Search Results for author:
Found 71 papers, 29 papers with code
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting
The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision.
Vision Generalist Model: A Survey
Recently, we have witnessed the great success of the generalist model in natural language processing.
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation
Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and <observation, action, rationale> history.
OGGSplat: Open Gaussian Growing for Generalizable Reconstruction with Expanded Field-of-View
To evaluate our method, we establish a Gaussian Outpainting (GO) benchmark that assesses both semantic and generative quality of reconstructed open-vocabulary scenes.
CogniPair: From LLM Chatbots to Conscious AI Agents -- GNWT-Based Multi-Agent Digital Twins for Social Pairing -- Dating & Hiring Applications
Current large language model (LLM) agents lack authentic human psychological processes necessary for genuine digital twins and social AI applications.
VUDG: A Dataset for Video Understanding Domain Generalization
Hence, we propose Video Understanding Domain Generalization (VUDG), a novel dataset designed specifically for evaluating the DG performance in video understanding.
HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex Motions
Recent advances in diffusion models have significantly improved conditional video generation, particularly in the pose-guided human image animation task.
StableMotion: Repurposing Diffusion-Based Image Priors for Motion Estimation
We present StableMotion, a novel framework leverages knowledge (geometry and content priors) from pretrained large-scale image diffusion models to perform motion estimation, solving single-image-based image rectification tasks such as Stitched Image Rectangling (SIR) and Rolling Shutter Correction (RSC).
Graph Synthetic Out-of-Distribution Exposure with Large Language Models
Empirical results on multiple benchmarks show that GOE-LLM substantially outperforms state-of-the-art methods without OOD exposure, achieving up to a 23. 5% improvement in AUROC for OOD detection, and attains performance on par with those relying on real OOD labels for exposure.
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
Out-of-distribution (OOD) detection is critical for ensuring the safety and reliability of machine learning systems, particularly in dynamic and open-world environments.
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results.
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
The core contribution of our method is expanding the capability of the recent pairwise reconstruction model to long-term incremental dynamic reconstruction by an uncertainty-aware dual memory mechanism.
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
Multimodal large language models (MLLMs) excel in vision-language tasks but also pose significant risks of generating harmful content, particularly through jailbreak attacks.
Few-Shot Graph Out-of-Distribution Detection with LLMs
Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribution (ID) data.
Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking
However, invisible watermarking for visual autoregressive (VAR) models remains underexplored, despite its importance in misuse prevention.
Cauchy-Schwarz Regularizers
We introduce a novel class of regularization functions, called Cauchy-Schwarz (CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems.
SemGeoMo: Dynamic Contextual Human Motion Generation with Semantic and Geometric Guidance
Generating reasonable and high-quality human interactive motions in a given dynamic environment is crucial for understanding, modeling, transferring, and applying human behaviors to both virtual and physical robots.
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
Pretraining models with unsupervised graph representation learning has led to significant advancements in domains such as social network analysis, molecular design, and electronic design automation (EDA).
Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning
To alleviate the long-existing difficulties brought by sparse rewards in RL, which are even exacerbated by the partial correctness of the long chain of thought for reasoning tasks, we further apply a token-level reward model to sample important tokens in reasoning trajectories for learning.
LLM Safety Alignment is Divergence Estimation in Disguise
We present a theoretical framework showing that popular LLM alignment methods, including RLHF and its variants, can be understood as divergence estimators between aligned (safe or preferred) and unaligned (harmful or less preferred) distributions.
Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of Large Language Models (LLMs) has demonstrated remarkable progress in complex reasoning tasks.
Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection
The global attention component employs global attention on downsampled feature maps to capture low-frequency information, while the local attention component focuses on high-frequency details using non-overlapping window-based local attention.
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation
In our approach, we developed a mask generator based on the denoising UNet from a pre-trained diffusion model, leveraging its capability for precise textual control over dense pixel representations and enhancing the open-world adaptability of the generated masks.
Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning
In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated.
ZIF-90 treats fungal keratitis by promoting macrophage apoptosis and inhibiting inflammatory response
In addition, to investigate whether the metal-ligand zinc and the organic ligand imidazole act as essential factors in ZIF-90, we investigated the in vitro antimicrobial and anti-inflammatory effects of ZIF-8, ZIF-67, and MOF-74 (Zn) by MIC and ELISA experiments.
Zeolitic Imidazolate Framework-8 offers an anti-inflammatory and antifungal method in the treatment of Aspergillus fungus keratitis in vitro and in vivo
Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability.
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM).
Ranked #1 on
Controllable Grasp Generation
on GRAB
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis.
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
Demystify Mamba in Vision: A Linear Attention Perspective
By exploring the similarities and disparities between the effective Mamba and subpar linear attention Transformer, we provide comprehensive analyses to demystify the key factors behind Mamba's success.
Overcoming Medical Overuse with AI Assistance: An Experimental Investigation
We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design.
From ChatGPT, DALL-E 3 to Sora: How has Generative AI Changed Digital Humanities Research and Services?
This article profoundly explores the application of large-scale language models in digital humanities research, revealing their significant potential in ancient book protection, intelligent processing, and academic innovation.
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
In view of this, this study proposes to study and develop a series of efficient non-negative coupled tensor decomposition algorithm frameworks based on federated learning called FCNCP for the EEG data arranged on different servers.
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
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.
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math.
GDN: A Stacking Network Used for Skin Cancer Diagnosis
Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions.
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
This paper investigates low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) with low-precision and full-precision gradient accumulators for both strongly log-concave and non-log-concave distributions.
A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis
More interestingly, although we aim at the synthesis effect of the style transfer model, the synthesized speech by the proposed text prosodic analysis model is even better than the style transfer from the original speech in some user evaluation indicators.
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Ranked #8 on
3D Part Segmentation
on ShapeNet-Part
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet
Bootstrap Generalization Ability from Loss Landscape Perspective
Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i. e., out-of-distribution data, which has different distribution from the training dataset.
Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve.
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning.
Ranked #21 on
3D Part Segmentation
on ShapeNet-Part
AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy
In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information.
Deep Graphic FBSDEs for Opinion Dynamics Stochastic Control
Our approach relies on the probabilistic representation of the solution of the Hamilton-Jacobi-Bellman partial differential equation.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion.
Ranked #1 on
Point Cloud Completion
on ShapeNet
(Chamfer Distance L2 metric)
Towards Interpretable Deep Metric Learning with Structural Matching
Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods.
Ranked #16 on
Metric Learning
on CUB-200-2011
Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms.
LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform
To alleviate this problem, existing user cold start methods either apply deep learning to build a cross-domain recommender system or map user attributes into the space of user behaviour.
Itinerary-aware Personalized Deep Matching at Fliggy
All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred.
Variational Inference MPC using Tsallis Divergence
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds.
Large-Scale Multi-Agent Deep FBSDEs
We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics.
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
In this paper we present a deep learning framework for solving large-scale multi-agent non-cooperative stochastic games using fictitious play.
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search
We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search.
Distributional Reinforcement Learning
Optimization and Control
Robotics
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
This paper introduces a new formulation for stochastic optimal control and stochastic dynamic optimization that ensures safety with respect to state and control constraints.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control
In this work we propose the use of adaptive stochastic search as a building block for general, non-convex optimization operations within deep neural network architectures.
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
Deep Nonlinear Stochastic Optimal Control for Systems with Multiplicative Uncertainties
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
Deep 2FBSDEs For Systems With Control Multiplicative Noise
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differential equations.
Deep Forward-Backward SDEs for Min-max Control
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems.
