Object Detector
Object detection, aiming to identify and locate objects within images or videos, is a core computer vision task with applications ranging from autonomous driving to medical image analysis. Current research emphasizes improving accuracy, particularly in addressing false positives and handling challenging conditions like occlusions, varying viewpoints, and noisy data, often employing transformer-based architectures and leveraging techniques like knowledge distillation and semi-supervised learning. These advancements are crucial for enhancing the reliability and robustness of object detectors in real-world applications, impacting fields requiring accurate and efficient scene understanding.
Papers
Seeing the Unseen: Visual Common Sense for Semantic Placement
Ram Ramrakhya, Aniruddha Kembhavi, Dhruv Batra, Zsolt Kira, Kuo-Hao Zeng, Luca Weihs
Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images
Wenhui Wu, Man Sing Wong, Xinyu Yu, Guoqiang Shi, Coco Yin Tung Kwok, Kang Zou
Generating Enhanced Negatives for Training Language-Based Object Detectors
Shiyu Zhao, Long Zhao, Vijay Kumar B. G, Yumin Suh, Dimitris N. Metaxas, Manmohan Chandraker, Samuel Schulter
MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World
Zheng Zhou, Hongbo Zhao, Ju Liu, Qiaosheng Zhang, Liwei Geng, Shuchang Lyu, Wenquan Feng