2023-06-22 更新
RaViTT: Random Vision Transformer Tokens
Authors:Felipe A. Quezada, Carlos F. Navarro, Cristian Muñoz, Manuel Zamorano, Jorge Jara-Wilde, Violeta Chang, Cristóbal A. Navarro, Mauricio Cerda
Vision Transformers (ViTs) have successfully been applied to image classification problems where large annotated datasets are available. On the other hand, when fewer annotations are available, such as in biomedical applications, image augmentation techniques like introducing image variations or combinations have been proposed. However, regarding ViT patch sampling, less has been explored outside grid-based strategies. In this work, we propose Random Vision Transformer Tokens (RaViTT), a random patch sampling strategy that can be incorporated into existing ViTs. We experimentally evaluated RaViTT for image classification, comparing it with a baseline ViT and state-of-the-art (SOTA) augmentation techniques in 4 datasets, including ImageNet-1k and CIFAR-100. Results show that RaViTT increases the accuracy of the baseline in all datasets and outperforms the SOTA augmentation techniques in 3 out of 4 datasets by a significant margin +1.23% to +4.32%. Interestingly, RaViTT accuracy improvements can be achieved even with fewer tokens, thus reducing the computational load of any ViT model for a given accuracy value.
PDF 9 pages, 6 figures
点此查看论文截图
Masked Diffusion Models are Fast Learners
Authors:Jiachen Lei, Peng Cheng, Zhongjie Ba, Kui Ren
Diffusion models have emerged as the de-facto technique for image generation, yet they entail significant computational overhead, hindering the technique’s broader application in the research community. We propose a prior-based denoising training framework, the first to incorporate the pre-train and fine-tune paradigm into the diffusion model training process, which substantially improves training efficiency and shows potential in facilitating various downstream tasks. Our approach centers on masking a high proportion (e.g., up to 90%) of the input image and employing masked score matching to denoise the visible areas, thereby guiding the diffusion model to learn more salient features from training data as prior knowledge. By utilizing this masked learning process in a pre-training stage, we efficiently train the ViT-based diffusion model on CelebA-HQ 256x256 in the pixel space, achieving a 4x acceleration and enhancing the quality of generated images compared to DDPM. Moreover, our masked pre-training technique is universally applicable to various diffusion models that directly generate images in the pixel space and facilitates learning pre-trained models with excellent generalizability: a diffusion model pre-trained on VGGFace2 attains a 46% quality improvement through fine-tuning with merely 10% local data. Our code is available at https://github.com/jiachenlei/maskdm.
PDF
点此查看论文截图
ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining
Authors:Dezhi Peng, Chongyu Liu, Yuliang Liu, Lianwen Jin
Scene text removal (STR) aims at replacing text strokes in natural scenes with visually coherent backgrounds. Recent STR approaches rely on iterative refinements or explicit text masks, resulting in higher complexity and sensitivity to the accuracy of text localization. Moreover, most existing STR methods utilize convolutional neural networks (CNNs) for feature representation while the potential of vision Transformers (ViTs) remains largely unexplored. In this paper, we propose a simple-yet-effective ViT-based text eraser, dubbed ViTEraser. Following a concise encoder-decoder framework, different types of ViTs can be easily integrated into ViTEraser to enhance the long-range dependencies and global reasoning. Specifically, the encoder hierarchically maps the input image into the hidden space through ViT blocks and patch embedding layers, while the decoder gradually upsamples the hidden features to the text-erased image with ViT blocks and patch splitting layers. As ViTEraser implicitly integrates text localization and inpainting, we propose a novel end-to-end pretraining method, termed SegMIM, which focuses the encoder and decoder on the text box segmentation and masked image modeling tasks, respectively. To verify the effectiveness of the proposed methods, we comprehensively explore the architecture, pretraining, and scalability of the ViT-based encoder-decoder for STR, which provides deep insights into the application of ViT to STR. Experimental results demonstrate that ViTEraser with SegMIM achieves state-of-the-art performance on STR by a substantial margin. Furthermore, the extended experiment on tampered scene text detection demonstrates the generality of ViTEraser to other tasks. We believe this paper can inspire more research on ViT-based STR approaches. Code will be available at https://github.com/shannanyinxiang/ViTEraser.
PDF
点此查看论文截图
Inter-Instance Similarity Modeling for Contrastive Learning
Authors:Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang
The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance similarities among natural images, then leading to potential representation degeneration. In this paper, we propose a novel image mix method, PatchMix, for contrastive learning in Vision Transformer (ViT), to model inter-instance similarities among images. Following the nature of ViT, we randomly mix multiple images from mini-batch in patch level to construct mixed image patch sequences for ViT. Compared to the existing sample mix methods, our PatchMix can flexibly and efficiently mix more than two images and simulate more complicated similarity relations among natural images. In this manner, our contrastive framework can significantly reduce the gap between contrastive objective and ground truth in reality. Experimental results demonstrate that our proposed method significantly outperforms the previous state-of-the-art on both ImageNet-1K and CIFAR datasets, e.g., 3.0% linear accuracy improvement on ImageNet-1K and 8.7% kNN accuracy improvement on CIFAR100. Moreover, our method achieves the leading transfer performance on downstream tasks, object detection and instance segmentation on COCO dataset. The code is available at https://github.com/visresearch/patchmix.
PDF
点此查看论文截图