1 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
RestorerID: Towards Tuning-Free Face Restoration with ID Preservation
Jiacheng Ying, Mushui Liu, Zhe Wu +7
Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradat…
Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision
Weijie He, Mushui Liu, Yunlong Yu
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter. In t…
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…
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…