When observers glance upon a natural scene, which aspects of that scene ultimately reach
perceptual awareness? To answer this question, we showed observers images of scenes …

Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications
such as home robots and medical diagnoses. One significant challenge is to devise a robust …

While deep neural network (DNN)-based perception models are useful for many
applications, these models are black boxes and their outputs are not yet well understood. To …

The rapid growth of Large Language Models (LLMs) has driven the development of Large
Vision-Language Models (LVLMs). The challenge of hallucination, prevalent in LLMs, also …

Despite the promising progress in multi-modal tasks, current large multi-modal models
(LMM) are prone to hallucinating inconsistent descriptions with respect to the associated …

Computer vision is an expertise associated with artificial intelligence and image processing
arenas to excerpt meaningful information from visual components like images and videos …

Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses
that apparently contradict to the image content occasionally. The key problem lies in its weak …

Here, we aimed to explain neural representations of perception, for which we analyzed the
relationship between multi-unit activity (MUA) recorded from the primate brain and various …

Determining how impactful an anomaly is on the viewing experience of an audience is
important to production studios, content creators, and content distributors. However, judging …

Over the past few years, AI and machine learning have evolved from research areas
secluded in laboratories far from the public, to technologies deployed on an industrial scale …