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Found 20 papers, 6 papers with code
NPC: Neural Predictive Control for Fuel-Efficient Autonomous Trucks
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions.
FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments
We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments.
Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
Multimodal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a broad spectrum of domains.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Towards Practical Human Motion Prediction with LiDAR Point Clouds
In this paper, we propose \textit{LiDAR-HMP}, the first single-LiDAR-based 3D human motion prediction approach, which receives the raw LiDAR point cloud as input and forecasts future 3D human poses directly.
LiveHPS++: Robust and Coherent Motion Capture in Dynamic Free Environment
LiDAR-based human motion capture has garnered significant interest in recent years for its practicability in large-scale and unconstrained environments.
Needle In A Multimodal Haystack
In this work, we present Needle In A Multimodal Haystack (MM-NIAH), the first benchmark specifically designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents.
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 #101 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.
Gait Recognition in Large-scale Free Environment via Single LiDAR
Human gait recognition is crucial in multimedia, enabling identification through walking patterns without direct interaction, enhancing the integration across various media forms in real-world applications like smart homes, healthcare and non-intrusive security.
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.
