38 citations · 82 across the 20 of their papers we have counts for
14 papers · 1 filter
Dual-Mode Low-Rank Learner with Bridge-Prototype Ensemble for Vision-Language Class-Incremental Learning
Chiyuan He, Zihuan Qiu, Fanman Meng +5
Benefiting from transferable visual-textual alignment, CLIP has been widely adopted for class-incremental learning (CIL). However, existing learners either repeatedly update compon…
SAVA-X: Ego-to-Exo Imitation Error Detection via Scene-Adaptive View Alignment and Bidirectional Cross View Fusion
Xiang Li, Heqian Qiu, Lanxiao Wang +4
Error detection is crucial in industrial training, healthcare, and assembly quality control. Most existing work assumes a single-view setting and cannot handle the practical case w…
Continual Learning with Vision-Language Models via Semantic-Geometry Preservation
Chiyuan He, Zihuan Qiu, Fanman Meng +4
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cros…
CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation
Shuai Chen, Fanman Meng, Liming Lei +5
Few-shot segmentation (FSS) aims to segment new classes using few annotated images. While recent FSS methods have shown considerable improvements by leveraging Segment Anything Mod…
DesCLIP: Robust Continual Learning via General Attribute Descriptions for VLM-Based Visual Recognition
Chiyuan He, Zihuan Qiu, Fanman Meng +3
Continual learning of vision-language models (VLMs) focuses on leveraging cross-modal pretrained knowledge to incrementally adapt to expanding downstream tasks and datasets, while…
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
Linfeng Xu, Fanman Meng, Qingbo Wu +16
The application of activity recognition in the ``AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in man…
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
Yilei Qian, Kanglei Geng, Kailong Chen +5
The application of activity recognition in the "AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manu…
Region Prompt Tuning: Fine-grained Scene Text Detection Utilizing Region Text Prompt
Xingtao Lin, Heqian Qiu, Lanxiao Wang +3
Recent advancements in prompt tuning have successfully adapted large-scale models like Contrastive Language-Image Pre-trained (CLIP) for downstream tasks such as scene text detecti…
No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentation
Shuai Chen, Fanman Meng, Chenhao Wu +5
Few-Shot Segmentation (FSS) aims to segment novel classes using only a few annotated images. Despite considerable progress under pixel-wise support annotation, current FSS methods…
Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration
Lei Wang, Qingbo Wu, Desen Yuan +4
Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the…
Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond
Chen Shuai, Meng Fanman, Zhang Runtong +4
Few-shot segmentation (FSS) aims to segment the novel classes with a few annotated images. Due to CLIP's advantages of aligning visual and textual information, the integration of C…
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
Linfeng Xu, Qingbo Wu, Lili Pan +6
With the rapid development of wearable cameras, a massive collection of egocentric video for first-person visual perception becomes available. Using egocentric videos to predict fi…
Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment
Rui Ma, Qingbo Wu, King Ngi Ngan +3
Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation…
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
Haoran Wei, Qingbo Wu, Hui Li +4
Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fort…