Perception Task

Perception tasks in computer vision and robotics aim to enable machines to understand and interpret sensory data, such as images and sensor readings, to perform actions or make decisions. Current research focuses on developing efficient and robust models, including multi-task learning frameworks and unified architectures like transformers, to handle diverse perception tasks simultaneously, often leveraging large pre-trained models and synthetic datasets for improved performance and generalization. This work is crucial for advancing autonomous systems, particularly in robotics and autonomous driving, where reliable and real-time perception is paramount for safe and effective operation. Furthermore, research is actively addressing challenges like hallucination detection in large language models used for perception and improving the efficiency of perception models for resource-constrained environments.