Search Results for author:
Found 34 papers, 14 papers with code
EmotiveTalk: Expressive Talking Head Generation through Audio Information Decoupling and Emotional Video Diffusion
Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation.
AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data
Address rewriting has emerged as a solution to rectify these abnormal addresses.
InLINE: Inner-Layer Information Exchange for Multi-task Learning on Heterogeneous Graphs
However, MTL introduces the issue of negative transfer, where the training of different tasks interferes with each other as they may focus on different information from the data, resulting in suboptimal performance.
Scale Propagation Network for Generalizable Depth Completion
Although deep learning based methods have made tremendous progress in this problem, these models cannot generalize well across different scenes that are unobserved in training, posing a fundamental limitation that yet to be overcome.
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-Attention
In multimodal sentiment analysis, collecting text data is often more challenging than video or audio due to higher annotation costs and inconsistent automatic speech recognition (ASR) quality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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HypomimiaCoach: An AU-based Digital Therapy System for Hypomimia Detection & Rehabilitation with Parkinson's Disease
To investigate this, we developed HypomimaCoach, an Action Unit (AU)-based digital therapy system for hypomimia detection and rehabilitation in Parkinson's disease.
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.
UBENCH: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
The rapid development of large language models (LLMs) has shown promising practical results.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention.
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition
In this paper, we investigate this contrasting phenomenon from the perspective of modality bias and reveal that an excessive modality bias on the audio caused by dropout is the underlying reason.
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees
In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain.
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
First, we involve a shared retrieval knowledge pool in the debate process to solve the problem of limited and different knowledge backgrounds.
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations.
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data
This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes.
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
We hope this paper serves as an introduction for beginners and fosters future research.
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023
Three different structures based on attention-guided feature gathering (AFG) are designed for deep feature fusion.
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations.
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
In reality, agents often have to learn and maximize the rewards of the resources at the same time.
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.
Enhanced Sharp-GAN For Histopathology Image Synthesis
To address the challenges, we propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization.
Domain Specified Optimization for Deployment Authorization
As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO).
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered nuclei.
On Cyclic Solutions to the Min-Max Latency Multi-Robot Patrolling Problem
For the Euclidean version of the problem, for instance, combining our results with known results on Euclidean TSP, yields a PTAS for approximating an optimal cyclic solution, and it yields a $(2(1-1/k)+\varepsilon)$-approximation of the optimal unrestricted solution.
Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis
Conditional generative adversarial networks have been applied to generate synthetic histopathology images to alleviate this issue, but current approaches fail to generate clear contours for overlapped and touching nuclei.
TA-Net: Topology-Aware Network for Gland Segmentation
The proposed topology loss computes gland topology using gland skeletons and markers.
Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images
Separating overlapped nuclei is a major challenge in histopathology image analysis.
Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection
Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production.
Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation
Both qualitative and quantitative results demonstrate that, in the presence of major pathology, the proposed method is more accurate and robust than state-of-the-art methods.
Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus.
Social and Information Networks Physics and Society
Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency
The problem is NP-hard, as it has the traveling salesman problem as a special case (when $k=1$ and all sites have the same weight).
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
Separating overlapped nuclei is a major challenge in histopathology image analysis.
