This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application. Prior neural networks, such as ResNet, tend to enhance representational power by increasing the depth of architectures and driving the training objective across layers. However, we argue that spatial dependencies in different layers, which generally represent the rich contexts among data elements, are also critical to building deep and discriminative representations. To this end, our PDNs enables to progressively broadcast information over the learned feature maps by inserting a stack of information diffusion layers, each of which exploits multi-dimensional convolutional LSTMs (Long-Short-Term Memory Structures). In each LSTM unit, a special type of atrous filters are designed to capture the short range and long range dependencies from various neighbors to a certain site of the feature map and pass the accumulated information to the next layer. From the extensive experiments on semantic image segmentation benchmarks (e.g., ImageNet Parsing, PASCAL VOC2012 and PASCAL-Part), our framework demonstrates the effectiveness to substantially improve the performances over the popular existing neural network models, and achieves state-of-the-art on ImageNet Parsing for large scale semantic segmentation.
In the literature, the Long Short Term Memory (LSTM) Network has been introduced to deal with the long-range dependencies in the representation modeling, and this advanced Recurrent Neural Network (RNN) has achieved great success in many intelligent tasks @cite_26 @cite_16 @cite_13 @cite_6 . In recent years, it has been extended to multi-dimensional communication @cite_21 @cite_11 @cite_39 and adapted to represent the rich contexts in image spatial @cite_3 @cite_10 . @cite_3 , a recent advance in LSTM-based context modeling was achieved by considering both short dependencies from local area and long-distance global information from the whole image. Liang . @cite_10 further extended this work from multi-dimensional data to general graph-structured data, and constructed an adaptive graph topology to propagate contextual information between adjacent superpixels. Nevertheless, in these works, the feature representation of each position is affected by a limited local factors (i.e. the adjacent positions), which restricts the capacity of involving diverse visual correlations in a large range. Different from using limited local LSTM units, the proposed PDNs captures the short-range and long-range dependencies from various neighbors and can generate more informative representation for pixel-wise prediction.
@cite_26 @cite_10 @cite_21 @cite_6 @cite_39 @cite_3 @cite_16 @cite_13 @cite_11
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The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) has been demonstrated successful in handwritten text recognition of Western and Arabic scripts. It is totally segmentation free and can be trained directly from text line images. However, the application of LSTM-RNNs (including Multi-Dimensional LSTM-RNN (MDLSTM-RNN)) to Chinese text recognition has shown limited success, even when training them with large datasets and using pre-training on datasets of other languages. In this paper, we propose a handwritten Chinese text recognition method by using Separable MDLSTMRNN (SMDLSTM-RNN) modules, which extract contextual information in various directions, and consume much less computation efforts and resources compared with the traditional MDLSTMRNN. Experimental results on the ICDAR-2013 competition dataset show that the proposed method performs significantly better than the previous LSTM-based methods, and can compete with the state-of-the-art systems. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio visual modeling in our photo-real talking head system. An audio visual database of a subject's talking is firstly recorded as our training data. The audio visual stereo data are converted into two parallel temporal sequences, i.e., contextual label sequences obtained by forced aligning audio against text, and visual feature sequences by applying active-appearance-model (AAM) on the lower face region among all the training image samples. The deep BLSTM is then trained to learn the regression model by minimizing the sum of square error (SSE) of predicting visual sequence from label sequence. After testing different network topologies, we interestingly found the best network is two BLSTM layers sitting on top of one feed-forward layer on our datasets. Compared with our previous HMM-based system, the newly proposed deep BLSTM-based one is better on both objective measurement and subjective A B test. In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains @math WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with @math and @math relative improvement respectively. Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks. In this paper we develop Tree Long Short-Term Memory (TreeLSTM), a neural network model based on LSTM, which is designed to predict a tree rather than a linear sequence. TreeLSTM defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated sub-tree. We further enhance the modeling power of TreeLSTM by explicitly representing the correlations between left and right dependents. Application of our model to the MSR sentence completion challenge achieves results beyond the current state of the art. We also report results on dependency parsing reranking achieving competitive performance.