Smaller but Better: Unifying Layout Generation with Smaller Large Language Models

New Breakthrough in Unified Layout Generation Research: Smaller but Stronger Large Language Models Research Background and Problem Statement Layout generation is an important research direction in the fields of computer vision and human-computer interaction, aiming to automatically generate graphic interfaces or layout designs that meet specific re...

Towards Boosting Out-of-Distribution Detection from a Spatial Feature Importance Perspective

Boosting Out-of-Distribution Detection Performance from the Perspective of Spatial Feature Importance Research Background and Problem Statement In practical applications of deep learning models, ensuring that models can reliably reject predictions when faced with inputs from unknown categories is crucial for system safety and robustness. This need ...

Moonshot: Towards Controllable Video Generation and Editing with Motion-Aware Multimodal Conditions

MoonShot——Towards Controllable Video Generation and Editing with Motion-Aware Multimodal Conditions Research Background and Problem Statement In recent years, text-to-video diffusion models (Video Diffusion Models, VDMs) have made significant progress, enabling the generation of high-quality, visually appealing videos. However, most existing VDMs r...

Deepfake-Adapter: Dual-Level Adapter for Deepfake Detection

Deepfake-Adapter——A Dual-Level Adapter for Deepfake Detection Research Background and Problem With the rapid development of deep generative models, hyper-realistic facial images and videos can be easily generated, which are capable of deceiving the human eye. When such technology is maliciously abused, it may lead to serious misinformation problems...

Image Synthesis under Limited Data: A Survey and Taxonomy

Image Synthesis Under Limited Data: A Survey Research Background and Problem Statement In recent years, deep generative models have achieved unprecedented progress in intelligent creation tasks, especially in areas such as image and video generation, and audio synthesis. However, the success of these models relies heavily on large amounts of traini...

Self-Supervised Shutter Unrolling with Events

Event Camera-Based Self-Supervised Shutter Unrolling Method Research Background and Problem Statement In the field of computer vision, recovering undistorted global shutter (GS) videos from rolling shutter (RS) images has been a highly challenging problem. RS cameras, due to their row-by-row exposure mechanism, are prone to spatial distortions (e.g...