12 accepted papers
Transfer attacks optimize on a surrogate and deploy to a black-box target. While iterative optimization attacks in this paradigm are limited by their per-input cost limits efficiency and scalability due to multistep gradient updates for each input, generative attacks alleviate these by producing adv…
Text-to-video and image-to-video generation have made rapid progress in visual quality, but they remain limited in controlling the precise timing of motion. In contrast, audio provides temporal cues aligned with video motion, making it a promising condition for temporally controlled video generatio…
Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). The model learns a straight ODE by reflow steps which iteratively update the supervisory flow. It allows for a relatively simple and efficient generat…
In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled more precise control over style and content. However, existing methods often suffer from content leakage, where undesired…
Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition-aligned image generation. CFG combines the conditional score (e.g., text-conditioned) with the unconditional score to c…
"Weakly Supervised Semantic Segmentation (WSSS) with classification labels typically uses Class Activation Maps to localize the object based on Convolutional Neural Networks (CNN). With limited receptive fields, CNN-based CAMs often fail to localize the whole object. The emergence of a Vision Transf…
"Weakly Supervised Semantic Segmentation (WSSS) with image-level supervision typically acquires object localization information from Class Activation Maps (CAMs). While Vision Transformers (ViTs) in WSSS have been increasingly explored for their superior performance in understanding global context,…
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem they suffer from unreliable predictions under distribution shifts during test time.…
"In low-light conditions, capturing videos with frame-based cameras often requires long exposure times, resulting in motion blur and reduced visibility. While frame-based motion deblurring and low-light enhancement have been studied, they still pose significant challenges. Event cameras have emerged…
Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the generative process. To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic l…
Event-based cameras sense changes in light intensity, and asynchronously outputs event data. Compared with conventional cameras, event-based cameras have the advantages of low latency, high dynamic range, and low power consumption. Event cameras have been utilized for various vision tasks, such as d
Omni-directional cameras have many advantages overconventional cameras in that they have a much wider field-of-view (FOV). Accordingly, several approaches have beenproposed recently to apply convolutional neural networks(CNNs) to omni-directional images for various visual tasks.However, most of…