118 citations · 275 across the 37 of their papers we have counts for
37 papers
Measuring Acoustics with Collaborative Multiple Agents
Yinfeng Yu, Changan Chen, Lele Cao +2
As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to mor…
Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos
Yikai Wang, Yinpeng Dong, Fuchun Sun +1
This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categ…
Towards the Sparseness of Projection Head in Self-Supervised Learning
Zeen Song, Xingzhe Su, Jingyao Wang +3
In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data. One successful SSL method is contra…
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
Lingyu Si, Hongwei Dong, Wenwen Qiang +5
Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark…
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Jiaqi Han, Wenbing Huang, Yu Rong +3
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why…
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Tianying Ji, Yu Luo, Fuchun Sun +3
Learning high-quality -value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works primarily focus on a…
Subequivariant Graph Reinforcement Learning in 3D Environments
Runfa Chen, Jiaqi Han, Fuchun Sun +1
Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL. Howe…
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge
Zhibin Qiu, Mengfan Fu, Fuchun Sun +2
We propose SE-Bridge, a novel method for speech enhancement (SE). After recently applying the diffusion models to speech enhancement, we can achieve speech enhancement by solving a…
Compacting Binary Neural Networks by Sparse Kernel Selection
Yikai Wang, Wenbing Huang, Yinpeng Dong +2
Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously reve…
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Xingzhe Su, Wenwen Qiang, Jie Hu +3
Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, a…
A Survey on Causal Reinforcement Learning
Yan Zeng, Ruichu Cai, Fuchun Sun +2
While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lac…
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Hang Gao, Jiangmeng Li, Wenwen Qiang +5
Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from…
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks
Xiao Li, Ziqi Wang, Bo Zhang +2
Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them…
Timestamp-Supervised Action Segmentation from the Perspective of Clustering
Dazhao Du, Enhan Li, Lingyu Si +2
Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frame…
SRTNet: Time Domain Speech Enhancement Via Stochastic Refinement
Zhibin Qiu, Mengfan Fu, Yinfeng Yu +3
Diffusion model, as a new generative model which is very popular in image generation and audio synthesis, is rarely used in speech enhancement. In this paper, we use the diffusion…
Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance
Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen +2
Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the…
Pay Self-Attention to Audio-Visual Navigation
Yinfeng Yu, Lele Cao, Fuchun Sun +2
Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio…
Bridged Transformer for Vision and Point Cloud 3D Object Detection
Yikai Wang, TengQi Ye, Lele Cao +4
3D object detection is a crucial research topic in computer vision, which usually uses 3D point clouds as input in conventional setups. Recently, there is a trend of leveraging mul…
When to Update Your Model: Constrained Model-based Reinforcement Learning
Tianying Ji, Yu Luo, Fuchun Sun +3
Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization a…
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
Jiangmeng Li, Yanan Zhang, Wenwen Qiang +5
Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification…
Robust Causal Graph Representation Learning against Confounding Effects
Hang Gao, Jiangmeng Li, Wenwen Qiang +4
The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: th…
Scene Graph for Embodied Exploration in Cluttered Scenario
Yuhong Deng, Qie Sima, Di Guo +3
The ability to handle objects in cluttered environment has been long anticipated by robotic community. However, most of works merely focus on manipulation instead of rendering hidd…
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
Xueyi Liu, Yu Rong, Tingyang Xu +3
Graph instance contrastive learning has been proved as an effective task for Graph Neural Network (GNN) pre-training. However, one key issue may seriously impede the representative…
SNAKE: Shape-aware Neural 3D Keypoint Field
Chengliang Zhong, Peixing You, Xiaoxue Chen +6
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detecti…