71 citations · 172 across the 38 of their papers we have counts for
11 papers · 1 filter
Closing the Oracle Gap: Increment Vector Transformation for Class Incremental Learning
Zihuan Qiu, Yi Xu, Fanman Meng +4
Class Incremental Learning (CIL) aims to sequentially acquire knowledge of new classes without forgetting previously learned ones. Despite recent progress, current CIL methods stil…
Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity
Zihuan Qiu, Lei Wang, Yang Cao +7
Data-free continual model merging (DFCMM) aims to fuse independently fine-tuned models into a single backbone that evolves with incoming tasks without accessing task data. This pap…
DFR: A Decompose-Fuse-Reconstruct Framework for Multi-Modal Few-Shot Segmentation
Shuai Chen, Fanman Meng, Xiwei Zhang +4
This paper presents DFR (Decompose, Fuse and Reconstruct), a novel framework that addresses the fundamental challenge of effectively utilizing multi-modal guidance in few-shot segm…
SS-DC: Spatial-Spectral Decoupling and Coupling Across Visible-Infrared Gap for Domain Adaptive Object Detection
Xiwei Zhang, Chunjin Yang, Yiming Xiao +2
Unsupervised domain adaptive object detection (UDAOD) from the visible domain to the infrared (RGB-IR) domain is challenging. Existing methods regard the RGB domain as a unified do…
Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection
Taijin Zhao, Heqian Qiu, Yu Dai +4
Few-shot object detection (FSOD) aims to detect objects with limited samples for novel classes, while relying on abundant data for base classes. Existing FSOD approaches, predomina…
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion
Yukun Chen, Zihuan Qiu, Fanman Meng +3
Unlike traditional Multimodal Class-Incremental Learning (MCIL) methods that focus only on vision and text, this paper explores MCIL across vision, audio and text modalities, addre…
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate
Chenhao Wu, Qingbo Wu, Haoran Wei +5
With the help of powerful generative models, Semantic Image Compression (SIC) has achieved impressive performance at ultra-low bitrate. However, due to coarse-grained visual-semant…
MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
Zihuan Qiu, Yi Xu, Chiyuan He +4
Continual model merging integrates independently fine-tuned models sequentially without access to the original training data, offering a scalable and efficient solution for continu…
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…
Unsupervised Ego- and Exo-centric Dense Procedural Activity Captioning via Gaze Consensus Adaptation
Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +3
Even from an early age, humans naturally adapt between exocentric (Exo) and egocentric (Ego) perspectives to understand daily procedural activities. Inspired by this cognitive abil…
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…