Recurrent Convolutional Model Research Articles

Recurrent convolutional model based on gated spiking neural P system for stereo matching networks

Recurrent convolutional model based on gated spiking neural P system for stereo matching networks

Knowledge distillation of convolutional neural network models on inaccurately labeled data for automatic text CAPTCHA recognition on mobile devices

Problem definition: most of the existing works investigate the recognition of a fixed-length CAPTCHA, but the authors suggest using knowledge distillation to simulate the operation of recurrent-convolutional models, which have proven themselves well in the task of predicting the dynamic length of characters in images. The rapid development of deep learning systems, the recognition quality of which has reached the level of human vision, makes the method of protection using CAPTCHA increasingly ineffective. In addition, such protection imposes high requirements on the characteristics of the devices on which recognition is performed. The research carried out in this work allowed us to propose an effective method of training CNN on inaccurate data for automatic circumvention of text CAPTCHAS on mobile devices. Purpose: acquiring a lightweight and high-quality model for text CAPTCHA recognition that can work on mobile devices. Results: the paper describes a method for training a lightweight model on inaccurate markup obtained from another model. The influence of the size of the training sample on the quality of recognition, the speed of the model on various end devices is studied on the example of a popular social network. Practical significance: The proposed method allows you to train convolutional models to bypass the protection of websites-text CAPTCHA, which are undemanding to the characteristics of devices. The analysis of the model errors allows us to make recommendations for improving ways to counteract automatic recognition.

Residual Invertible Spatio-Temporal Network for Video Super-Resolution

Video super-resolution is a challenging task, which has attracted great attention in research and industry communities. In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution. The RISTN can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames. Compared with existing recurrent convolutional network based approaches, RISTN is much deeper but more efficient. It consists of three major components: In the spatial component, a lightweight residual invertible block is designed to reduce information loss during feature transformation and provide robust feature representations. In the temporal component, a novel recurrent convolutional model with residual dense connections is proposed to construct deeper network and avoid feature degradation. In the reconstruction component, a new fusion method based on the sparse strategy is proposed to integrate the spatial and temporal features. Experiments on public benchmark datasets demonstrate that RISTN outperforms the state-ofthe-art methods.

Recurrent Attention Model for Pedestrian Attribute Recognition

Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other. RC model mines the correlations among different attribute groups with convolutional LSTM unit, while RA model takes advantage of the intra-group spatial locality and inter-group attention correlation to improve the performance of pedestrian attribute recognition. Our RA method combines the Recurrent Learning and Attention Model to highlight the spatial position on feature map and mine the attention correlations among different attribute groups to obtain more precise attention. Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description.

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized.