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Found 38 papers, 12 papers with code
DHRNet: A Dual-Path Hierarchical Relation Network for Multi-Person Pose Estimation
Multi-person pose estimation (MPPE) presents a formidable yet crucial challenge in computer vision.
Towards more realistic human motion prediction with attention to motion coordination
However, the motion coordination, a global joint relation reflecting the simultaneous cooperation of all joints, is usually weakened because it is learned from part to whole progressively and asynchronously.
Full-frequency dynamic convolution: a physical frequency-dependent convolution for sound event detection
Recently, 2D convolution has been found unqualified in sound event detection (SED).
A Two-stream Hybrid CNN-Transformer Network for Skeleton-based Human Interaction Recognition
Specifically, Transformer-based stream integrates 3D convolutions with multi-head self-attention to learn inter-token correlations; We propose a new multi-branch CNN framework for CNN-based streams that automatically learns joint spatio-temporal features from skeleton sequences.
Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimation
In the second stage, we allow the keypoints to further emphasize the retained critical image features.
Spatial-Temporal Decoupling Contrastive Learning for Skeleton-based Human Action Recognition
Furthermore, to explicitly exploit the latent data distributions, we employ the attentive features to contrastive learning, which models the cross-sequence semantic relations by pulling together the features from the positive pairs and pushing away the negative pairs.
BiHRNet: A Binary high-resolution network for Human Pose Estimation
On the challenging of COCO dataset, the proposed method enables the binary neural network to achieve 70. 8 mAP, which is better than most tested lightweight full-precision networks.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition.
Physics-constrained Attack against Convolution-based Human Motion Prediction
Specifically, we introduce a novel adaptable scheme that facilitates the attack to suit the scale of the target pose and two physical constraints to enhance the naturalness of the adversarial example.
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios
Recent works rely on elaborate target-agnostic parsing of audio-visual scenes for spatial grounding while mistreating audio and video as separate entities for temporal grounding.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+3
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition
MLP-T is used to model the temporal relation between different frames for each actor.
SDVRF: Sparse-to-Dense Voxel Region Fusion for Multi-modal 3D Object Detection
To solve these two problems, we present a new concept, Voxel Region (VR), which is obtained by projecting the sparse local point clouds in each voxel dynamically.
Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation
In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE).
An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop
This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop.
Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows
This work introduces a new task of instance-incremental scene graph generation: Given a scene of the point cloud, representing it as a graph and automatically increasing novel instances.
An end-to-end multi-scale network for action prediction in videos
Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales. Therefore, we solve the complexity problems of the two stages of modeling and the problem of insufficient temporal and spatial information of a single scale.
Leveraging the Video-level Semantic Consistency of Event for Audio-visual Event Localization
In contrast to existing methods, we propose a novel video-level semantic consistency guidance network for the AVE localization task.
Kinematics Modeling Network for Video-based Human Pose Estimation
In this work, we propose a plug-and-play kinematics modeling module (KMM) to explicitly model temporal correlations between joints across different frames by calculating their temporal similarity.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Past and Future Motion Guided Network for Audio Visual Event Localization
In recent years, audio-visual event localization has attracted much attention.
Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction
The key of DSTD-GC is constrained dynamic correlation modeling, which explicitly parameterizes the common static constraints as a spatial/temporal vanilla adjacency matrix shared by all frames/joints and dynamically extracts correspondence variances for each frame/joint with an adjustment modeling function.
Rich Action-semantic Consistent Knowledge for Early Action Prediction
Next, we build a bi-directional semantic graph for the teacher network and a single-directional semantic graph for the student network to model rich ASCK among partial videos.
Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Clouds
Specifically, the NSA-MC dropout samples the model many times through a space-dependent way, outputting point-wise distribution by aggregating stochastic inference results of neighbors.
Real-World Semantic Grasp Detection Based on Attention Mechanism
And we also design a target feature attention mechanism to guide the model focus on the features of target object ontology for grasp prediction according to the semantic information.
Amodal segmentation just like doing a jigsaw
This jigsaw method can better model the occlusion relationship and use the occlusion context information, which is important for amodal segmentation.
Uncertainty-aware Human Motion Prediction
It is far more enough for current approaches in actual scenarios because people can't know how to interact with the machine without the evaluation of prediction, and unreliable predictions may mislead the machine to harm the human.
Relation-Based Associative Joint Location for Human Pose Estimation in Videos
Moreover, the JRE can infer invisible joints according to the relationship between joints, which is beneficial for the model to locate occluded joints.
An Attractor-Guided Neural Networks for Skeleton-Based Human Motion Prediction
However, the global coordination of all joints, which reflects human motion's balance property, is usually weakened because it is learned from part to whole progressively and asynchronously.
Temporal Consistency Two-Stream CNN for Human Motion Prediction
In this paper, we propose a novel temporal fusion (TF) module to fuse the two-stream joints' information to predict human motion, including a temporal concatenation and a reinforcement trajectory spatial-temporal (TST) block, specifically designed to keep prediction temporal consistency.
Mask-GD Segmentation Based Robotic Grasp Detection
MASK is a segmented image that only contains the pixels of the target object.
Robotics
Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction
A multi-grained trajectory graph convolutional networks based and lightweight framework is proposed for habit-unrelated human motion prediction.
SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction
Predicting future human motion is critical for intelligent robots to interact with humans in the real world, and human motion has the nature of multi-granularity.
DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
In contrast to prior works, we improve the multi-order modeling ability of human motion systems for more accurate predictions by building a deep state-space model (DeepSSM).
Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos
Our work features two-fold: 1) An important insight that deep features extracted for action recognition can well model the self-similarity periodicity of the repetitive action is presented.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.
PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction
Specifically, a skeletal representation is proposed by transforming the joint coordinate sequence into an image sequence, which can model the different correlations of different joints.
Ranked #1 on
Pose Prediction
on Filtered NTU RGB+D
DWnet: Deep-Wide Network for 3D Action Recognition
We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions.
