Search Results for author:
Found 24 papers, 14 papers with code
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
In this paper, we propose a novel SSL setting, where unlabeled samples are drawn from a mixed distribution that deviates from the feature distribution of labeled samples.
Clothes-Changing Person Re-Identification with Feasibility-Aware Intermediary Matching
Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features.
Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
In this paper, we try to understand FSL by delving into two key questions: (1) How to quantify the relationship between \emph{training} and \emph{novel} tasks?
Prototype-Guided Text-based Person Search based on Rich Chinese Descriptions
Text-based person search aims to simultaneously localize and identify the target person based on query text from uncropped scene images, which can be regarded as the unified task of person detection and text-based person retrieval task.
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
+2
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 #1 on
Multiview Gait Recognition
on CASIA-B
Clothes Changing Person Re-Identification
Multiview Gait Recognition
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 #4 on
Visual Tracking
on TNL2K
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 #88 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 #9 on
Person Re-Identification
on iLIDS-VID
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.
A Spatio-Temporal Appearance Representation for Viceo-Based Pedestrian Re-Identification
Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions.
Cross-pose Face Recognition by Canonical Correlation Analysis
We argue that one of the diffculties in this problem is the severe misalignment in face images or feature vectors with different poses.
