Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Recently, the proposed deep multilayer perceptron (MLP) models have stirred up a lot of
interest in the vision community. Historically, the availability of larger datasets combined with …

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to
optimize the performance and complexity of ViTs to enable efficient deployment on mobile …

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual understanding …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

In this paper, we propose Mixed and Masked AutoEncoder (MixMAE), a simple but efficient
pretraining method that is applicable to various hierarchical Vision Transformers. Existing …

Abstract Neural Architecture Search (NAS) has shown promising performance in the
automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing …

Human vision possesses a special type of visual processing systems called peripheral
vision. Partitioning the entire visual field into multiple contour regions based on the distance …

Abstract Recent Vision Transformer Compression (VTC) works mainly follow a two-stage
scheme where the importance score of each model unit is first evaluated or preset in each …

Deep learning has made significant progress in computer vision, specifically in image
classification, object detection, and semantic segmentation. The skip connection has played …