activity
20172022
most citedDeep Multimodal Fusion by Channel Exchanging

118 citations · 425 across the 21 of their papers we have counts for

collaborators

30 papers

cs.CV20228 cited

Learning Active Camera for Multi-Object Navigation

Peihao Chen, Dongyu Ji, Kunyang Lin +5

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently w…

cs.LG202210 cited

Learning Physical Dynamics with Subequivariant Graph Neural Networks

Jiaqi Han, Wenbing Huang, Hengbo Ma +3

Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. However, they still encounter several challenges: 1) Physical laws abide by symmetry, whi…

cs.GT20222 cited

Benefits of Permutation-Equivariance in Auction Mechanisms

Tian Qin, Fengxiang He, Dingfeng Shi +2

Designing an incentive-compatible auction mechanism that maximizes the auctioneer's revenue while minimizes the bidders' ex-post regret is an important yet intricate problem in eco…

cs.CV20221 cited

Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation

Runfa Chen, Yu Rong, Shangmin Guo +4

After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation. Unfortunately,…

cs.SD20227 cited

Sound Adversarial Audio-Visual Navigation

Yinfeng Yu, Wenbing Huang, Fuchun Sun +3

Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visu…

cs.LG202234 cited

Geometrically Equivariant Graph Neural Networks: A Survey

Jiaqi Han, Yu Rong, Tingyang Xu +1

Many scientific problems require to process data in the form of geometric graphs. Unlike generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or…