In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes. Object Detection System 110 Object Detection Application 112 slay124 Image Image Module Word 144 Embedding Processor W D 1 LModoule 150 16Word Dt 3 Input Condition Detection Storage 128 Attention Map 10 ~Data 134 Module 146 Module 152 Data 134 _ _ _ - ~Transceiver _ Module 140 DtcinDt Attention Map Training Module 148 Module 154 Assets Training Data 104- - Image 120 'wheel' 118 122 122 122 104-1 Network -o 104-2 /c-----J.. Image System 1_1q4 Image 116 ODS 110 Applications Detection Assets Application 160 1_58 -|
METHOD FOR SCALING OBJECT DETECTION TO A VERY LARGE NUMBER OF CATEGORIES