Concerns with enabling computers to interpret and understand visual inputs, such as images and videos.
The paper presents an innovative automated system for generating architectural schematic designs. This system is designed to streamline complex decision-making processes at the outset of multifamily real estate development projects….
Continue readingThe article introduces Synchformer, a new model for audio-visual synchronization focused on ‘in-the-wild’ videos, such as those found on YouTube, where synchronization cues can be sparse. The authors propose a…
Continue readingThe research presents AMuSE (Adaptive Multimodal Analysis for Speaker Emotion), a model developed for recognizing individual emotions in group conversations. This model is crucial in developing intelligent agents for natural…
Continue readingThe paper introduces a new kind of adversarial attack on machine learning models, specifically neural networks. It focuses on the vulnerability of these models to adversarial attacks. The proposed attack…
Continue readingThe study focuses on the development of an attack-resilient Generative Adversarial Network (AR-GAN) for traffic sign classification in autonomous vehicles (AVs). AVs rely on Deep Neural Network (DNN)-based systems to…
Continue readingAVs utilize deep neural network (DNN)-based classification systems to identify traffic signs. However, these models are susceptible to adversarial attacks that can cause misclassification by introducing slight perturbations to an…
Continue readingThis article presents two frameworks for combating issues with fine-grained vehicle recognition (FGVR) caused by image noise. The first, a progressive multi-task anti-noise learning (PMAL) framework, improves recognition accuracy by…
Continue readingThe article discusses AR-GAN, a Generative Adversarial Network-based defense method against adversarial attacks on the Traffic Sign Classification System of Autonomous Vehicles. The AR-GAN classification system includes a generator that…
Continue readingThe article presents Sketch2NeRF, a multi-view sketch-guided text-to-3D generation framework. Unlike previous text-to-3D approaches that lack fine-grained control, Sketch2NeRF utilizes sketches to offer such control. The method uses pretrained 2D…
Continue readingThis study introduces POUR-Net, a population-prior-aided over-under-representation network for generating high-quality attenuation maps from low-dose PET scans. The aim is to minimize radiation exposure in PET imaging, which is a…
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