Enhancing Multimodal Representation Learning through Dynamic Anchor Alignment
The core message of this paper is that dynamic anchor-based multimodal representation learning, as proposed in the CentroBind method, can effectively capture intra-modal, inter-modal, and multimodal alignment information, overcoming the limitations of fixed anchor-based approaches like ImageBind.
OMG-LLaVA: A Unified Model for Image-level, Object-level, and Pixel-level Understanding and Reasoning
OMG-LLaVA is a new and elegant framework that combines powerful pixel-level vision understanding with reasoning abilities, enabling it to accept various visual and text prompts for flexible user interaction.
Robust Multimodal Sentiment Analysis with Incomplete Data
The proposed Language-dominated Noise-resistant Learning Network (LNLN) enhances the robustness of multimodal sentiment analysis by preserving the integrity of the dominant language modality under various noise scenarios.
Comprehensive Review of Multimodal Large Language Models: Architectures, Benchmarks, and Emerging Trends
Multimodal Large Language Models (MLLMs) represent a significant advancement in artificial intelligence, enabling machines to process and generate content across multiple modalities, including text, images, audio, and video. This survey synthesizes key insights from existing literature to provide a comprehensive overview of MLLM architectures, evaluation methodologies, applications, and emerging trends.
다양한 비디오 환경에서 강건한 오디오-비주얼 음성 인식 모델
비주얼 정보를 활용하여 다양한 비디오 환경에서 강건한 음성 인식 성능을 달성하는 오디오-비주얼 음성 인식 모델 EVA를 제안한다.
Enhancing Audio-Visual Representation Learning by Incorporating Object Information
Incorporating object information from audio and visual modalities can enhance audio-visual representation learning and improve performance on tasks such as audio-visual retrieval and classification.
다양한 모달리티 부재 상황에서도 강건한 멀티모달 학습을 위한 매개변수 효율적 적응 기법
다양한 모달리티 부재 상황에서도 강건한 멀티모달 학습을 위해 매개변수 효율적 적응 기법을 제안한다. 중간 특징 변조를 통해 부재 모달리티를 보상하여 성능 저하를 부분적으로 해결할 수 있다.
Enhancing Robustness of Multimodal Learning Models to Handle Missing Modalities via Parameter-Efficient Adaptation
Multimodal learning models can be made robust to missing modalities through a simple and parameter-efficient adaptation procedure that modulates the intermediate features of available modalities to compensate for the missing ones.
A Lightweight Framework for Progressive Alignment of Image, Text, Audio, and Video Modalities
OneEncoder is a lightweight framework that progressively aligns image, text, audio, and video modalities without relying on vast aligned datasets. It leverages frozen pretrained modality-specific encoders and a compact Universal Projection module to achieve efficient and cost-effective multimodal alignment.
A Comprehensive Survey on Deep Learning Methods for Multimodal Learning with Missing Modalities
Multimodal learning systems often face the challenge of missing or incomplete data in real-world applications. This survey provides a comprehensive overview of recent deep learning techniques that address the problem of Multimodal Learning with Missing Modality (MLMM), including modality augmentation, feature space engineering, architecture engineering, and model selection approaches.
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