138 citations · 171 across the 3 of their papers we have counts for
8 papers
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…
Revisiting Few-shot Activity Detection with Class Similarity Control
Huijuan Xu, Ximeng Sun, Eric Tzeng +3
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able…
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun, Rameswar Panda, Rogerio Feris +1
Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafted…
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
Ping Hu, Ximeng Sun, Kate Saenko +1
Learning from a few examples is a challenging task for machine learning. While recent progress has been made for this problem, most of the existing methods ignore the compositional…
Domain Agnostic Learning with Disentangled Representations
Xingchao Peng, Zijun Huang, Ximeng Sun +1
Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of tar…
Similarity R-C3D for Few-shot Temporal Activity Detection
Huijuan Xu, Bingyi Kang, Ximeng Sun +3
Many activities of interest are rare events, with only a few labeled examples available. Therefore models for temporal activity detection which are able to learn from a few example…