Vision-language models (VLMs) pre-trained on large-scale image-text pairs have
demonstrated impressive transferability on various visual tasks. Transferring knowledge …

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is
an important topic in computer vision research. Along with the growth of computational …

The rapid development of facial manipulation techniques has aroused public concerns in
recent years. Existing deepfake video detection approaches attempt to capture the discrim …

Transferring knowledge from pre-trained deep models for downstream tasks, particularly
with limited labeled samples, is a fundamental problem in computer vision research. Recent …

With the rapid development of Deepfake techniques, the capacity of generating hyper-
realistic faces has aroused public concerns in recent years. The temporal inconsistency …

We study self-supervised video representation learning, which is a challenging task due to
1) sufficient labels for supervision; 2) unstructured and noisy visual information. Existing …

Temporal modeling plays a crucial role in understanding video content. To tackle this
problem, previous studies built complicated temporal relations through time sequence …

It is challenging for artificial intelligence systems to achieve accurate video recognition
under the scenario of low computation costs. Adaptive inference based efficient video …

Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing
cross-scale complementarity. However, existing architectures treat different scale features …

Temporal action proposal generation (TAPG) is a challenging task that aims to locate action
instances in untrimmed videos with temporal boundaries. To evaluate the confidence of …