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Found 26 papers, 10 papers with code
Adapting World Models with Latent-State Dynamics Residuals
Simulation-to-reality reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade agent performance.
Guiding Human-Object Interactions with Rich Geometry and Relations
Human-object interaction (HOI) synthesis is crucial for creating immersive and realistic experiences for applications such as virtual reality.
MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis
Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge.
Discovering Influential Neuron Path in Vision Transformers
In this paper, we investigate the significance of influential neuron paths within vision Transformers, which is a path of neurons from the model input to output that impacts the model inference most significantly.
SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball Tactic
This framework aligns with the feature extraction paradigm in RGB-based methods, enabling direct evaluation of RGB-based models on skeleton-based benchmarks.
Group Activity Recognition
Temporal Group Activity Localization
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior models.
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment
Decoding human activity from EEG signals has long been a popular research topic.
MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks
While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality.
BioTD: an online database of biotoxins
Biotoxins, mainly produced by venomous animals, plants and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia.
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits).
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications.
Xinyu: An Efficient LLM-based System for Commentary Generation
To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology.
Multibeam Hybrid Transmitarray Based on Polarization Rotating Metasurface With Reconfigurable Bidirectional Radiation
To accomplish the reconfiguration of both unidirectional and bidirectional radiation states in the +z, -z, and +/-z directions, a polarization switchable multi-feed array (MFA) is placed at the focal plane between the TA and FTA, radiating x-polarization, y-polarization, and 45-degree oblique polarization waves, respectively.
Enabling Visual Recognition at Radio Frequency
This paper introduces PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals.
Towards Variable and Coordinated Holistic Co-Speech Motion Generation
This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars, focusing on two key aspects: variability and coordination.
Enhancing Court View Generation with Knowledge Injection and Guidance
Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions.
Boosting Data Analytics With Synthetic Volume Expansion
This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data.
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
FireFly A Synthetic Dataset for Ember Detection in Wildfire
This paper presents "FireFly", a synthetic dataset for ember detection created using Unreal Engine 4 (UE4), designed to overcome the current lack of ember-specific training resources.
Revisiting Token Pruning for Object Detection and Instance Segmentation
In relation to the dense counterpart that utilizes all tokens, our method realizes an increase in inference speed, achieving up to 34% faster performance for the entire network and 46% for the backbone.
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification
The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models.
Crop mapping in the small sample/no sample case: an approach using a two-level cascade classifier and integrating domain knowledge
To solve this problem, a crop mapping method in the small sample/no sample case that integrating domain knowledge and using a cascaded classification framework that combine a weak classifier learned from samples with strong features and a strong classifier trained by samples with weak feature was proposed.
Generating Holistic 3D Human Motion from Speech
This work addresses the problem of generating 3D holistic body motions from human speech.
Ranked #3 on
3D Face Animation
on BEAT2
Multi-objective Explanations of GNN Predictions
We design a user study to investigate such joint effects and use the findings to design a multi-objective optimization (MOO) algorithm to find Pareto optimal explanations that are well-balanced in simulatability and counterfactual.
Rigorous Explanation of Inference on Probabilistic Graphical Models
There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models.
Scalable Explanation of Inferences on Large Graphs
Probabilistic inferences distill knowledge from graphs to aid human make important decisions.
