447 papers with code • 4 benchmarks • 39 datasets
Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
( Image credit: Tensorflow Object Detection API )
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers.
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world.
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.
This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.