630 citations · 2.4k across the 27 of their papers we have counts for
61 papers · 1 filter
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Aadarsh Sahoo, Rutav Shah, Rameswar Panda +2
Unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain has attracted much attention in recent years. W…
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…
Dynamic Network Quantization for Efficient Video Inference
Ximeng Sun, Rameswar Panda, Chun-Fu Chen +3
Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational…
Separating Skills and Concepts for Novel Visual Question Answering
Spencer Whitehead, Hui Wu, Heng Ji +2
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them…
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
Rameswar Panda, Chun-Fu Chen, Quanfu Fan +4
Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learni…