43 citations · 80 across the 26 of their papers we have counts for
7 papers · 1 filter
Aligning Large Multimodal Models with Factually Augmented RLHF
Zhiqing Sun, Sheng Shen, Shengcao Cao +9
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounde…
InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
Sirui Xu, Zhengyuan Li, Yu-Xiong Wang +1
This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dyna…
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Shengcao Cao, Mengtian Li, James Hays +3
Resource-constrained perception systems such as edge computing and vision-for-robotics require vision models to be both accurate and lightweight in computation and memory usage. Wh…
Stochastic Multi-Person 3D Motion Forecasting
Sirui Xu, Yu-Xiong Wang, Liang-Yan Gui
This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity…
Contrastive Mean Teacher for Domain Adaptive Object Detectors
Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui +1
Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in…
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Yunze Man, Liang-Yan Gui, Yu-Xiong Wang
Closing the domain gap between training and deployment and incorporating multiple sensor modalities are two challenging yet critical topics for self-driving. Existing work only foc…
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors
Sirui Xu, Yu-Xiong Wang, Liang-Yan Gui
Predicting diverse human motions given a sequence of historical poses has received increasing attention. Despite rapid progress, existing work captures the multi-modal nature of hu…