4 papers
Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
Donghyun Lee, Yuhang Li, Ruokai Yin +2
State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models gro…
OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts
Shiting Xiao, Rishabh Kabra, Yuhang Li +3
The ability to segment objects based on open-ended language prompts remains a critical challenge, requiring models to ground textual semantics into precise spatial masks while hand…
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
Yuhang Li, Ruokai Yin, Donghyun Lee +2
We introduce GPTAQ, a novel finetuning-free quantization method for compressing large-scale transformer architectures. Unlike the previous GPTQ method, which independently calibrat…
Spiking Transformer with Spatial-Temporal Attention
Donghyun Lee, Yuhang Li, Youngeun Kim +2
Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results throu…