive language models.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Prune and tune: Improving e fficient pruning 18 techniques for massive language models

Reference 41

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This paper cites Are sixteen heads really better than one? Advances in neural information processing systems, 32, 2019.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Are sixteen heads really better than one? Advances in neural information processing systems, 32, 2019

Reference 42

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This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Reducing Transformer Depth on Demand with Structured Dropout

Reference 43

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This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 44

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This paper cites Comprehensive Survey of Model Compression and Speed up for Vision Transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Comprehensive Survey of Model Compression and Speed up for Vision Transformers

Reference 45

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This paper cites Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 46

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This paper cites Adaptive token sampling for e fficient vision trans- formers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Adaptive token sampling for e fficient vision trans- formers

Reference 47

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This paper cites Spvit: Enabling faster vision transformers via latency-aware soft token pruning.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Spvit: Enabling faster vision transformers via latency-aware soft token pruning

Reference 48

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This paper cites Ia-red2: Interpretability-aware redundancy reduction for vision transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Ia-red2: Interpretability-aware redundancy reduction for vision transformers

Reference 50

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This paper cites Token Merging: Your ViT But Faster.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Token Merging: Your ViT But Faster

Reference 51

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This paper cites TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?

Reference 52

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This paper cites A-vit: Adaptive tokens for efficient vision transformer.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning A-vit: Adaptive tokens for efficient vision transformer

Reference 53

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Observation 5d73a1b2-4429-4038-91ae-a86bb499bc3a · outbound

This paper cites Dynamicvit: E fficient vision transformers with dynamic token sparsification.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Dynamicvit: E fficient vision transformers with dynamic token sparsification

Reference 54

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Observation 59d25d33-2032-4f73-bf5b-03ab6d8c57e9 · outbound

This paper cites K-means-type algorithms: A generalized convergence theorem and char- acterization of local optimality.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning K-means-type algorithms: A generalized convergence theorem and char- acterization of local optimality

Reference 55

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This paper cites Multilevel image thresholding based on 2d histogram and maximum tsallis entropy—a differential evolution approach.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Multilevel image thresholding based on 2d histogram and maximum tsallis entropy—a differential evolution approach

Reference 56

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This paper cites Edge detection using guided sobel image filtering.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Edge detection using guided sobel image filtering

Reference 57

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Observation 39d00b96-b803-4ac2-9f2a-8285d8128c4d · outbound

This paper cites Machine learning for aerial image labeling.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Machine learning for aerial image labeling

Reference 58

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Observation 9ed553bf-c275-4b10-ba1d-fe7f30bbadd3 · outbound

This paper cites What object should i use?-task driven object detection.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning What object should i use?-task driven object detection

Reference 59

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Observation 671f418b-1fa0-4fd4-bd6b-8b9290bc0513 · outbound

This paper cites Rio: A benchmark for reasoning intention-oriented objects in open environments.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Rio: A benchmark for reasoning intention-oriented objects in open environments

Reference 60

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Observation ba7333b1-869f-4d78-9d0f-32229de3f895 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 61

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This paper cites Segment anything.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Segment anything

Reference 62

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Observation a72f56f8-2cac-4c99-ad64-427b2ac16861 · outbound

This paper cites Content-aware token sharing for e fficient semantic segmen- tation with vision transformers.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Content-aware token sharing for e fficient semantic segmen- tation with vision transformers

Reference 63

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Observation 178b3dff-3a28-4b6f-9536-3da6ca9088ed · outbound

This paper cites Dynamic token pruning in plain vision transform- ers for semantic segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Dynamic token pruning in plain vision transform- ers for semantic segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:07.406473Z

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Observation 483f515e-558b-49c3-b007-f778338b9564 · outbound

This paper cites Revisiting token pruning 19 for object detection and instance segmentation.

Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Revisiting token pruning 19 for object detection and instance segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:07.392648Z

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