|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
In this work, we revisit the global average pooling layer proposed in , and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible.
Large-scale object detection datasets (e. g., MS-COCO) try to define the ground truth bounding boxes as clear as possible.
#15 best model for Object Detection on PASCAL VOC 2007
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
#2 best model for Weakly Supervised Object Detection on COCO
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset.