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
Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
Miguel Antunes-García, Luis M. Bergasa, Santiago Montiel-Marín, Rafael Barea, Fabio Sánchez-García, Ángel Llamazares
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
Jia Syuen Lim, Yadan Luo, Zhi Chen, Tianqi Wei, Scott Chapman, Zi Huang
Integrating Object Detection Modality into Visual Language Model for Enhanced Autonomous Driving Agent
Linfeng He, Yiming Sun, Sihao Wu, Jiaxu Liu, Xiaowei Huang
Open-set object detection: towards unified problem formulation and benchmarking
Hejer Ammar, Nikita Kiselov, Guillaume Lapouge, Romaric Audigier
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser
IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks
Manjunath D, Prajwal Gurunath, Sumanth Udupa, Aditya Gandhamal, Shrikar Madhu, Aniruddh Sikdar, Suresh Sundaram