665 citations · 1.3k across the 20 of their papers we have counts for
20 papers
Regularizing Second-Order Influences for Continual Learning
Zhicheng Sun, Yadong Mu, Gang Hua
Continual learning aims to learn on non-stationary data streams without catastrophically forgetting previous knowledge. Prevalent replay-based methods address this challenge by reh…
Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce
Yang Jin, Yongzhi Li, Zehuan Yuan +1
This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-…
Image Completion with Heterogeneously Filtered Spectral Hints
Xingqian Xu, Shant Navasardyan, Vahram Tadevosyan +3
Image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawb…
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
Yang Jin, Yongzhi Li, Zehuan Yuan +1
Spatio-Temporal video grounding (STVG) focuses on retrieving the spatio-temporal tube of a specific object depicted by a free-form textual expression. Existing approaches mainly tr…
Learning Sample Importance for Cross-Scenario Video Temporal Grounding
Peijun Bao, Yadong Mu
The task of temporal grounding aims to locate video moment in an untrimmed video, with a given sentence query. This paper for the first time investigates some superficial biases th…
Rethinking the Spatial Route Prior in Vision-and-Language Navigation
Xinzhe Zhou, Wei Liu, Yadong Mu
Vision-and-language navigation (VLN) is a trending topic which aims to navigate an intelligent agent to an expected position through natural language instructions. This work addres…
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds
Guiyu Tian, Wenhao Jiang, Wei Liu +1
This paper presents Poisoning MorphNet, the first backdoor attack method on point clouds. Conventional adversarial attack takes place in the inference stage, often fooling a model…
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Baifeng Shi, Dinghuai Zhang, Qi Dai +3
Convolutional Neural Networks (CNNs) are known to rely more on local texture rather than global shape when making decisions. Recent work also indicates a close relationship between…
Weakly-Supervised Action Localization by Generative Attention Modeling
Baifeng Shi, Qi Dai, Yadong Mu +1
Weakly-supervised temporal action localization is a problem of learning an action localization model with only video-level action labeling available. The general framework largely…
Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos
Guoqiang Gong, Liangfeng Zheng, Kun Bai +1
Temporal action localization is a recently-emerging task, aiming to localize video segments from untrimmed videos that contain specific actions. Despite the remarkable recent progr…
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang, Ke Sun, Tianheng Cheng +9
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-…
Two-Stream Video Classification with Cross-Modality Attention
Lu Chi, Guiyu Tian, Yadong Mu +1
Fusing multi-modality information is known to be able to effectively bring significant improvement in video classification. However, the most popular method up to now is still simp…
High-Resolution Representations for Labeling Pixels and Regions
Ke Sun, Yang Zhao, Borui Jiang +7
High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite{…
Convolutional Video Steganography with Temporal Residual Modeling
Xinyu Weng, Yongzhi Li, Lu Chi +1
Steganography represents the art of unobtrusively concealing a secrete message within some cover data. The key scope of this work is about visual steganography techniques that hide…
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues
Lu Chi, Yadong Mu
In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts…
Deep Hashing: A Joint Approach for Image Signature Learning
Yadong Mu, Zhu Liu
Similarity-based image hashing represents crucial technique for visual data storage reduction and expedited image search. Conventional hashing schemes typically feed hand-crafted f…
Learning Binary Codes and Binary Weights for Efficient Classification
Fumin Shen, Yadong Mu, Wei Liu +2
This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image ca…
Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization
Yadong Mu, Wei Liu, Wei Fan
Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning models over big data. A stochastic gradient is typically calculated from a limit…
Distributed Low-rank Subspace Segmentation
Ameet Talwalkar, Lester Mackey, Yadong Mu +2
Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to rec…
Compact Hyperplane Hashing with Bilinear Functions
Wei Liu, Jun Wang, Yadong Mu +2
Hyperplane hashing aims at rapidly searching nearest points to a hyperplane, and has shown practical impact in scaling up active learning with SVMs. Unfortunately, the existing ran…