activity
20202023
most citedWhat Makes Multi-modal Learning Better than Single (Provably)

43 citations · 150 across the 24 of their papers we have counts for

collaborators
Showing 2021Show all

8 papers · 1 filter

eess.AS2021★ 2 cited

Neural Dubber: Dubbing for Videos According to Scripts

Chenxu Hu, Qiao Tian, Tingle Li +3

Dubbing is a post-production process of re-recording actors' dialogues, which is extensively used in filmmaking and video production. It is usually performed manually by profession…

cs.CV2021★ 2 cited

Co-advise: Cross Inductive Bias Distillation

Sucheng Ren, Zhengqi Gao, Tianyu Hua +4

Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…

cs.LG2021★ 25 cited

Improving Multi-Modal Learning with Uni-Modal Teachers

Chenzhuang Du, Tingle Li, Yichen Liu +4

Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. Howev…

cs.LG2021

Intrinsically Motivated Self-supervised Learning in Reinforcement Learning

Yue Zhao, Chenzhuang Du, Hang Zhao +1

In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and…

cs.LG2021★ 43 cited

What Makes Multi-modal Learning Better than Single (Provably)

Yu Huang, Chenzhuang Du, Zihui Xue +3

The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is…

cs.LG2021★ 4 cited

On Feature Decorrelation in Self-Supervised Learning

Tianyu Hua, Wenxiao Wang, Zihui Xue +3

In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…

cs.CV2021★ 1 cited

Multimodal Knowledge Expansion

Zihui Xue, Sucheng Ren, Zhengqi Gao +1

The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data. Since existing datasets and well-trained m…

cs.CV2021★ 4 cited

Predictive Visual Tracking: A New Benchmark and Baseline Approach

Bowen Li, Yiming Li, Junjie Ye +2

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams ine…