Early exiting has become a promising approach to im-proving the inference efficiency of
deep networks. By structuring models with multiple classifiers (exits), predictions for" easy" …

The superior performance of modern deep networks usually comes with a costly training
procedure. This paper presents a new curriculum learning approach for the efficient training …

The development of vision models for real-world applications is hindered by the challenge of
annotated data scarcity, which has necessitated the adoption of data-efficient visual learning …

Human action recognition (HAR) refers to the process in which computers analyze and
process video data to obtain the categories of action presented in the video. It has a wide …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …

In this paper, we explore the spatial redundancy in continuous sign language recognition
(CSLR), aiming to improve its efficiency. Despite recent advances in accuracy in CSLR, state …

Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …

Compressed video action recognition offers the advantage of reducing decoding and
inference time compared to the RGB domain. However, the compressed domain poses …

Recently numerous approaches have achieved notable success in compressed video
quality enhancement (VQE). However these methods usually ignore the utilization of …

With the rapid growth of social media, fake news (rumors) are rampant online, seriously
endangering the health of mainstream social consciousness. Fake news detection (FEND) …