27 citations · 93 across the 14 of their papers we have counts for
31 papers · 1 filter
Temporal Relevance Analysis for Video Action Models
Quanfu Fan, Donghyun Kim, Chun-Fu +4
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to…
Many-to-many Splatting for Efficient Video Frame Interpolation
Ping Hu, Simon Niklaus, Stan Sclaroff +1
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of mo…
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang +4
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition. Existing video domain adaptation met…
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +3
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achievin…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
Qi Feng, Vitaly Ablavsky, Stan Sclaroff
Natural Language (NL) descriptions can be one of the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle…