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- 3D Hand Shape and Pose Estimation From a Single RGB Image517
This work proposes a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose and proposes a weakly-supervised approach by leveraging the depth map as a weak supervision in training.
- Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera455
A novel distance metric for hand dissimilarity measure, called Finger-Earth Mover's Distance (FEMD), which only matches fingers while not the whole hand shape, can better distinguish hand gestures of slight differences.
- MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video444
This work proposes MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer block to separately model the temporal motion of each joint and a spatial transformer block for inter-joint spatial correlation, and extends from the central frame to entire frames of the input video, thereby improving the coherence between the input and output sequences.
- Track to Detect and Segment: An Online Multi-Object Tracker431
A new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end, infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation.
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- Robust hand gesture recognition with kinect sensor323
This work presents a hand gesture recognition system with Kinect sensor, which operates robustly in uncontrolled environments and is insensitive to hand variations and distortions, and applies a novel shape distance metric called Finger-Earth Mover's Distance (FEMD) forHand gesture recognition.
- Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images320
A weakly-supervised method, adaptating from fully-annotated synthetic dataset toWeakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for3D pose regression.
- Abnormal event detection in crowded scenes using sparse representation320
This work proposes the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample, and introduces the prior weight of each basis during sparse reconstruction, which is more robust than other outlier detection criteria.
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- Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs301
This work proposes to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane to produce final 3D hand pose estimation with learned pose priors.
- PointCloud Saliency Maps284
A novel way of characterizing critical points and segments to build point-cloud saliency maps is proposed, and each saliency score can be efficiently measured by the corresponding gradient of the loss w.r.t the point under the spherical coordinates.
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- Clustering Driven Deep Autoencoder for Video Anomaly Detection257
A novel convolution autoencoder architecture is designed to separately capture spatial and temporal informative representation to separately capture spatial and temporal informative representation for video anomaly detection.
- Multi-stream CNN: Learning representations based on human-related regions for action recognition247
A human-related multi-stream CNN (HR-MSCNN) architecture that encodes appearance, motion, and the captured tubes of the human- related regions is introduced that achieves state-of-the-art results on these four datasets.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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