1 citations · 1 across the 7 of their papers we have counts for
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NowYouSee Me: Context-Aware Automatic Audio Description
Seon-Ho Lee, Jue Wang, David Fan +5
Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multimedia content, which provides additional narrations at suitable int…
GEXIA: Granularity Expansion and Iterative Approximation for Scalable Multi-grained Video-language Learning
Yicheng Wang, Zhikang Zhang, Jue Wang +6
In various video-language learning tasks, the challenge of achieving cross-modality alignment with multi-grained data persists. We propose a method to tackle this challenge from tw…
Video Token Merging for Long-form Video Understanding
Seon-Ho Lee, Jue Wang, Zhikang Zhang +2
As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resor…
Text-Guided Video Masked Autoencoder
David Fan, Jue Wang, Shuai Liao +3
Recent video masked autoencoder (MAE) works have designed improved masking algorithms focused on saliency. These works leverage visual cues such as motion to mask the most salient…
Motion-Guided Masking for Spatiotemporal Representation Learning
David Fan, Jue Wang, Shuai Liao +5
Several recent works have directly extended the image masked autoencoder (MAE) with random masking into video domain, achieving promising results. However, unlike images, both spat…
MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation
Najmeh Sadoughi, Xinyu Li, Avijit Vajpayee +5
Previous research has studied the task of segmenting cinematic videos into scenes and into narrative acts. However, these studies have overlooked the essential task of multimodal a…
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos
David Fan, Deyu Yang, Xinyu Li +2
Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain. State-of-the-art video contrastive learning methods suc…