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[2210.17115v1] ViT-LSLA: Vision Transformer with Light Self-Limited-Attention

a hierarchical Vision Transformer with Light self-Limited-attention (ViT-LSLA) is presented. The experiments show that ViT-LSLA achieves 71.6% top-1 accuracy on IP102 (2.4% absolute improvement of Swin-T); 87.2% top-1 accuracy on Mini-ImageNet (3.7% absolute improvement of Swin-T). Furthermore, it greatly reduces FLOPs (3.5GFLOPs v...

arxiv.org

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ViT的复仇:Meta AI提出ViT训练的全新baseline

即使在中等规模的数据集上,研究者也会利用具有更多能力的模型。例如,仅在 ImageNet1k上训练 ViT-H时, top-1准确率达到 85.2%,这比文献中报道的分辨率为 224×224的监督训练过程的最佳 ViT-H提高了 +5.1%。 ImageNet-1k 训练程序允许训练十亿参数的 ViT-H(52层),无需任何超参数适应,只需使用与 ViT-H相同的随机深度下降率。在 224...

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[2508.13957] ViT-FIQA: Assessing Face Image Quality using Vision Transformers

View PDFHTML (experimental)Abstract:Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA...

arxiv.org

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[2609.34021] Position Aware Layer Queries for Test Time Training in Vision Language Models

adapts and improves zero-shot CLIP ViT-B/16 by 9.8% Top-1 on OOD ImageNet, ii) outperforms the previous best GS-Bias on fine-grained classification by 3.9% Top-1, iii) achieves faster convergence than TPS for CLIP ResNet-50 (47 mins vs 55 mins), iv) generalizes adaptation to VLMs like SigLIP, EVA-CLIP, and CoCa, and lightweight studen...

arxiv.org
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