Search Results for author:
Found 21 papers, 7 papers with code
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction.
MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration
Large Language Model (LLM)-driven interactive systems currently show potential promise in healthcare domains.
Improving Factuality in Large Language Models via Decoding-Time Hallucinatory and Truthful Comparators
In this paper, we propose a Comparator-driven Decoding-Time (CDT) framework to alleviate the response hallucination.
HybridOcc: NeRF Enhanced Transformer-based Multi-Camera 3D Occupancy Prediction
This paper proposes HybridOcc, a hybrid 3D volume query proposal method generated by Transformer framework and NeRF representation and refined in a coarse-to-fine SSC prediction framework.
MaskBEV: Towards A Unified Framework for BEV Detection and Map Segmentation
However, current multimodal perception research follows independent paradigms designed for specific perception tasks, leading to a lack of complementary learning among tasks and decreased performance in multi-task learning (MTL) due to joint training.
Faster Diffusion Action Segmentation
However, the heavy sampling steps required by diffusion models pose a substantial computational burden, limiting their practicality in real-time applications.
Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations
To tackle these issues, we propose a Multimodal fusion approach for learning modality-Exclusive and modality-Agnostic representations (MEA) to refine multimodal features and leverage the complementarity across distinct modalities.
CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report Generation
Automatic medical report generation (MRG), which possesses significant research value as it can aid radiologists in clinical diagnosis and report composition, has garnered increasing attention.
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
To bridge this gap, we introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation within the medical multimodal domain.
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery.
SMCD: High Realism Motion Style Transfer via Mamba-based Diffusion
To address these problems, we consider style motion as a condition and propose the Style Motion Conditioned Diffusion (SMCD) framework for the first time, which can more comprehensively learn the style features of motion.
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Specifically, we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics.
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models
In the realm of Medical Visual Language Models (Med-VLMs), the quest for universal efficient fine-tuning mechanisms remains paramount, especially given researchers in interdisciplinary fields are often extremely short of training resources, yet largely unexplored.
Medical Visual Question Answering
parameter-efficient fine-tuning
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Can LLMs' Tuning Methods Work in Medical Multimodal Domain?
In this paper, we delve into the fine-tuning methods of LLMs and conduct extensive experiments to investigate the impact of fine-tuning methods for large models on the existing multimodal model in the medical domain from the training data level and the model structure level.
Robust Emotion Recognition in Context Debiasing
Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias.
Towards Multimodal Sentiment Analysis Debiasing via Bias Purification
In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases.
Debiased Multimodal Understanding for Human Language Sequences
Human multimodal language understanding (MLU) is an indispensable component of expression analysis (e. g., sentiment or humor) from heterogeneous modalities, including visual postures, linguistic contents, and acoustic behaviours.
HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images
KGC extracts hand prior information from 2D hand pose by graph convolution.
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception
In this paper, we propose SCOPE, a novel collaborative perception framework that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner.
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Driver distraction has become a significant cause of severe traffic accidents over the past decade.
Context De-confounded Emotion Recognition
However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios.
