174 citations · 566 across the 18 of their papers we have counts for
18 papers
Associative Transformer
Yuwei Sun, Hideya Ochiai, Zhirong Wu +2
Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual lea…
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
Long Lian, Zhirong Wu, Stella X. Yu
We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move…
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
Long Lian, Zhirong Wu, Stella X. Yu
We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. Previous methods in unsupervised video object segmentation…
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Huimin Wu, Chenyang Lei, Xiao Sun +5
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques…
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Zhihang Zhong, Xiao Sun, Zhirong Wu +3
We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without consi…
Extreme Masking for Learning Instance and Distributed Visual Representations
Zhirong Wu, Zihang Lai, Xiao Sun +1
The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously. We us…
Instance Localization for Self-supervised Detection Pretraining
Ceyuan Yang, Zhirong Wu, Bolei Zhou +1
Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. The objective…
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou +3
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learnin…
What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau +1
Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress. Notably, recent work on unsupervised pretraining has shown to surpas…
A Transductive Approach for Video Object Segmentation
Yizhuo Zhang, Zhirong Wu, Houwen Peng +1
Semi-supervised video object segmentation aims to separate a target object from a video sequence, given the mask in the first frame. Most of current prevailing methods utilize info…
Distilling Localization for Self-Supervised Representation Learning
Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau +1
Recent progress in contrastive learning has revolutionized unsupervised representation learning. Concretely, multiple views (augmentations) from the same image are encouraged to ma…
Deep Metric Transfer for Label Propagation with Limited Annotated Data
Bin Liu, Zhirong Wu, Han Hu +1
We study object recognition under the constraint that each object class is only represented by very few observations. Semi-supervised learning, transfer learning, and few-shot reco…
Improving Generalization via Scalable Neighborhood Component Analysis
Zhirong Wu, Alexei A. Efros, Stella X. Yu
Current major approaches to visual recognition follow an end-to-end formulation that classifies an input image into one of the pre-determined set of semantic categories. Parametric…
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu +1
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether th…
Temporal Action Detection with Structured Segment Networks
Yue Zhao, Yuanjun Xiong, Limin Wang +3
Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the tempo…
Deep Markov Random Field for Image Modeling
Zhirong Wu, Dahua Lin, Xiaoou Tang
Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact…
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
Zhirong Wu, Dahua Lin, Xiaoou Tang
Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representati…
3D ShapeNets: A Deep Representation for Volumetric Shapes
Zhirong Wu, Shuran Song, Aditya Khosla +4
3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability…