88 citations · 179 across the 11 of their papers we have counts for
12 papers
CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
Yunxiang Fu, Meng Lou, Zicheng Liao +1
Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a data stream without catastrophi…
One Adapter, Many Tasks: Task-Conditioned Feature Transformations for Continual Learning
Yunxiang Fu, Meng Lou, Yizhou Yu
Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to reco…
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
Meng Lou, Hanzhong Guo, Linwei Chen +1
Recent studies suggest that Reinforcement Fine-Tuning (RFT) is inherently more resilient to catastrophic forgetting than Supervised Fine-Tuning (SFT). However, whether RFT (e.g., G…
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
Boyang Dai, Zeng Fan, Zihao Qi +2
Source-Free Domain Adaptive Object Detection (SF-DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data.…
Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing
Meng Lou, Stanley Yu, Yizhou Yu
Adapting pre-trained vision models using parameter-efficient fine-tuning (PEFT) remains challenging, as it aims to achieve performance comparable to full fine-tuning using a minima…
Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
Meng Lou, Yunxiang Fu, Yizhou Yu
Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how…
A2Mamba: Attention-augmented State Space Models for Visual Recognition
Meng Lou, Yunxiang Fu, Yizhou Yu
Transformers and Mamba, initially invented for natural language processing, have inspired backbone architectures for visual recognition. Recent studies integrated Local Attention T…
OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels
Meng Lou, Yizhou Yu
Top-down attention plays a crucial role in the human vision system, wherein the brain initially obtains a rough overview of a scene to discover salient cues (i.e., overview first),…
SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation
Yunxiang Fu, Meng Lou, Yizhou Yu
High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods str…
SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer Networks
Meng Lou, Yunxiang Fu, Yizhou Yu
Due to the capability of dynamic state space models (SSMs) in capturing long-range dependencies with linear-time computational complexity, Mamba has shown notable performance in NL…
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
Meng Lou, Hanning Ying, Xiaoqing Liu +3
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transforme…
TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
Meng Lou, Shu Zhang, Hong-Yu Zhou +3
Recent studies have integrated convolutions into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolu…