3 citations · 11 across the 32 of their papers we have counts for
11 papers · 1 filter
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Qiang Wang, Songlin Dong, Shaokun Wang +5
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domai…
SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
Xinyi Fang, Kejian Tong, Jiabei Liu +2
Large scale document deduplication must preserve semantic equivalence while remaining efficient over massive corpora. We present SemHash LLM, a multi granularity framework that uni…
SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference
Amirhossein Abaskohi, Giuseppe Carenini, Peter West +1
Large language models increasingly operate over long contexts, where the KV cache becomes a dominant memory bottleneck: its size grows linearly with sequence length and must be ret…
ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
Amirhossein Abaskohi, Yuhang He, Peter West +3
Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajector…
Beyond World-Frame Action Heads: Motion-Centric Action Frames for Vision-Language-Action Models
Huoren Yang, Jianchao Zhao, Hu Yusong +7
Vision-Language-Action (VLA) models have advanced rapidly with stronger backbones, broader pre-training, and larger demonstration datasets, yet their action heads remain largely ho…
Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery
Jizhou Han, Chenhao Ding, Yuhang He +4
Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual-only pipelines and…
GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
Shiwei Lu, Yuhang He, Jiashuo Li +2
Federated learning (FL) facilitates the secure utilization of decentralized images, advancing applications in medical image recognition and autonomous driving. However, conventiona…
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
Jizhou Han, Chenhao Ding, SongLin Dong +4
Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typic…
Is Parameter Isolation Better for Prompt-Based Continual Learning?
Jiangyang Li, Chenhao Ding, Songlin Dong +4
Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolatin…
IOTA: Corrective Knowledge-Guided Prompt Learning via Black-White Box Framework
Shaokun Wang, Yifan Yu, Yuhang He +2
Recently, adapting pre-trained models to downstream tasks has attracted increasing interest. Previous Parameter-Efficient-Tuning (PET) methods regard the pre-trained model as an op…
P2L-CA: An Effective Parameter Tuning Framework for Rehearsal-Free Multi-Label Class-Incremental Learning
Songlin Dong, Jiangyang Li, Chenhao Ding +4
Multi-label Class-Incremental Learning aims to continuously recognize novel categories in complex scenes where multiple objects co-occur. However, existing approaches often incur h…