95 citations · 202 across the 16 of their papers we have counts for
17 papers
Morphable Detector for Object Detection on Demand
Xiangyun Zhao, Xu Zou, Ying Wu
Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel classes on the fly with minimum o…
Unsupervised Embedding Learning from Uncertainty Momentum Modeling
Jiahuan Zhou, Yansong Tang, Bing Su +1
Existing popular unsupervised embedding learning methods focus on enhancing the instance-level local discrimination of the given unlabeled images by exploring various negative data…
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
Wei Wei, Li Guan, Yue Liu +4
HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to…
MetaView: Few-shot Active Object Recognition
Wei Wei, Haonan Yu, Haichao Zhang +2
In robot sensing scenarios, instead of passively utilizing human captured views, an agent should be able to actively choose informative viewpoints of a 3D object as discriminative…
Contrastive Learning for Label-Efficient Semantic Segmentation
Xiangyun Zhao, Raviteja Vemulapalli, Philip Mansfield +4
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Netwo…
Object Detection with a Unified Label Space from Multiple Datasets
Xiangyun Zhao, Samuel Schulter, Gaurav Sharma +3
Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical ben…
Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection
Gengxing Wang, Jiahuan Zhou, Ying Wu
Recent deep learning based video synthesis approaches, in particular with applications that can forge identities such as "DeepFake", have raised great security concerns. Therefore,…
Uncertainty-aware Score Distribution Learning for Action Quality Assessment
Yansong Tang, Zanlin Ni, Jiahuan Zhou +4
Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ign…
Recognizing Part Attributes with Insufficient Data
Xiangyun Zhao, Yi Yang, Feng Zhou +4
Recognizing attributes of objects and their parts is important to many computer vision applications. Although great progress has been made to apply object-level recognition, recogn…
Bimodal Stereo: Joint Shape and Pose Estimation from Color-Depth Image Pair
Chi Zhang, Yuehu Liu, Ying Wu +2
Mutual calibration between color and depth cameras is a challenging topic in multi-modal data registration. In this paper, we are confronted with a "Bimodal Stereo" problem, which…
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Peixi Xiong, Huayi Zhan, Xin Wang +2
Visual Query Answering (VQA) is of great significance in offering people convenience: one can raise a question for details of objects, or high-level understanding about the scene,…
A Compositional Textual Model for Recognition of Imperfect Word Images
Wei Tang, John Corring, Ying Wu +1
Printed text recognition is an important problem for industrial OCR systems. Printed text is constructed in a standard procedural fashion in most settings. We develop a mathematica…
A Modulation Module for Multi-task Learning with Applications in Image Retrieval
Xiangyun Zhao, Haoxiang Li, Xiaohui Shen +2
Multi-task learning has been widely adopted in many computer vision tasks to improve overall computation efficiency or boost the performance of individual tasks, under the assumpti…
Fictitious GAN: Training GANs with Historical Models
Hao Ge, Yin Xia, Xu Chen +2
Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as…
Cross-view Action Modeling, Learning and Recognition
Jiang wang, Xiaohan Nie, Yin Xia +2
Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel mult…
Learning Fine-grained Image Similarity with Deep Ranking
Jiang Wang, Yang song, Thomas Leung +5
Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that em…
Scalable Matting: A Sub-linear Approach
Philip G. Lee, Ying Wu
Natural image matting, which separates foreground from background, is a very important intermediate step in recent computer vision algorithms. However, it is severely underconstrai…