236 citations · 862 across the 49 of their papers we have counts for
32 papers · 1 filter
Exploring the Robustness of Human Parsers Towards Common Corruptions
Sanyi Zhang, Xiaochun Cao, Rui Wang +2
Human parsing aims to segment each pixel of the human image with fine-grained semantic categories. However, current human parsers trained with clean data are easily confused by num…
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Yansong Tang, Xingyu Liu, Xumin Yu +3
Rapid progress and superior performance have been achieved for skeleton-based action recognition recently. In this article, we investigate this problem under a cross-dataset settin…
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Jianglin Lu, Jie Zhou, Yudong Chen +2
Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, existing hashing methods assume that…
Similarity-Aware Fusion Network for 3D Semantic Segmentation
Linqing Zhao, Jiwen Lu, Jie Zhou
In this paper, we propose a similarity-aware fusion network (SAFNet) to adaptively fuse 2D images and 3D point clouds for 3D semantic segmentation. Existing fusion-based methods ac…
Global Filter Networks for Image Classification
Yongming Rao, Wenliang Zhao, Zheng Zhu +2
Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases…
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
Zheng Zhu, Guan Huang, Jiankang Deng +8
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training da…
Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search
Cheng Ma, Jiwen Lu, Jie Zhou
As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level…
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images
Lei He, Jiwen Lu, Guanghui Wang +2
Depth estimation and semantic segmentation play essential roles in scene understanding. The state-of-the-art methods employ multi-task learning to simultaneously learn models for t…
Weakly-Supervised Saliency Detection via Salient Object Subitizing
Xiaoyang Zheng, Xin Tan, Jie Zhou +2
Salient object detection aims at detecting the most visually distinct objects and producing the corresponding masks. As the cost of pixel-level annotations is high, image tags are…
Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
Jun Wan, Zhihui Lai, Jing Li +2
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facia…
SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation
An Tao, Yueqi Duan, Yi Wei +2
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. Howe…
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
Yi Wei, Ziyi Wang, Yongming Rao +2
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unorder…
Robust Face Alignment by Multi-order High-precision Hourglass Network
Jun Wan, Zhihui Lai, Jun Liu +2
Heatmap regression (HR) has become one of the mainstream approaches for face alignment and has obtained promising results under constrained environments. However, when a face image…
Reinforced Axial Refinement Network for Monocular 3D Object Detection
Lijie Liu, Chufan Wu, Jiwen Lu +3
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the inf…
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
Benlin Liu, Yongming Rao, Jiwen Lu +2
Knowledge Distillation (KD) has been one of the most popu-lar methods to learn a compact model. However, it still suffers from highdemand in time and computational resources caused…
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation
Xiaobin Wei, Jianjiang Feng, Jie Zhou
We propose a semantics-driven unsupervised learning approach for monocular depth and ego-motion estimation from videos in this paper. Recent unsupervised learning methods employ ph…
Latent Fingerprint Registration via Matching Densely Sampled Points
Shan Gu, Jianjiang Feng, Jiwen Lu +1
Latent fingerprint matching is a very important but unsolved problem. As a key step of fingerprint matching, fingerprint registration has a great impact on the recognition performa…
Graph-based Kinship Reasoning Network
Wanhua Li, Yingqiang Zhang, Kangchen Lv +3
In this paper, we propose a graph-based kinship reasoning (GKR) network for kinship verification, which aims to effectively perform relational reasoning on the extracted features o…
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation
Cheng Ma, Zhenyu Jiang, Yongming Rao +2
Recent works based on deep learning and facial priors have succeeded in super-resolving severely degraded facial images. However, the prior knowledge is not fully exploited in exis…
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
Yongming Rao, Jiwen Lu, Jie Zhou
Local and global patterns of an object are closely related. Although each part of an object is incomplete, the underlying attributes about the object are shared among all parts, wh…
Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation
Yansong Tang, Jiwen Lu, Jie Zhou
Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade,…
BiDet: An Efficient Binarized Object Detector
Ziwei Wang, Ziyi Wu, Jiwen Lu +1
In this paper, we propose a binarized neural network learning method called BiDet for efficient object detection. Conventional network binarization methods directly quantize the we…
SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
Jiachen Xu, Jingyu Gong, Jie Zhou +3
Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information…
PGNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose Estimation
Peiyu Yu, Yongming Rao, Jiwen Lu +1
Humans are able to perform fast and accurate object pose estimation even under severe occlusion by exploiting learned object model priors from everyday life. However, most recently…