The paper presents a deep recurrent attention model for recognizing multiple objects in images. The model is trained using reinforcement learning to attend to the most relevant regions of the input image, enabling it to localize and recognize multiple objects even when only class labels are provided during training. The authors evaluate the model on the task of transcribing house number sequences from Google Street View images, demonstrating superior performance compared to state-of-the-art convolutional neural networks (ConvNets) in terms of accuracy, parameter usage, and computational efficiency. The model's effectiveness is further validated through experiments on multi-object classification tasks using variants of MNIST and the SVHN dataset, showing that it can handle variable-length label sequences and perform well even with less cropped input images. The paper concludes by highlighting the model's flexibility, power, and efficiency, suggesting it as a promising approach for tackling challenging computer vision tasks.The paper presents a deep recurrent attention model for recognizing multiple objects in images. The model is trained using reinforcement learning to attend to the most relevant regions of the input image, enabling it to localize and recognize multiple objects even when only class labels are provided during training. The authors evaluate the model on the task of transcribing house number sequences from Google Street View images, demonstrating superior performance compared to state-of-the-art convolutional neural networks (ConvNets) in terms of accuracy, parameter usage, and computational efficiency. The model's effectiveness is further validated through experiments on multi-object classification tasks using variants of MNIST and the SVHN dataset, showing that it can handle variable-length label sequences and perform well even with less cropped input images. The paper concludes by highlighting the model's flexibility, power, and efficiency, suggesting it as a promising approach for tackling challenging computer vision tasks.