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Found 41 papers, 14 papers with code
2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting
To address these problems, we propose 2DGS-Avatar, a novel approach based on 2D Gaussian Splatting (2DGS) for modeling animatable clothed avatars with high-fidelity and fast training performance.
BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation
To this end, we propose BloomScene, a lightweight structured 3D Gaussian splatting for crossmodal scene generation, which creates diverse and high-quality 3D scenes from text or image inputs.
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
Recent advancements in multimodal pre-training models have significantly advanced computational pathology.
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.
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions
It trains a common policy with role embedding observations and employs a role predictor to estimate the joint role embeddings of other agents, helping the learning agent adapt to its assigned role.
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.
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
However, this paradigm relies on the use of a large number of labelled WSIs for training.
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.
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.
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.
Large Vision-Language Models as Emotion Recognizers in Context Awareness
Extensive experiments and analyses demonstrate that LVLMs achieve competitive performance in the CAER task across different paradigms.
Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training
To address the issue, we embrace causal inference to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a customized causal graph.
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.
Skip and Skip: Segmenting Medical Images with Prompts
Most medical image lesion segmentation methods rely on hand-crafted accurate annotations of the original image for supervised learning.
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.
Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images
Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images.
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
+2
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data.
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.
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.
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.
HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images
KGC extracts hand prior information from 2D hand pose by graph convolution.
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
A total of 6 teams from 4 countries participated, whose proposed methods were evaluated on 16 images and two preoperative 3D models from two patients.
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA
Medical visual question answering (VQA) is a challenging multimodal task, where Vision-Language Pre-training (VLP) models can effectively improve the generalization performance.
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training
For the first time, this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR.
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.
Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey
This paper comprehensively reviews the application of implicit neural representation in human body modeling.
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.
Towards Simultaneous Segmentation of Liver Tumors and Intrahepatic Vessels via Cross-attention Mechanism
Accurate visualization of liver tumors and their surrounding blood vessels is essential for noninvasive diagnosis and prognosis prediction of tumors.
Achieving a Given Financial Goal with Optimal Deferred Term Insurance Purchasing Policy
It is worth noting that when m=0, n \rightarrow \infty, our problem is equivalent to achieving the just mentioned bequest goal by purchasing whole life insurance, at which point the maximum probability and the life insurance purchasing strategies we provide are consistent with those in \cite{Bayraktar2014, Bayraktar2016}.
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space
Considering that the pose estimator is sensitive to background interference, this paper proposes a counterfactual analysis framework named CASpaceNet to complete robust 6D pose estimation of the spaceborne targets under complicated background.
A Survey of Video-based Action Quality Assessment
Most of the existing work focuses on sports and medical care.
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
Specifically, we introduce a single object tracker into AQA and propose the Tube Self-Attention Module (TSA), which can efficiently generate rich spatio-temporal contextual information by adopting sparse feature interactions.
Sample Efficient Imitation Learning via Reward Function Trained in Advance
Our method, which we call \textit{Model Reward Function Based Imitation Learning} (MRFIL), uses an ensemble dynamic model as a reward function, what is trained with expert demonstrations.
A Unified Joint Matrix Factorization Framework for Data Integration
In this paper, we introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration.
