Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer
  for Fine-grained Visual Recognition   Learning subtle representation about object parts plays a vital role in
fine-grained visual recognition (FGVR) field. The vision transformer (ViT)
achieves promising results on computer vision due to its attention mechanism.
Nonetheless, with the fixed size of patches in ViT, the class token in deep
layer focuses on the global receptive field and cannot generate
multi-granularity features for FGVR. To capture region attention without box
annotations and compensate for ViT shortcomings in FGVR, we propose a novel
method named Adaptive attention multi-scale Fusion Transformer (AFTrans). The
Selective Attention Collection Module (SACM) in our approach leverages
attention weights in ViT and filters them adaptively to correspond with the
relative importance of input patches. The multiple scales (global and local)
pipeline is supervised by our weights sharing encoder and can be easily trained
end-to-end. Comprehensive experiments demonstrate that AFTrans can achieve SOTA
performance on three published fine-grained benchmarks: CUB-200-2011, Stanford
Dogs and iNat2017.

24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci