arXiv AI

ACSAN: All-Convolutional Self-Attention Network Integrating Convolutional Operations with Vision Transformer

The paper introduces ACSAN, an All‑Convolutional Self‑Attention Network that replaces the fully‑connected matrix multiplications of Vision Transformers with convolution operations. Its core module, Dynamic Convolutional Self‑Attention (DCSA), uses multi‑scale dilated convolutions to produce Query, Key, and Value tensors, and combines spatial and channel self‑attention into a single output. Experiments on CIFAR‑10, CIFAR‑100, and ImageNet‑1k demonstrate that ACSAN attains competitive or superior classification accuracy to ViT while using comparable or fewer parameters, and ablation studies confirm the positive contribution of each component.

arXiv Computer Vision

5d ago

SAGE: Saliency-Annealed Gated Encoder for Universal Vision Recognition

SAGE (Saliency‑Annealed Gated Encoder) is a Vision Transformer‑based backbone that explicitly leverages saliency, global context, and high‑frequency details through a saliency‑guided architecture, an annealing training paradigm, and a two‑stage loss transition. It achieves state‑of‑the‑art or competitive results on a wide range of dense prediction tasks, including COCO object detection and segmentation, ADE20K segmentation, TinyImageNet classification, and DIOR‑R oriented detection, all using the same backbone with task‑specific decoders. The framework is notable for being the first to deliver multi‑task SOTA performance on Huawei Ascend NPUs.

By Le Yang, Yucheng Huang, Junyu Chen, Qi Dang, Xin Kong, Haifeng Huo, Qiang Zhao

llms computer-vision benchmarks

Hugging Face Trending Papers

Aug 11

Grid-Preserving Knowledge Distillation: Transferring Convolutional Inductive Bias to Vision Transformers under Data Scarcity

Vision Transformers underperform convolutional networks when training data is scarce, and distilling convolutional inductive biases from a CNN teacher is an effective remedy that leaves the deployed model unchanged. General-purpose feature distillation, however, transfers little in this setting.

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arXiv Computer Vision

Sep 16

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

arXiv:2608.06205v2 Announce Type: replace Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...

By Nima Hatami, Karim Faez, Saeed Sharifian, Hamidreza Amindavar

computer-vision

arXiv Computer Vision

Sep 22

Efficient Concertormer for Image Deblurring and Beyond

arXiv:2404.06135v4 Announce Type: replace Abstract: The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. Ho...

By Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, Ming-Hsuan Yang

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arXiv Computer Vision

Oct 9

MCL: Meta Convolution Layer

The paper introduces the Meta Convolution Layer (MCL), a dynamic convolution approach that models kernels as input‑conditioned high‑order polynomial functions instead of linear mixtures of basis kernels. MCL generates a single adaptive kernel through a structured polynomial meta‑network, improving training stability and expressivity. Experiments show significant accuracy gains on ImageNet, CIFAR, and fine‑grained classification tasks when MCL is added to ResNet, Wide‑ResNet, Swin, and ViT backbones.

By Naim Reza, Md Al Amin, Ho Yub Jung

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arXiv AI

Jun 9

RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT

arXiv:2606. 08156v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity.

By Kyumin Choi, Ikbeom Jang

llms efficiency

arXiv Machine Learning

Jun 5

Exact Linear Attention

arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.

By Weinuo Ou

llms computer-vision reinforcement-learning efficiency safety

arXiv Machine Learning

Aug 19

Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket

Spikformer V2 introduces a Spiking Self‑Attention (SSA) mechanism that removes softmax and uses spike‑based Query, Key, and Value to capture sparse visual features efficiently. It also adds a Spiking Convolutional Stem (SCS) and employs self‑supervised learning (masking and reconstruction) to pre‑train the model before fine‑tuning on ImageNet. The result is the first spiking neural network to surpass 80 % accuracy on ImageNet, achieving 81.10 % with a 172 M‑parameter, 16‑layer model in just one time step.

By Zhaokun Zhou, Yijie Lu, Kaiwei Che, Wei Fang, Keyu Tian, Qihao Peng, Yuesheng Zhu, Shuicheng Yan, Yonghong Tian, Li Yuan

llms computer-vision

arXiv Computer Vision

Sep 1

Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention

arXiv:2505.16157v3 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...

By Yuang Ai

llms diffusion computer-vision benchmarks

arXiv Machine Learning

Aug 4

Hierarchical Pre-Training of Vision Encoders with Large Language Model

arXiv:2604. 00086v2 Announce Type: replace-cross Abstract: The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks.

By Eugene Lee, Ting-Yu Chang, Jui-Huang Tsai, Jiajie Diao, Chen-Yi Lee

llms rag computer-vision multimodal benchmarks safety

arXiv Computer Vision

Sep 1

ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition

arXiv:2608.30183v1 Announce Type: cross Abstract: Lightweight channel attention mechanisms are widely used in image classification, yet their effectiveness in fine-grained visual recognition (FGVR) r...

By Yu-Sheng Liu, Yu-Chen Tung

computer-vision benchmarks

arXiv Computer Vision

Sep 4

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

The paper presents a two‑pipeline framework for retinal fundus analysis that combines four‑class disease classification with vessel segmentation. It fine‑tunes eight ImageNet‑pretrained CNNs on the FIVES dataset, applies five gradient‑based explanation methods to assess model interpretability, and benchmarks ten U‑Net variants—including transformer‑based and attention‑enhanced architectures—on the FIVES and DRIVE datasets. The best classification results come from ResNet101 (94.17% accuracy), while the strongest segmentation performance is achieved by Attention U‑Net with a ResNet101V2 backbone, improving DRIVE IoU from 60.80% to 64.83%.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah

llms computer-vision fine-tuning

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