6 papers
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Jingren Liu, Zhong Ji, YunLong Yu +4
Parameter-efficient fine-tuning for continual learning (PEFT-CL) has shown promise in adapting pre-trained models to sequential tasks while mitigating catastrophic forgetting probl…
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts
Zhong Ji, Rongshuai Wei, Jingren Liu +2
Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable, but they often struggle in data-scarce s…
iEBAKER: Improved Remote Sensing Image-Text Retrieval Framework via Eliminate Before Align and Keyword Explicit Reasoning
Yan Zhang, Zhong Ji, Changxu Meng +2
Recent studies focus on the Remote Sensing Image-Text Retrieval (RSITR), which aims at searching for the corresponding targets based on the given query. Among these efforts, the ap…
Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification
Zhong Ji, Ci Liu, Jingren Liu +3
Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples. Existing methods typically emphasiz…
Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning
Hongsheng Zhang, Zhong Ji, Jingren Liu +2
Vision Language Models (VLMs), pre-trained on large-scale image-text datasets, enable zero-shot predictions for unseen data but may underperform on specific unseen tasks. Continual…
A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization
Zhong Ji, Shuo Yang, Jingren Liu +2
Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data. However, current approaches inadequately address the intrinsic optimizatio…