Weakly supervised object detection (WSOD) is a challenging task when provided with image
category supervision but required to simultaneously learn object locations and object …

Weakly supervised object localization remains a challenge when learning object localization
models from image category labels. Optimizing image classification tends to activate object …

Weakly supervised object detection is a challenging task when provided with image
category supervision but required to learn, at the same time, object locations and object …

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging
technological framework focusing on seamless integration of AI, communication networks …

Weakly supervised object localization remains challenging, where only image labels instead
of bounding boxes are available during training. Object proposal is an effective component …

Abstract Multiple Instance Learning (MIL) is a fundamental method for weakly supervised
object detection (WSOD), but experiences difficulty in excluding local optimal solutions and …

Conventional methods for weakly supervised object detection (WSOD) typically enumerate
dense proposals and select the discriminative proposals as objects. However, these two …

In recent years, fully supervised object detection methods in remote sensing images with
good performance have been developed. However, this approach requires a large number …

Image scaling techniques such as Super-Resolution (SR) are useful for object detection,
especially for detecting small objects. However, we found that scaling by an inappropriate …

Pedestrian detection has achieved great improve-ments in recent years, while complex
occlusion handling and high-accurate localization are still the most important problems. To …