In this section, we will describe the evaluation process in detail. We evaluated various L VLMs, including medical-specific models, open-source general models, and closed-source API general models.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

As a cost-effective alternative, learning-free PTQ schemes have been proposed. However, the performance is somewhat limited because they cannot consider the inter-layer dependency within the attention module, which is a significant feature of Transformers. In this paper, we thus propose a novel PTQ algorithm that balances accuracy and efficiency. The key idea of the proposed algorithm called aespa is to perform quantization layer-wise for efficiency while targeting attention-wise reconstruction to consider the cross-layer dependency.

We revisit the Adaptive Stochastic Quantization (ASQ) problem and present algorithms that find optimal solutions with asymptotically improved time and space complexities.

RL benchmark tailored for embodied pixel-based RL.

Sharpness-A ware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural networks.

Under Display Camera (UDC) is a novel imaging system that mounts a digital camera lens beneath a display panel with the panel covering the camera.

If we consider for example the image pictured in Figure 1 on the left, we can easily describe its content by'what' we see - the building, sky and a flag.