12 citations · 25 across the 27 of their papers we have counts for
5 papers · 1 filter
Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
Shihan Wu, Ji Zhang, Pengpeng Zeng +3
Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning…
GT23D-Bench: A Comprehensive General Text-to-3D Generation Benchmark
Xiao Cai, Sitong Su, Jingkuan Song +6
Text-to-3D (T23D) generation has emerged as a crucial visual generation task, aiming at synthesizing 3D content from textual descriptions. Studies of this task are currently shifti…
SeMv-3D: Towards Concurrency of Semantic and Multi-view Consistency in General Text-to-3D Generation
Xiao Cai, Pengpeng Zeng, Lianli Gao +3
General Text-to-3D (GT23D) generation is crucial for creating diverse 3D content across objects and scenes, yet it faces two key challenges: 1) ensuring semantic consistency betwee…
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
Run Luo, Haonan Zhang, Longze Chen +13
The development of Multimodal Large Language Models (MLLMs) has seen significant advancements with increasing demands in various fields (e.g., multimodal agents, embodied intellige…
Text-Video Retrieval with Global-Local Semantic Consistent Learning
Haonan Zhang, Pengpeng Zeng, Lianli Gao +4
Adapting large-scale image-text pre-training models, e.g., CLIP, to the video domain represents the current state-of-the-art for text-video retrieval. The primary approaches involv…