676 citations · 1.4k across the 27 of their papers we have counts for
27 papers · 1 filter
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
Yiran Qin, Chaoqun Wang, Zijian Kang +3
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boos…
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification
Ziyi Tang, Ruimao Zhang, Zhanglin Peng +2
In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods m…
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
Hao Zhang, Ruimao Zhang, Zhanglin Peng +2
To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific pro…
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
Zhaoyi Yan, Ruimao Zhang, Hongzhi Zhang +2
Crowd counting is critical for numerous video surveillance scenarios. One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by th…
Parser-Free Virtual Try-on via Distilling Appearance Flows
Yuying Ge, Yibing Song, Ruimao Zhang +3
Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results…
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Zhihao Yuan, Xu Yan, Yinghong Liao +4
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, name…
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Xu Yan, Jiantao Gao, Jie Li +4
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep…
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov, Radu Timofte, Zhilu Zhang +36
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to…
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Yuanfeng Ji, Ruimao Zhang, Zhen Li +3
Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and t…
Exemplar Normalization for Learning Deep Representation
Ruimao Zhang, Zhanglin Peng, Lingyun Wu +2
Normalization techniques are important in different advanced neural networks and different tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by prop…
Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingPreserving Image Content
Han Yang, Ruimao Zhang, Xiaobao Guo +3
Image visual try-on aims at transferring a target clothing image onto a reference person, and has become a hot topic in recent years. Prior arts usually focus on preserving the cha…
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
Zhaoyang Zhang, Jingyu Li, Wenqi Shao +4
Group convolution, which divides the channels of ConvNets into groups, has achieved impressive improvement over the regular convolution operation. However, existing models, eg. Res…
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Jiangfan Han, Xiaoyi Dong, Ruimao Zhang +5
Modern deep neural networks are often vulnerable to adversarial samples. Based on the first optimization-based attacking method, many following methods are proposed to improve the…
Switchable Normalization for Learning-to-Normalize Deep Representation
Ping Luo, Ruimao Zhang, Jiamin Ren +2
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neu…
SSN: Learning Sparse Switchable Normalization via SparsestMax
Wenqi Shao, Tianjian Meng, Jingyu Li +4
Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new per…
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
Yuying Ge, Ruimao Zhang, Lingyun Wu +3
Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial…
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Ping Luo, Zhanglin Peng, Jiamin Ren +1
Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towa…
Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview
Lili Huang, Jiefeng Peng, Ruimao Zhang +2
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused wid…
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
Ruimao Zhang, Hongbin Sun, Jingyu Li +4
Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually imp…
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Ruimao Zhang, Liang Lin, Guangrun Wang +2
This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object inte…
Progressively Diffused Networks for Semantic Image Segmentation
Ruimao Zhang, Wei Yang, Zhanglin Peng +2
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemp…
Cost-Effective Active Learning for Deep Image Classification
Keze Wang, Dongyu Zhang, Ya Li +2
Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In t…
Geometric Scene Parsing with Hierarchical LSTM
Zhanglin Peng, Ruimao Zhang, Xiaodan Liang +2
This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction r…
Deep Structured Scene Parsing by Learning with Image Descriptions
Liang Lin, Guangrun Wang, Rui Zhang +3
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i.e., a semantic object hierarchy with object inter…