6 citations · 6 across the 8 of their papers we have counts for
11 papers
PaLoRA: Paced Low-Rank Adaptation for Continual Learning
Yuxuan Li, Fanhu Zeng, Hao Tang
LoRA-based continual learning methods mitigate catastrophic forgetting through various mechanisms, yet nearly all complement these with small learning rates as a heuristic to restr…
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
Lei Xin, Zeheng Wang, Jiayin Zhu +6
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term developmental outcomes. However, e…
The Token Before the Value Is the Key
Ke Cheng, Xin Xu, Yixiao Chen +7
Induction circuits retrieve a value by matching a query to information carried from the token before that value. How this address forms, incorporates context, and supports sequenti…
UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models
Lei Xin, Bin Gu, Peize Li +8
Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank. W…
LiST: Local-Simplex Test-Time LoRA Fusion
Yihua Shao, Jia Li, Siyu Chen +12
Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot…
MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction
Peize Li, Fanhu Zeng, Tongda Xu +5
In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can…
Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration
Fanhu Zeng, Deli Yu, Zhenglun Kong +1
Vision transformers have been widely explored in various vision tasks. Due to heavy computational cost, much interest has aroused for compressing vision transformer dynamically in…
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
Zhenglun Kong, Yize Li, Fanhu Zeng +7
In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped…
EventVAD: Training-Free Event-Aware Video Anomaly Detection
Yihua Shao, Haojin He, Sijie Li +11
Video Anomaly Detection~(VAD) focuses on identifying anomalies within videos. Supervised methods require an amount of in-domain training data and often struggle to generalize to un…
MambaIC: State Space Models for High-Performance Learned Image Compression
Fanhu Zeng, Hao Tang, Yihua Shao +3
A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational…
PPT: Token Pruning and Pooling for Efficient Vision Transformers
Xinjian Wu, Fanhu Zeng, Xiudong Wang +1
Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computati…