Temporal action detection (TAD) is a fundamental video understanding task that aims to identify human actions and localize their temporal boundaries in videos. Although this field …
CLIP is a powerful spatial feature extractor trained on a large dataset of image-text pairs. It exhibits strong generalization when extended to other domains and modalities. …
Movie trailers are an essential tool for promoting films and attracting audiences. However the process of creating trailers can be time-consuming and expensive. To streamline this …
Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly …
Recently, temporal action detection (TAD) has seen significant performance improvement with end-to-end training. However, due to the memory bottleneck, only models with limited …
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end …
Recently, conditional diffusion models have gained popularity in numerous applications due to their exceptional generation ability. However, many existing methods are …
With the recent advances in video and 3D understanding, novel 4D spatio-temporal methods fusing both concepts have emerged. Towards this direction, the Ego4D Episodic Memory …
The 'pre-training → downstream adaptation' presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved …
Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. …