1 citations · 3 across the 10 of their papers we have counts for
11 papers
FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration
Jingren Liu, Shuning Xu, Qirui Yang +3
All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-s…
TeleEgo: Benchmarking Egocentric AI Assistants in the Wild
Jiaqi Yan, Ruilong Ren, Jingren Liu +13
Egocentric AI assistants in real-world settings must process multi-modal inputs (video, audio, text), respond in real time, and retain evolving long-term memory. However, existing…
Video-EM: Event-Centric Episodic Memory for Long-Form Video Understanding
Yun Wang, Long Zhang, Jingren Liu +9
Video Large Language Models (Video-LLMs) have shown strong video understanding, yet their application to long-form videos remains constrained by limited context windows. A common w…
Infinite Video Understanding
Dell Zhang, Xiangyu Chen, Jixiang Luo +6
The rapid advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have ushered in remarkable progress in video understanding. However, a fundamental ch…
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…
CCD: Continual Consistency Diffusion for Lifelong Generative Modeling
Jingren Liu, Shuning Xu, Yun Wang +2
While diffusion-based models have shown remarkable generative capabilities in static settings, their extension to continual learning (CL) scenarios remains fundamentally constraine…
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…
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…
NTK-Guided Few-Shot Class Incremental Learning
Jingren Liu, Zhong Ji, Yanwei Pang +1
The proliferation of Few-Shot Class Incremental Learning (FSCIL) methodologies has highlighted the critical challenge of maintaining robust anti-amnesia capabilities in FSCIL learn…