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
Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation
Evan Ling, Dezhao Huang, Minhoe Hur
Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking
Alan Lukezic, Ziga Trojer, Jiri Matas, Matej Kristan
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, Rao Muhammad Anwer, Fahad Shahbaz Khan
Automatic satellite building construction monitoring
Insaf Ashrapov, Dmitriy Malakhov, Anton Marchenkov, Anton Lulin, Dani El-Ayyass
GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions
Sanket Kalwar, Dhruv Patel, Aakash Aanegola, Krishna Reddy Konda, Sourav Garg, K Madhava Krishna
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
Won Joon Yun, Soohyun Park, Joongheon Kim, David Mohaisen
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Jiaqing Zhang, Jie Lei, Weiying Xie, Zhenman Fang, Yunsong Li, Qian Du
A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields
Paraskevi Nousi, Emmanouil Mpampis, Nikolaos Passalis, Ole Green, Anastasios Tefas