18 citations · 60 across the 15 of their papers we have counts for
15 papers
DPL: Decoupled Prompt Learning for Vision-Language Models
Chen Xu, Yuhan Zhu, Guozhen Zhang +5
Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e.g., CLIP) to downstream tasks. However, current methods t…
Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images
Yang Zhang, Le Cheng, Yuting Peng +4
For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from…
Semantic-Aware Mixup for Domain Generalization
Chengchao Xu, Xinmei Tian
Deep neural networks (DNNs) have shown exciting performance in various tasks, yet suffer generalization failures when meeting unknown target domains. One of the most promising appr…
Exploring Data Geometry for Continual Learning
Zhi Gao, Chen Xu, Feng Li +3
Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies…
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
Chen Xu, Yuhan Zhu, Haocheng Shen +4
Prompt learning has been designed as an alternative to fine-tuning for adapting Vision-language (V-L) models to the downstream tasks. Previous works mainly focus on text prompt whi…
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
Saghir Alfasly, Zaid Al-huda, Saifullah Bello +3
Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects…