https://vit.ggto3.top

[2410.22709v3] FilterViT and DropoutViT

View PDFHTML (experimental)Abstract:In this study, we introduce an enhanced version of ViT that conducts attention-based QKV operations during the initial stages of downsampling. Performing attention directly on high-resolution feature maps is computationally demanding due to the large size and numerous tokens. To mitigate this, we propose ...

arxiv.org 2024年10月30日

SAC-ViT: Semantic-Aware Clustering Vision Transformer with Early Exit

significantly boosting overall ViT performance. Extensive experiments demonstrate the efficacy of SAC-ViT, reducing 62% of the FLOPs of DeiT and achieving 1.98× throughput without compromising performance. Figure 1:Comparing our SAC-ViT with state-of-the-art adaptive ViT optimization methods, SAC-ViT achieves better efficiency/Top-1 a...

arxiv.org 2010年4月15日

[2407.05266] CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs

ViT, with performance improvements of up to 3% in top-1 accuracy for classification, 0.6 mAP for object detection, and 1.5 mIoU for segmentation at similar or better compression ratio over existing alternatives. Code is available at this https URLComments: ECCV 2024 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligen...

arxiv.org 2024年7月7日

[2103.11886] DeepViT: Towards Deeper Vision Transformer

cost. The pro-posed method makes it feasible to train deeper ViT models with consistent performance improvements via minor modification to existing ViT models. Notably, when training a deep ViT model with 32 transformer blocks, the Top-1 classification accuracy can be improved by 1.6% on ImageNet. Code is publicly available at this https UR...

arxiv.org

[2606.07558v1] Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing

with RegNetY-16GF achieving 99.16% and ViT-large 99.12% Top-1 accuracy on the held-out test set. CLIP ViT-B/16 reached 99.14% with optimized text descriptions. Conclusion: Image-only models, particularly RegNetY-16GF, deliver near-perfect classification accuracy and produce consistent labels across 649,508 unlabeled archival pages with...

arxiv.org 5月25日

ViT的复仇:Meta AI提出ViT训练的全新baseline

论文地址:https://arxiv.org/pdf/2204.07118.pdf研究者们希望这个工作能有助于更好地理解如何充分利用 Transformer 的潜力以及说明类似 BerT 的预训练的重要性。他们的工... 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...

澎湃新闻 2022年5月4日

TOPVideoto3GPConverte – 手机爱问

TOPVideoto3GPConverter1.1: TOP Video to 3GP Converter是一款热门视频到3GP移动转换器。转换你喜欢的W ... TOP Video to 3GP Converter是一款热门视频到3GP移动转换器。转换你喜欢的WMV, MPEG, AVI, MOV, MP4, RM,FLV到黑莓 3GP,黑莓 AMR,黑莓 AVI,黑莓 MPEG-4。可以帮助您预览电影,电影剪辑,快照影片,转换完成后自动关机...

新浪爱问 2022年12月15日

ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers

Vision Transformers (ViTs)(vit)have achieved outstanding performance across various computer vision tasks such as image classification, object detection, and instance segmentation. Despite this, in contrast to previously common visual backbones like Convolutional Neural Networks (CNNs)(krizhevsky2012imagenet), ViTs require significantly mo...

arxiv.org 2014年12月10日

Muon Learns More Robust and Transferable Features than Adam

Table 2:Robustness of optimizers across image and language pretraining. For ResNet-18 and ViT-S we report Top-1 accuracy (%,↑\uparrow) on ImageNet-C across the four standard corruption categories (Noise, Blur, Weather, Digital), with the rightmost column averaging over all 15 corruption types. For GPT-2 and GPT-medium we report perple...

arxiv.org 4月29日

[2506.23532] GViT: Representing Images as Gaussians for Visual Recognition

and opacities are optimized jointly with a ViT classifier trained on top of these representations. We reuse the classifier gradients as constructive guidance, steering the Gaussians toward class-salient regions while a differentiable renderer optimizes an image reconstruction loss. We demonstrate that by 2D Gaussian input representations coupled w...

arxiv.org 2025年6月30日

[2410.22709v3] FilterViT and DropoutViT

SAC-ViT: Semantic-Aware Clustering Vision Transformer with Early Exit

[2407.05266] CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs