Search Results for author:
Found 25 papers, 14 papers with code
Dual Compensation Residual Networks for Class Imbalanced Learning
Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models.
Diversity-Measurable Anomaly Detection
In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies.
Ranked #1 on
Anomaly Detection
on UCSD Ped2
Anomaly Detection In Surveillance Videos
Defect Detection
+1
Clothes-Changing Person Re-identification with RGB Modality Only
In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w. r. t.
Ranked #2 on
Person Re-Identification
on PRCC
Clothes Changing Person Re-Identification
Person Re-Identification
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability.
Ranked #3 on
Visual Object Tracking
on UAV123
Salient-to-Broad Transition for Video Person Re-Identification
To further improve SBM, an Integration-and-Distribution Module (IDM) is introduced to enhance frame-level representations.
Feature Completion for Occluded Person Re-Identification
Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets.
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification
Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts.
Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking
This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking.
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts.
Temporal Complementary Learning for Video Person Re-Identification
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.
Appearance-Preserving 3D Convolution for Video-based Person Re-identification
Due to the imperfect person detection results and posture changes, temporal appearance misalignment is unavoidable in video-based person re-identification (ReID).
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors.
Ranked #89 on
Object Detection
on COCO test-dev
Cross Attention Network for Few-shot Classification
The unseen classes and low-data problem make few-shot classification very challenging.
Temporal Knowledge Propagation for Image-to-Video Person Re-identification
With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated.
Ranked #8 on
Person Re-Identification
on iLIDS-VID
Image-To-Video Person Re-Identification
Video-Based Person Re-Identification
VRSTC: Occlusion-Free Video Person Re-Identification
For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame.
Interaction-and-Aggregation Network for Person Re-identification
Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings.
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection
Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Visual relationship detection with deep structural ranking
In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
Robust path-based spectral clustering
In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings.
Super-Resolution with Deep Adaptive Image Resampling
In this work, we revisit the more traditional interpolation-based methods, that were popular before, now with the help of deep learning.
A Unified Multiplicative Framework for Attribute Learning
Attributes are mid-level semantic properties of objects.
Generalized Unsupervised Manifold Alignment
In this paper, we propose a generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences.
Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity.
Deeply Coupled Auto-encoder Networks for Cross-view Classification
In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers.
