Driver attention prediction has gained significant attention recently due to its role in developing advanced driver assistance systems (ADAS) and intelligent vehicles. The emergence of video foundation models (VFMs) has opened up new possibilities for improving video understanding tasks like video saliency prediction (VSP). However, these large models are often not cost-effective for ADAS and intelligent vehicles due to their size and resource demands. To address this, we present an early effort to use knowledge distillation for predicting driver visual attention, employing the first VFM-based VSP model, SalFoM, as the teacher network. Given that driver attention prediction datasets are smaller than those used for large models, fine-tuning such models is challenging due to their high parameter count. To overcome this, we designed a VFM-based driver attention prediction network with fewer parameters than the teacher network. Experimental results show our model’s effectiveness on benchmark datasets.

Recent advancements in diffusion-based image generation and large vision/language models have revolutionized visual brain decoding (VBD), driving progress in neuroscience and brain-computer interfaces. While state-of-the-art models produce high-quality reconstructed images, a significant semantic gap remains between original stimuli and reconstructed images, posing challenges for applications like forensics, medical treatments, and human-robot interactions. This gap arises from VBD models’ limitations in interpreting brain signals and generating accurate representations. To address this, we analyze the issue through the lens of salient object detection, statistically comparing the similarity between visual stimuli and reconstructed images with a focus on salient objects. To our knowledge, this is the first study to evaluate fMRI-based VBD models from this perspective. Our findings provide measurable insights to guide the development of VBD models that align more closely with human perception.

Despite progress in visual reconstruction from fMRI signals, evaluating reconstruction quality remains challenging due to noisy, low-resolution data and semantic ambiguity. Conventional metrics often overlook perceptual and structural alignment with the original stimuli. To address this, we propose Graph-based Semantic and Structural Similarity (GSS), a novel evaluation approach that represents both stimuli and reconstructions as patch-wise graphs using CLIP-derived features. By applying graph matching, GSS captures spatial and semantic relationships beyond pixel-level similarities. Our approach aligns with neuroscientific models of visual processing and demonstrates robust, interpretable results that complement existing metrics.

Abstract Nowadays, because of the undeniable impact of the Internet on all aspects of human life, security preserving has received more attention. To reach an acceptable level of security, Completely Automatic Public Turing test to tell Computer and Human Apart or simply CAPTCHA as a security preserving tool has been tailored for situations that need to prevent bots from doing a specific action; for example, signing up and downloading. Simultaneously, it should be also designed in such a way to allow humans to perform the same action. Despite its advantageous applications, there are several important issues such as security, usability, and accessibility that make its use controversial. In this paper, attempts were made to do a comprehensive review on various aspects and state‐of‐the‐art of CAPTCHA in general and its alternatives in particular to help researchers easily focus on specific issues for the sake of proposing new solutions and ideas. Regarding the advancement in CAPTCHA development, new classifications were proposed to categorize different variations of CAPTCHAs and their problems and then compare them. Moreover, different types of CAPTCHAs' alternatives were classified and evaluated by introducing several proposed measures. This evaluation could come in handy for future studies that aim to develop new techniques for overcoming current deficiencies. Copyright © 2014 John Wiley & Sons, Ltd.