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Found 5 papers, 3 papers with code
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.
