Object Proposal
Object proposal generation aims to identify potential object locations within an image or video, serving as a crucial preprocessing step for various computer vision tasks like object detection and segmentation. Current research emphasizes improving proposal quality through techniques like incorporating contextual information (e.g., spatial relationships between objects, temporal evolution in videos), leveraging vision-language models for open-vocabulary scenarios, and employing self-supervised or weakly-supervised learning to reduce reliance on extensive annotations. These advancements enhance the accuracy and efficiency of downstream tasks, impacting applications ranging from autonomous driving and robotics to medical image analysis and agricultural automation.
Papers
Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask
Christian Wilms, Alexander Michael Gerlach, Rüdiger Schmitz, Simone Frintrop
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
Antonin Vobecky, David Hurych, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Josef Sivic