Current few-shot action recognition methods reach impressive performance by learning
discriminative features for each video via episodic training and designing various temporal …

Current state-of-the-art approaches for few-shot action recognition achieve promising
performance by conducting frame-level matching on learned visual features. However, they …

Many believe that the successes of deep learning on image understanding problems can be
replicated in the realm of video understanding. However, due to the scale and temporal …

Abstract Temporal Action Detection (TAD) focuses on detecting pre-defined actions, while
Moment Retrieval (MR) aims to identify the events described by open-ended natural …

Most existing temporal action localization (TAL) methods rely on a transfer learning pipeline:
by first optimizing a video encoder on a large action classification dataset (ie, source …

We evaluate existing foundation models video understanding capabilities using a carefully
designed experiment protocol consisting of three hallmark tasks (action recognition …

Temporal action localization (TAL), which involves recognizing and locating action
instances, is a challenging task in video understanding. Most existing approaches directly …

Current few-shot action recognition approaches have achieved impressive performance
using only a few labeled examples. However, they usually assume the base (train) and …

In this paper, we propose a novel sequence verification task that aims to distinguish positive
video pairs performing the same action sequence from negative ones with step-level …

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction
for label-efficient learning. However, current methods mainly focus on optimizing the network …