45 citations · 333 across the 23 of their papers we have counts for
24 papers
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
Zhiyang Dou, Xuelin Chen, Qingnan Fan +2
We present CASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters. Our physically simulated character c…
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
Jiazhao Zhang, Liu Dai, Fanpeng Meng +4
Object goal navigation (ObjectNav) in unseen environments is a fundamental task for Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene graphs, o…
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
Yan Zhao, Ruihai Wu, Zhehuan Chen +4
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that…
Multi-Robot Active Mapping via Neural Bipartite Graph Matching
Kai Ye, Siyan Dong, Qingnan Fan +5
We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimati…
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Mingxin Yu, Lin Shao, Zhehuan Chen +4
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly sim…
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Yian Wang, Ruihai Wu, Kaichun Mo +4
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in…
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
Yanchao Yang, Hanxiang Ren, He Wang +5
We describe an unsupervised domain adaptation method for image content shift caused by viewpoint changes for a semantic segmentation task. Most existing methods perform domain alig…
Contrastive Multimodal Fusion with TupleInfoNCE
Yunze Liu, Qingnan Fan, Shanghang Zhang +3
This paper proposes a method for representation learning of multimodal data using contrastive losses. A traditional approach is to contrast different modalities to learn the inform…
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Ruihai Wu, Yan Zhao, Kaichun Mo +7
Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of…
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Yijia Weng, He Wang, Qiang Zhou +6
In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle…
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Yunze Liu, Li Yi, Shanghang Zhang +3
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used a…
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Siyan Dong, Qingnan Fan, He Wang +5
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization…
Towards Accurate Active Camera Localization
Qihang Fang, Yingda Yin, Qingnan Fan +6
In this work, we tackle the problem of active camera localization, which controls the camera movements actively to achieve an accurate camera pose. The past solutions are mostly ba…
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
Ziyi Wu, Yueqi Duan, He Wang +2
Point cloud is an important 3D data representation widely used in many essential applications. Leveraging deep neural networks, recent works have shown great success in processing…
Generative 3D Part Assembly via Dynamic Graph Learning
Jialei Huang, Guanqi Zhan, Qingnan Fan +5
Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a si…
Single image reflection removal via learning with multi-image constraints
Yingda Yin, Qingnan Fan, Dongdong Chen +5
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is i…
A General Decoupled Learning Framework for Parameterized Image Operators
Qingnan Fan, Dongdong Chen, Lu Yuan +3
Many different deep networks have been used to approximate, accelerate or improve traditional image operators. Among these traditional operators, many contain parameters which need…
GraphX Chest X-Ray Classification Under Extreme Minimal Supervision
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the crit…
Gated Context Aggregation Network for Image Dehazing and Deraining
Dongdong Chen, Mingming He, Qingnan Fan +5
Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.…
Image Smoothing via Unsupervised Learning
Qingnan Fan, Jiaolong Yang, David Wipf +2
Image smoothing represents a fundamental component of many disparate computer vision and graphics applications. In this paper, we present a unified unsupervised (label-free) learni…
Decouple Learning for Parameterized Image Operators
Qingnan Fan, Dongdong Chen, Lu Yuan +3
Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these…
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero +4
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature…
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
Qingnan Fan, Jiaolong Yang, Gang Hua +2
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.…
Revisiting Deep Intrinsic Image Decompositions
Qingnan Fan, Jiaolong Yang, Gang Hua +2
While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse…