1 citations · 3 across the 6 of their papers we have counts for
6 papers
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
Mushui Liu, Yuhang Ma, Yang Zhen +6
Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving mult…
Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition
Bozheng Li, Mushui Liu, Gaoang Wang +1
In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre-traini…
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
Mushui Liu, Fangtai Wu, Bozheng Li +3
Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing approaches attempt to incorporate semantic information into the limited vi…
OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning
Mushui Liu, Bozheng Li, Yunlong Yu
Recent Vision-Language Models (VLMs) \textit{e.g.} CLIP have made great progress in video recognition. Despite the improvement brought by the strong visual backbone in extracting s…
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Wanggui He, Siming Fu, Mushui Liu +10
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis. In…
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
Mushui Liu, Bozheng Li, Yunlong Yu
Prompt tuning, which involves training a small set of parameters, effectively enhances the pre-trained Vision-Language Models (VLMs) to downstream tasks. However, they often come a…