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Found 47 papers, 14 papers with code
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.
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.
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.
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.
Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
Prompt learning based on the pre-trained models (\eg, CLIP) appears to be a promising scheme for this setting; however, current research in this area is limited, and existing algorithms often focus solely on patch-level prompts or confine themselves to language prompts.
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.
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.
Self-Cooperation Knowledge Distillation for Novel Class Discovery
Existing works focus on instance-level or class-level knowledge representation and build a shared representation space to achieve performance improvements.
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.
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
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.
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
Multimodal intention understanding (MIU) is an indispensable component of human expression analysis (e. g., sentiment or humor) from heterogeneous modalities, including visual postures, linguistic contents, and acoustic behaviors.
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.
Delving into Decision-based Black-box Attacks on Semantic Segmentation
We conduct adversarial robustness evaluation on 5 models from Cityscapes and ADE20K under 8 attacks.
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.
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation
In particular, CG2A develops a Gradient Agreement Solver to adaptively balance the varying gradient magnitudes, and introduces a Soft Gradient Surgery strategy to alleviate the gradient conflicts.
Sampling to Distill: Knowledge Transfer from Open-World Data
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data.
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.
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.
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT).
Efficient Decision-based Black-box Patch Attacks on Video Recognition
First, STDE introduces target videos as patch textures and only adds patches on keyframes that are adaptively selected by temporal difference.
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.
A novel efficient Multi-view traffic-related object detection framework
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception.
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.
Adversarial Contrastive Distillation with Adaptive Denoising
To this end, we propose a novel structured ARD method called Contrastive Relationship DeNoise Distillation (CRDND).
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos.
Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization
Particularly, when attacking advanced defense methods in the image domain, it achieves an average attack success rate of 95. 4%.
Learning Appearance-motion Normality for Video Anomaly Detection
Video anomaly detection is a challenging task in the computer vision community.
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.
