Large video-language models (VLMs) have demonstrated promising progress in various video understanding tasks. However, their effectiveness in long-form video analysis is …
Exposure correction is a fundamental problem in computer vision and image processing. Recently, frequency domainbased methods have achieved impressive improvement, yet they still …
Masked video modeling, such as VideoMAE, is an effective paradigm for video self-supervised learning (SSL). However, they are primarily based on reconstructing pixellevel details …
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by …
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. …
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (house-hold, …
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 …
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric …