Search Results for author:
Found 10 papers, 3 papers with code
Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs
To overcome these limitations, we propose an agent-centric personalized clustering framework that leverages multi-modal large language models (MLLMs) as agents to comprehensively traverse a relational graph to search for clusters based on user interests.
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Using STE, we systematically compare implicit and explicit temporal modeling across dimensions such as overall performance, token compression effectiveness, and temporal-specific understanding.
Evaluating and Advancing Multimodal Large Language Models in Ability Lens
To address this, we introduce \textbf{AbilityLens}, a unified benchmark designed to evaluate MLLMs across six key perception abilities, focusing on both accuracy and stability, with each ability encompassing diverse question types, domains, and metrics.
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks.
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization
may change model output and make the output hallucinate again.
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
To achieve this, we introduce a pre-trained LLM to generate context descriptions, and we encourage the prompts to learn from the LLM's knowledge by alignment, as well as the alignment between prompts and local image features.
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM).
HIRL: A General Framework for Hierarchical Image Representation Learning
This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained.
HCSC: Hierarchical Contrastive Selective Coding
In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space.
Cross-category Video Highlight Detection via Set-based Learning
For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework.
