SIMILARnet: Simultaneous Intelligent Localization and Recognition Network
This work addresses a domain-specific challenge in computer vision by offering a more efficient and flexible approach for object detection and classification.
The paper tackles the problem of simultaneous object localization and recognition in images by proposing a biologically inspired model that eliminates the need for differential connections and separate training, achieving promising results and insensitivity to input image size.
Global Average Pooling (GAP) [4] has been used previously to generate class activation for image classification tasks. The motivation behind SIMILARnet comes from the fact that the convolutional filters possess position information of the essential features and hence, combination of the feature maps could help us locate the class instances in an image. We propose a biologically inspired model that is free of differential connections and doesn't require separate training thereby reducing computation overhead. Our novel architecture generates promising results and unlike existing methods, the model is not sensitive to the input image size, thus promising wider application. Codes for the experiment and illustrations can be found at: https://github.com/brcsomnath/Advanced-GAP .