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Found 12 papers, 4 papers with code
A Unified Framework for Human-centric Point Cloud Video Understanding
Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further advancing downstream human-centric tasks and applications.
LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment
Language-guided scene-aware human motion generation has great significance for entertainment and robotics.
The All-Seeing Project V2: Towards General Relation Comprehension of the Open World
In addition, we design a new benchmark, termed Circular-based Relation Probing Evaluation (CRPE) for comprehensively evaluating the relation comprehension capabilities of MLLMs.
Ranked #30 on
Visual Question Answering
on MM-Vet
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment
For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications.
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information
Molecular property prediction, crucial for early drug candidate screening and optimization, has seen advancements with deep learning-based methods.
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules.
Ranked #1 on
Image Captioning
on ChEBI-20
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR
Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light.
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index
An approach was developed that minimises risk by combining the Kelly Index with the predefined confidence thresholds of the predictive models.
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors
We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly.
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.
Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism
In this paper, we propose a self-supervised point cloud completion method (TraPCC) for vehicles in real traffic scenes without any complete data.
Train and Deploy an Image Classifier for Disaster Response
With Deep Learning Image Classification becoming more powerful each year, it is apparent that its introduction to disaster response will increase the efficiency that responders can work with.
