Deep Learning

Object detection has produced many algorithms because of the complexity of combining the localization and classification branches. These methods improve algorithm performance from different angles, but are hard to measure in a unified way. This paper uses multiple attention mechanisms to unify different object detection heads: attention over the feature level, over the spatial position and across channels, corresponding respectively to scale awareness, spatial awareness and task awareness. This method can noticeably improve model performance without increasing the computational cost. ...

Make it dynamic. Dynamic Convolution Ordinary static convolution applies the same kernel to all input images, whereas dynamic convolution adapts to different images and processes them with more suitable convolution parameters, which increases the expressive power of the model without increasing network depth or width. Dynamic convolution dynamically aggregates multiple parallel convolution kernels according to attention. The attention dynamically adjusts the weight of each kernel according to the input, thereby producing an adaptive dynamic convolution. Because the attention is a function of the input, dynamic convolution is no longer a linear function. Superimposing kernels in a nonlinear way through attention gives stronger representational power. The rough structure is as follows: ...

Image segmentation is pixel-level classification of an image, and mainly includes semantic segmentation and instance segmentation. Semantic segmentation only requires segmentation at the pixel level, while instance segmentation additionally requires separating different individuals on top of semantic segmentation. This post briefly introduces several basic semantic segmentation networks. FCN FCN (Full Convolution Network) is a classic image segmentation network whose main structure is an encoder-decoder, where the encoder extracts image feature information and the decoder interprets the extracted features. FCN replaces the fully connected layers in the network with convolution layers, so that 2D feature maps are obtained; these are then upsampled through deconvolution (transposed convolution), and finally a softmax layer produces the mask for each class. It can also handle input images of different sizes. The rough structure is shown below. ...

Object detection is an important branch of the computer vision field, and its essential content is: What objects are where? At the same time, as a basic task it supports some more complex problems in the field (such as instance segmentation, image text recognition, object tracking and so on). From the application point of view, object detection can be divided into two major categories: general object detection and detection applications. General object detection mainly explores methods for detecting different types of objects within a unified algorithmic framework, similar to human visual perception; detection applications focus on actual scenarios, such as pedestrian detection, face detection and text detection. ...