Zero-Shot Detection

19 Mar 2018 Pengkai Zhu Hanxiao Wang Venkatesh Saligrama

As we move towards large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes at sufficient scale, and so methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features to propose object bounding boxes for seen and unseen classes... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Max Pooling
Pooling Operations
Softmax
Output Functions
Convolution
Convolutions
Darknet-19
Convolutional Neural Networks
YOLOv2
Object Detection Models