Object Detection
Object detection, a core computer vision task, aims to identify and locate objects within images or videos. Current research emphasizes improving accuracy and efficiency across diverse scenarios, focusing on architectures like YOLO and DETR, and exploring techniques such as multimodal fusion, attention mechanisms, and loss function refinements to handle challenges like small object detection, adverse weather conditions, and limited labeled data. These advancements have significant implications for applications ranging from autonomous driving and robotics to medical image analysis and remote sensing, driving progress in both theoretical understanding and practical deployment of object detection systems.
Papers
Multimodal Object Detection via Probabilistic a priori Information Integration
Hafsa El Hafyani, Bastien Pasdeloup, Camille Yver, Pierre Romenteau
Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection
Fan Liu, Liang Yao, Chuanyi Zhang, Ting Wu, Xinlei Zhang, Xiruo Jiang, Jun Zhou
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection
Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, Jiandong Tian
Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection
Mehmet Kerem Turkcan, Sanjeev Narasimhan, Chengbo Zang, Gyung Hyun Je, Bo Yu, Mahshid Ghasemi, Javad Ghaderi, Gil Zussman, Zoran Kostic
CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection under Adverse Weather Conditions
Haoyuan Li, Qi Hu, You Yao, Kailun Yang, Peng Chen