68 citations · 278 across the 32 of their papers we have counts for
14 papers · 1 filter
Cascaded Multilingual Audio-Visual Learning from Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +8
In this paper, we explore self-supervised audio-visual models that learn from instructional videos. Prior work has shown that these models can relate spoken words and sounds to vis…
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…
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…
IA-RED: Interpretability-Aware Redundancy Reduction for Vision Transformers
Bowen Pan, Rameswar Panda, Yifan Jiang +3
The self-attention-based model, transformer, is recently becoming the leading backbone in the field of computer vision. In spite of the impressive success made by transformers in a…
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Ashraful Islam, Chun-Fu Chen, Rameswar Panda +3
Most existing works in few-shot learning rely on meta-learning the network on a large base dataset which is typically from the same domain as the target dataset. We tackle the prob…
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…