Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a
promising alternative to convolutional neural networks (CNNs) in several vision-related …

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual understanding …

Post-Training Quantization (PTQ) is a vital technique for network compression and
acceleration, gaining prominence as model sizes increase. This paper addresses a critical …

Post-training quantization (PTQ) for vision transformers (ViTs) has garnered significant
attention due to its efficiency in compressing models. However, existing methods typically …

Albeit the scalable performance of vision transformers (ViTs), the dense computational costs
(training & inference) undermine their position in industrial applications. Post-training …

Mixed-precision quantization mostly predetermines the model bit-width settings before
actual training due to the non-differential bit-width sampling process, obtaining suboptimal …

In this paper, we present a simple optimization-based preprocessing technique called
Weight Magnitude Reduction (MagR) to improve the performance of post-training …

Post-training quantization (PTQ) has emerged as a practical approach to compress large
neural networks, making them highly efficient for deployment. However, effectively reducing …

Convolutional neural network (CNN)-based synthetic aperture radar (SAR) ship detection
models operating directly on satellites can reduce transmission latency and improve real …

Abstract Post-Training Quantization (PTQ) has received significant attention because it
requires only a small set of calibration data to quantize a full-precision model, which is more …