• Publications
  • Influence
Learning to Detect Salient Objects with Image-Level Supervision
This paper develops a weakly supervised learning method for saliency detection using image-level tags only, which outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts.
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Amulet is presented, a generic aggregating multi-level convolutional feature framework for salient object detection that provides accurate salient object labeling and performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.
Learning Uncertain Convolutional Features for Accurate Saliency Detection
A novel deep fully convolutional network model for accurate salient object detection and an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in the authors' decoder network are proposed.
Least Soft-Threshold Squares Tracking
The proposed Least Soft-thresold Squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be solved efficiently and derived a LSS distance to measure the difference between an observation sample and the dictionary.
Online Object Tracking With Sparse Prototypes
This paper proposes a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models, and introduces l1 regularization into the PCA reconstruction.
Stepwise Metric Promotion for Unsupervised Video Person Re-identification
This paper proposes a stepwise metric promotion approach to estimate the identities of training tracklets, which iterates between cross-camera tracklet association and feature learning, and can eliminate the hard negative label matches.
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters
A novel adaptive spatially-regularized correlation filters model to simultaneously optimize the filter coefficients and the spatial regularization weight is proposed, which could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process.
Visual Tracking via Probability Continuous Outlier Model
  • D. Wang, Huchuan Lu
  • Mathematics, Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 23 June 2014
This paper presents a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the linear representation model and designs an effective observation likelihood function and a simple update scheme for visual tracking.
Structured Siamese Network for Real-Time Visual Tracking
A local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking and can be formulated as the inference process of a conditional random field and implemented by differentiable operations, allowing the entire model to be trained in an end-to-end manner.
Learning regression and verification networks for long-term visual tracking
This work proposes a novel long-term tracking framework based on deep regression and verification networks, designed using the object-aware feature fusion and region proposal networks to generate a series of candidates and estimate their similarity scores effectively.