Search Results for author:
Found 31 papers, 14 papers with code
Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition
Gait recognition aims at identifying different people by the walking patterns, which can be conducted at a long distance without the cooperation of subjects.
OmniDiff: A Comprehensive Benchmark for Fine-grained Image Difference Captioning
Image Difference Captioning (IDC) aims to generate natural language descriptions of subtle differences between image pairs, requiring both precise visual change localization and coherent semantic expression.
Bridging Gait Recognition and Large Language Models Sequence Modeling
Gait sequences exhibit sequential structures and contextual relationships similar to those in natural language, where each element--whether a word or a gait step--is connected to its predecessors and successors.
OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization
This paper addresses the challenge of animal re-identification, an emerging field that shares similarities with person re-identification but presents unique complexities due to the diverse species, environments and poses.
Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future.
OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality
To this end, we first develop OpenGait, a flexible and efficient gait recognition platform.
Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser
To address these problems, a Disentangled Diffusion-based 3D Human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose.
QAGait: Revisit Gait Recognition from a Quality Perspective
Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns.
Learning Visual Prompt for Gait Recognition
However gait recognition in real-world scenarios is challenging due to the limitations of capturing comprehensive cross-viewing and cross-clothing data.
Towards More Efficient Depression Risk Recognition via Gait
Gait is a complex form of motion, and hand-crafted gait features often only capture a fraction of the intricate associations between gait and depression risk.
FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition
Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks.
Free Lunch for Gait Recognition: A Novel Relation Descriptor
Gait recognition is to seek correct matches for query individuals by their unique walking patterns.
Unsupervised Gait Recognition with Selective Fusion
So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR).
GPGait: Generalized Pose-based Gait Recognition
Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods.
Exploring Deep Models for Practical Gait Recognition
Gait recognition is a rapidly advancing vision technique for person identification from a distance.
Dynamic Aggregated Network for Gait Recognition
Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns.
Fine-grained Unsupervised Domain Adaptation for Gait Recognition
Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security.
OpenGait: Revisiting Gait Recognition Towards Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions
For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions.
OpenGait: Revisiting Gait Recognition Toward Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
Deep Learning-based Occluded Person Re-identification: A Survey
After that, we summarize and compare the performance of recent occluded person Re-ID methods on four popular datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC.
Progressive Feature Learning for Realistic Cloth-Changing Gait Recognition
Gait recognition is instrumental in crime prevention and social security, for it can be conducted at a long distance to figure out the identity of persons.
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark
As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.
GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality
Gait is one of the most promising biometrics to identify individuals at a long distance.
Data Augmentation For Medical MR Image Using Generative Adversarial Networks
Our results show that PGGAN-SSIM successfully generates 256x256 realistic brain tumor MR images which fill the real image distribution uncovered by the original dataset.
Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN
In this paper, we, therefore, introduce a Dense-View GEIs Set (DV-GEIs) to deal with the challenge of limited view angles.
Learning Efficient Convolutional Networks through Irregular Convolutional Kernels
As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power devices are designed with very limited memory that can not store large models.
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.
Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels.
Enhanced Mixtures of Part Model for Human Pose Estimation
In this condition, we can combine extracted human blobs with histogram of gradient feature, which is commonly used in mixture of parts model for training body part templates.
Relevance Topic Model for Unstructured Social Group Activity Recognition
Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data.
