Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing | Paper Citation Record

Observation 5bbf912a-01ae-4e9d-bd57-95fc243458a6 · outbound

This paper cites Hiervl: Learning hierarchical video- language embeddings.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Hiervl: Learning hierarchical video- language embeddings

Reference 1

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Observation 9949af15-89e3-4f4a-b3af-6d505aaabfab · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing BEiT: BERT Pre-Training of Image Transformers

Reference 2

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Observation f3c30f5f-6149-4898-bfc4-b0c156b7a00d · outbound

This paper cites Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words

Reference 3

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This paper cites Hypermae: Modulating implicit neural representations for mae training.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Hypermae: Modulating implicit neural representations for mae training

Reference 4

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This paper cites Prefix sums and their applications.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Prefix sums and their applications

Reference 5

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Observation 54ff10f1-55c4-4078-84ea-6e34e694eb1f · outbound

This paper cites Token Merging: Your ViT But Faster.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Token Merging: Your ViT But Faster

Reference 6

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This paper cites High-performance large-scale image recognition without normalization.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing High-performance large-scale image recognition without normalization

Reference 7

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Observation 1d1a7761-c3ff-4c41-8174-c5885e3f8f46 · outbound

This paper cites Jump cell painting dataset: morphological im- pact of 136,000 chemical and genetic perturbations.BioRxiv, pages 2023–03, 2023.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Jump cell painting dataset: morphological im- pact of 136,000 chemical and genetic perturbations.BioRxiv, pages 2023–03, 2023

Reference 8

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Observation 12b4d707-183f-45f4-8c74-18a95e8010e6 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Towards a general-purpose foundation model for computational pathology

Reference 9

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Observation f2d18df4-8a15-4d62-8759-a14a1e8fac3f · outbound

This paper cites Openmmlab semantic seg- mentation toolbox and benchmark, 2020.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Openmmlab semantic seg- mentation toolbox and benchmark, 2020

Reference 10

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Observation 3d490cf1-c8f7-47eb-90a5-b3c89bde4360 · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 11

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Observation d0efb87a-c2bb-4a91-998d-2266d105f783 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 12

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Observation 172ea684-c459-409f-b2d5-d3328d2b95e3 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 13

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Observation e7b47e4f-9d2c-4748-959a-22054eaad46b · outbound

This paper cites Flashattention: Fast and memory-efficient exact at- tention with io-awareness.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Flashattention: Fast and memory-efficient exact at- tention with io-awareness

Reference 14

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Observation 48660da0-bd72-47a3-84cb-2b8a395f45e5 · outbound

This paper cites Vpn++: Rethinking video-pose embeddings for understand- ing activities of daily living.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Vpn++: Rethinking video-pose embeddings for understand- ing activities of daily living

Reference 15

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Observation 17fe9fe6-b38e-44e3-80d9-36814e67a872 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Imagenet: A large-scale hierarchical image database

Reference 16

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

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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Observation 9c06baab-2d78-4e7f-bd16-95b9cdfbed3c · outbound

This paper cites Learned representation-guided diffusion models for large-image generation.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Learned representation-guided diffusion models for large-image generation

Reference 18

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Observation b063ed24-cb3b-4042-bbb5-adc8cbbbc8de · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 19

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Observation 92f2adb6-0584-4bc7-992e-f56b1a030687 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Efficiently Modeling Long Sequences with Structured State Spaces

Reference 20

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This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 21

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This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 22

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This paper cites Mask r-cnn.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Mask r-cnn

Reference 23

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This paper cites Masked autoencoders are scalable vision learners.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Masked autoencoders are scalable vision learners

Reference 24

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Observation dc1375a1-1019-4157-b9d7-7d69177970f7 · outbound

This paper cites Token Dropping for Efficient BERT Pretraining.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Token Dropping for Efficient BERT Pretraining

Reference 25

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This paper cites Deep networks with stochastic depth.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Deep networks with stochastic depth

Reference 26

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This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 27

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This paper cites Attention-based deep multiple instance learning.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Attention-based deep multiple instance learning

Reference 28

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This paper cites Object- centric diffusion for efficient video editing.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Object- centric diffusion for efficient video editing

Reference 29

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This paper cites Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology

Reference 30

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This paper cites Vision transformers inference acceleration based on adaptive layer normalization.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Vision transformers inference acceleration based on adaptive layer normalization

Reference 31

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This paper cites ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

Reference 32

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Videomamba: State space model for efficient video understanding

Reference 33

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

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Exploring plain vision transformer backbones for object de- tection

Reference 34

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

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 35

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This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Jamba: A Hybrid Transformer-Mamba Language Model

Reference 36

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This paper cites Microsoft coco: Common objects in context.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Microsoft coco: Common objects in context

Reference 37

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This paper cites MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining

Reference 38

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This paper cites Vmamba: Visual state space model, 2024.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Vmamba: Visual state space model, 2024

Reference 39

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Swin transformer: Hierarchical vision transformer using shifted windows

Reference 40

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing A convnet for the 2020s

Reference 41

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This paper cites Decoupled Weight Decay Regularization.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Decoupled Weight Decay Regularization

Reference 42

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 43

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This paper cites Vim4path: Self-supervised vi- sion mamba for histopathology images.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Vim4path: Self-supervised vi- sion mamba for histopathology images

Reference 44

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This paper cites An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels

Reference 45

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

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing DINOv2: Learning Robust Visual Features without Supervision

Reference 46

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This paper cites EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

Reference 47

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This paper cites Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers

Reference 48

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This paper cites Per- ceptual grouping in contrastive vision-language models.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Per- ceptual grouping in contrastive vision-language models

Reference 49

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 50

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Autoregressive Pretraining with Mamba in Vision

Reference 52

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This paper cites Learning to Merge Tokens in Vision Transformers.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Learning to Merge Tokens in Vision Transformers

Reference 53

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

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?

Reference 54

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This paper cites GroupMamba: Efficient Group-Based Visual State Space Model.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing GroupMamba: Efficient Group-Based Visual State Space Model

Reference 55

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This paper cites Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion

Reference 56

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Observation ea069a7f-a15e-43ce-abfb-7f3d17f8f959 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Simplified State Space Layers for Sequence Modeling

Reference 57

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This paper cites Training data-efficient image transformers & distillation through at- tention.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Training data-efficient image transformers & distillation through at- tention

Reference 58

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Observation f0c6ea6a-bdb4-423d-a2db-e4c906d18f08 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Training data-efficient image transformers & distillation through at- tention

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.552685Z

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Observation 991bd086-c06d-4792-8654-b728adc15798 · outbound

This paper cites Attention is all you need.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Attention is all you need

Reference 60

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Observation 0ad339ca-3122-4447-8af3-47c93253a370 · outbound

This paper cites Mamba-R: Vision Mamba ALSO Needs Registers.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Mamba-R: Vision Mamba ALSO Needs Registers

Reference 61

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Observation 2875a687-c8b4-475a-a1d0-892dae9ef6b2 · outbound

This paper cites Unified perceptual parsing for scene understand- ing.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unified perceptual parsing for scene understand- ing

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T18:28:48.489438Z

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Observation 5e042235-bfed-423c-8b9e-1ff59bf6d8c8 · outbound

This paper cites A whole-slide foundation model for digital pathology from real-world data.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing A whole-slide foundation model for digital pathology from real-world data

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.523234Z

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Observation 097394c1-05c2-46b5-b550-ebb2bcf64845 · outbound

This paper cites PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Reference 64

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Observation d3a3a8bc-6e17-443d-9c8a-d14953886b67 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.510913Z

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Observation db50be05-09c9-4811-abbe-72743fcfd13d · outbound

This paper cites Exploring Token Pruning in Vision State Space Models.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Exploring Token Pruning in Vision State Space Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T18:28:48.549618Z

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Observation 2a9c3ef6-1368-4ee3-a3e7-26beef0231d8 · outbound

This paper cites Rethinking Token Reduction for State Space Models.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Rethinking Token Reduction for State Space Models

Reference 67

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Observation c4ab4d19-b994-4c85-914d-a0b7f7315ca9 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing mixup: Beyond Empirical Risk Minimization

Reference 68

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Observation 96bd558b-6d88-40ff-8ea6-66a3befa02c5 · outbound

This paper cites Scene parsing through ade20k dataset.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Scene parsing through ade20k dataset

Reference 69

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Observation 884c7d76-4d70-46bb-a65f-9ada91e8cea6 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 70

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Observation 72815423-f091-4727-9707-331e6092ac0c · outbound

This paper cites We closely followed the pre- training (Table 9), fine-tuning (Table 10), and linear- probing (Table 11) settings from the Masked Autoen- coders [24] codebase.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing We closely followed the pre- training (Table 9), fine-tuning (Table 10), and linear- probing (Table 11) settings from the Masked Autoen- coders [24] codebase

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.488891Z

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Observation c1b391d4-647e-48ef-952c-b126e0fae6c3 · outbound

This paper cites 2) We applied a scaling factor of 1 − mask ratio (75% masking by default) during fine-tuning and linear probing when pooling tokens before the SSM block.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing 2) We applied a scaling factor of 1 − mask ratio (75% masking by default) during fine-tuning and linear probing when pooling tokens before the SSM block

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.476026Z

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Observation 8f2b76bc-12f2-4a91-9726-bed24d7e4d83 · outbound

This paper cites mean pool in Fast- MaskVim.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing mean pool in Fast- MaskVim

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.462686Z

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Observation 3677531d-e374-4dd1-a2af-83dfa366a714 · outbound

This paper cites In Table 13, we compare the performance of fine-tuning pre-trained FastMaskVim using alternate layer learning rate decay instead of per-layer decay as in the MAE codebase.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 13, we compare the performance of fine-tuning pre-trained FastMaskVim using alternate layer learning rate decay instead of per-layer decay as in the MAE codebase

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.449688Z

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Observation cad2c0ed-963b-4fdd-a011-b288a5b87dea · outbound

This paper cites Apply- ing the scaling factor results in an improvement of 0.3% compared to the default mean pooling in fine-tuning without multiplying by the scaling factor.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Apply- ing the scaling factor results in an improvement of 0.3% compared to the default mean pooling in fine-tuning without multiplying by the scaling factor

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.436446Z

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Observation d008e347-c57b-4761-90cf-014178075cef · outbound

This paper cites In Table 15, we compare the linear probing performance of Fast- MaskVim with and without the scaling factor (0.25).

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 15, we compare the linear probing performance of Fast- MaskVim with and without the scaling factor (0.25)

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.423402Z

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Observation f5ae686b-e2e5-4250-86f0-02299296d374 · outbound

This paper cites an unresolved cited work.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 77

Resolution: parse uncertain; raw_fallback, observed 2026-08-09T18:28:49.410087Z

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Observation 0066557f-f833-4bb4-aa90-bd5f325eafc7 · outbound

This paper cites In Table 16, we compare the performance of FastVim-S with a class token versus without a class token (default).

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 16, we compare the performance of FastVim-S with a class token versus without a class token (default)

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.396909Z

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Observation 39f5c2a8-6f78-4cfd-8eca-46fb7e5670d3 · outbound

This paper cites In Table 17, we empir- ically demonstrate the performance of FastVim trained with different combinations of input normalization and post-SSM normalization.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 17, we empir- ically demonstrate the performance of FastVim trained with different combinations of input normalization and post-SSM normalization

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.383837Z

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Observation ea6d773f-6309-4c77-a392-188473668fe5 · outbound

This paper cites In Table 18, we explore whether in Fig.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 18, we explore whether in Fig

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.371044Z

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Observation f578d65f-0c8d-4930-a71f-173773202692 · outbound

This paper cites We followed the implementation details primarily from ChannelViT [3].

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing We followed the implementation details primarily from ChannelViT [3]

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.358146Z

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Observation 3f58022d-ad0b-4c3c-83e0-9e36a20bc279 · outbound

This paper cites Channel- First with and without sorted HCS.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Channel- First with and without sorted HCS

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.346395Z

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Observation c8d43edc-e6aa-41b9-9d7a-bf12f540d892 · outbound

This paper cites We then explore the effect of different pooling methods, such as max pooling [49] and attention pooling [28], as detailed in Table 20 on the JUMP-CP dataset.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing We then explore the effect of different pooling methods, such as max pooling [49] and attention pooling [28], as detailed in Table 20 on the JUMP-CP dataset

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.334222Z

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Observation dad8146f-16ab-4f56-bb46-027a6f2d510c · outbound

This paper cites Now, we preliminarily explore pooling along two dimen- Table 20.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Now, we preliminarily explore pooling along two dimen- Table 20

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.322383Z

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Observation bee2db5a-7fec-4ea4-a149-9b62938f443a · outbound

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Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:28:49.309789Z

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Observation 8d1121a7-1742-4580-adb0-a6c5775f1b32 · outbound

This paper cites an unresolved cited work.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:28:49.296577Z

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Observation 90da51ca-3337-4716-b396-7567125093c1 · outbound

This paper cites an unresolved cited work.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:28:49.283163Z

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Observation cc8fc2a1-58e6-4821-8667-8a2fd380a361 · outbound

This paper cites an unresolved cited work.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:28:49.269793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b675a518-fb63-429c-a27c-fe1de2209a42 · outbound

This paper cites an unresolved cited work.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:28:49.256152Z

Source-reported events for the cited work

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Observation 68b26e95-cc81-4cbd-b083-05aa12fc2641 · outbound

This paper cites In Table 22, we demonstrate the throughput improvement in FastChannelVim compared to ChannelVim.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing In Table 22, we demonstrate the throughput improvement in FastChannelVim compared to ChannelVim

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.242420Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 505fef0c-ccf5-4083-b4f1-83bf717a8875 · outbound

This paper cites Here, we calcu- late the processing time for Forward SSM + Backward SSM in only one block (see Fig.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Here, we calcu- late the processing time for Forward SSM + Backward SSM in only one block (see Fig

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.227388Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b83603b0-749f-4159-8994-013e7e0e667d · outbound

This paper cites We employed the AdamW optimizer with a weight decay of 0.01.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing We employed the AdamW optimizer with a weight decay of 0.01

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.213392Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 23b235ad-6ca2-48f2-bd7d-6068e50fbb1c · outbound

This paper cites We employed the AdamW optimizer with a weight decay of 0.05, with a total batch size of 64.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing We employed the AdamW optimizer with a weight decay of 0.05, with a total batch size of 64

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.199418Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9e16d200-e858-4ce7-9c97-469ad51776c9 · outbound

This paper cites 2), we apply mean pooling to the tokens before performing the SSM scan.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing 2), we apply mean pooling to the tokens before performing the SSM scan

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.185072Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 63e740a5-14e1-4335-82eb-dd8fe6848f19 · outbound

This paper cites Model configurations for FastVim Model Layers Embedding dim.

Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing Model configurations for FastVim Model Layers Embedding dim

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:28:49.170789Z

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