arXiv Computer Vision
ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition
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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
Oct 2
Subtoken Vision Transformer for Fine-grained Recognition
arXiv:2607.09086v2 Announce Type: replace Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfo...
arXiv Computer Vision
Aug 25
DiD It in 87 Minutes: A Label-Free Softmax-to-Linear Adaptation of Vision Transformers for Object Detection
arXiv:2608.22368v1 Announce Type: new Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the S...
arXiv Computer Vision
1d ago
ChanSFormer: A Channel Agnostic Vision Transformer for Multi-Channel Cell Painting Images
arXiv:2611.06015v1 Announce Type: new Abstract: High-content multi-channel imaging, from Cell Painting assays to remote sensing, is central to modern scientific pipelines. Because these channels are...
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.
arXiv Computer Vision
Sep 22
Rethinking Vision Architectures with Gated Linear Attention and KAN
arXiv:2609.22506v1 Announce Type: new Abstract: Vision Transformers allocate most parameters to multi-layer perceptrons (MLPs) for channel mixing, while token interactions usually rely on quadratic m...