9 papers
Detect in Any Scene: An Agentic Framework for Object Detection with Experience-Aware Reasoning
Wenlun Zhang, Jun Yin, Kentaro Yoshioka
Object detection in real-world scenarios remains challenging due to diverse image degradations and heterogeneous object distributions, which significantly hinder the generalization…
AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization
Wenlun Zhang, Yunshan Zhong, Weiqi Yan +3
The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massive parameter scale and high computation…
Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing
Ryo Yoshida, Takami Sato, Wenlun Zhang +7
LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind Li…
D4C: Data-Free Quantization for Contrastive Language-Image Pre-training Models
Wenlun Zhang, Yunshan Zhong, Zihao Ding +2
Data-Free Quantization (DFQ) offers a practical solution for model compression without requiring access to real data, making it particularly attractive in privacy-sensitive scenari…
BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference
Wenlun Zhang, Xinyu Li, Shimpei Ando +1
Compute-in-Read-Only-Memory (CiROM) accelerators offer outstanding energy efficiency for CNNs by eliminating runtime weight updates. However, their scalability to Large Language Mo…
ASiM: Modeling and Analyzing Inference Accuracy of SRAM-Based Analog CiM Circuits
Wenlun Zhang, Shimpei Ando, Yung-Chin Chen +1
SRAM-based Analog Compute-in-Memory (ACiM) demonstrates promising energy efficiency for deep neural network (DNN) processing. Nevertheless, efforts to optimize efficiency frequentl…