activity
20242026
collaborators

20 papers

cs.LG2026

SAFE-Merge: Data-Free Continual Model Merging with General Knowledge Preservation

Zihuan Qiu, Zhiyang Liao, Chiyuan He +5

Data-free continual model merging must incorporate a stream of specialized models while retaining both pretrained general knowledge and previously acquired tasks, without access to…

cs.RO2026

FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking

Hai Jiang, Yixian Zou, Binbin Liang +3

Real-time deployment of Vision-Language-Action (VLA) policies necessitates asynchronous execution, wherein subsequent action chunks are computed concurrently with the execution of…

cs.CV2026

Test-time Ego-Exo-centric Adaptation for Action Anticipation via Multi-Label Prototype Growing and Dual-Clue Consistency

Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +4

Efficient adaptation between Egocentric (Ego) and Exocentric (Exo) views is crucial for applications such as human-robot cooperation. However, the success of most existing Ego-Exo…

cs.CV2026

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…

cs.CV2026

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…

cs.LG2026

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…