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Found 24 papers, 14 papers with code
Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification
Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments.
BigGait: Learning Gait Representation You Want by Large Vision Models
Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities.
Cross-Covariate Gait Recognition: A Benchmark
Compared to existing datasets, CCGR has both population and individual-level diversity.
A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition
In this paper, by considering the temporal and spatial characteristics of gait data, we propose a multi-stage feature fusion strategy (MSFFS), which performs multimodal fusions at different stages in the feature extraction process.
SkeletonGait: Gait Recognition Using Skeleton Maps
In this paper, we introduce a novel skeletal gait representation named skeleton map, together with SkeletonGait, a skeleton-based method to exploit structural information from human skeleton maps.
Recent neutrino oscillation result with the IceCube experiment
The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole.
Pedestrian Attribute Editing for Gait Recognition and Anonymization
As a kind of biometrics, the gait information of pedestrians has attracted widespread attention from both industry and academia since it can be acquired from long distances without the cooperation of targets.
Exploring Deep Models for Practical Gait Recognition
Gait recognition is a rapidly advancing vision technique for person identification from a distance.
OpenGait: Revisiting Gait Recognition Towards Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
Video-based gait recognition has achieved impressive results in constrained scenarios.
OpenGait: Revisiting Gait Recognition Toward Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
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.
A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges
We provide a comprehensive survey on recent literature using deep learning and a discussion on the privacy and security of gait biometrics.
An Identity-Preserved Framework for Human Motion Transfer
Although previous methods have achieved good results in synthesizing good-quality videos, they lose sight of individualized motion information from the source and target motions, which is significant for the realism of the motion in the generated video.
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.
Gait Recognition with Mask-based Regularization
Also, we design an Inception-like ReverseMask Block, which has three branches composed of a global branch, a feature dropping branch, and a feature scaling branch.
Ranked #2 on
Gait Recognition
on OUMVLP
Detect Faces Efficiently: A Survey and Evaluations
However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various "in the wild" faces.
A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization
Specifically, the stricter the metric is, the more notable the gain is, improving \textbf{8. 2 mAP} on VOC2007 and \textbf{5. 4 mAP} on COCO2017 at metric $AP_{90}$.
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples
In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance.
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.
iLGaCo: Incremental Learning of Gait Covariate Factors
In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset.
Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs
NOvA is a long-baseline neutrino oscillation experiment.
POP-Norm: A Theoretically Justified and More Accelerated Normalization Approach
Batch Normalization (BatchNorm) has been a default module in modern deep networks due to its effectiveness for accelerating training deep neural networks.
PEA265: Perceptual Assessment of Video Compression Artifacts
To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.
