In the ever-evolving landscape of artificial intelligence, a new framework called MAViD is making waves. Developed by a team of researchers including Youxin Pang, Jiajun Liu, Lingfeng Tan, Yong Zhang, Feng Gao, Xiang Deng, Zhuoliang Kang, Xiaoming We
Imagine a world where machines don’t just see but also hear, where they can navigate and interact with their environment using multiple senses just like we do. This is the exciting frontier that researchers are now exploring with the introduction of
In the rapidly evolving world of video generation, a new player has stepped onto the stage, promising to revolutionize the way we create and interact with multi-person videos. Meet AnyTalker, a framework developed by a team of researchers from variou
In the world of video understanding and education, especially when it comes to scientific content, there’s a significant challenge. Existing systems, while advanced, often fall short when dealing with the complex, step-by-step reasoning and professio
Robotic manipulation tasks are inherently contact-rich, yet most imitation learning (IL) approaches rely heavily on vision. While vision is powerful, it struggles to capture subtle interaction cues like stiffness, roughness, and slip. This is where t
In the rapidly evolving world of artificial intelligence, the quest for more efficient and scalable models is never-ending. A recent breakthrough in this area comes from a team of researchers who have developed a novel framework called AnyExperts. Th
Imagine you’ve spent countless hours perfecting an AI model to classify audio, only to find it stumbles when faced with real-world sounds. This is the challenge of domain shift, where models trained on one dataset falter when confronted with acoustic
Imagine being able to pinpoint the exact objects involved in a collision just by listening to the sound. That’s the fascinating goal of a new research task called Collision Sound Source Segmentation (CS3). This innovative approach aims to segment the
In the rapidly evolving world of connected cyber-physical systems, the ability to perform real-time inference based on multiple data streams is becoming increasingly crucial. However, these systems often face the challenge of uncertain communication
In the rapidly evolving landscape of digital signal processing, Implicit Neural Representations (INRs) have emerged as a powerful tool for modeling continuous signals such as images, audio, and 3D reconstructions. These representations use multilayer
