Artificial intelligence (AI) has recently become the focus of academia and practitioners,
reflecting the substantial evolution of scientific production in this area, particularly during the …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

In this paper, we explore the spatial redundancy in video recognition with the aim to improve
the computational efficiency. It is observed that the most informative region in each frame of …

Dynamic networks have shown their promising capability in reducing theoretical
computation complexity by adapting their architectures to the input during inference …

Accurately detecting and tracking pedestrians in 3D space is challenging due to large
variations in rotations, poses and scales. The situation becomes even worse for dense …

This paper proposes an Any-time super-Resolution Method (ARM) to tackle the over-
parameterized single image super-resolution (SISR) models. Our ARM is motivated by three …

The local key features in video are important for improving the accuracy of human action
recognition. However, most end-to-end methods focus on global feature learning from …

Fine-grained image retrieval mainly focuses on learning salient features from the seen
subcategories as discriminative embedding while neglecting the problems behind zero-shot …

The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair
evaluation and thus facilitate the evolution of a specific area. Nonetheless, we point out that …

Due to the resource-intensive nature of training vision-language models on expansive video
data a majority of studies have centered on adapting pre-trained image-language models to …