Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames, in contrast to standard image segmentation. They also show that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.
In order to perceive liquids at the pixel level, we make use of fully-convolutional neural networks (FCN). FCNs have been successfully applied to the task of image segmentation in the past @cite_7 @cite_4 @cite_1 and are a natural fit for pixel-wise classification. In addition to FCNs, we utilize long short-term memory (LSTM) @cite_11 recurrent cells to reason about the temporal evolution of liquids. LSTMs are preferable over more standard recurrent networks for long-term memory as they overcome many of the numerical issues during training such as exploding gradients @cite_5 . LSTM-based CNNs have been successfully applied to many temporal memory tasks by previous work @cite_10 @cite_1 , and in fact @cite_1 even combine LSTMs and FCNs by replacing the standard fully-connected layers of their LSTMs with @math convolution layers. We use a similar method in this paper.
@cite_4 @cite_7 @cite_1 @cite_5 @cite_10 @cite_11
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Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelize on GPUs. Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion. The resulting PyraMiD-LSTM is easy to parallelize, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12). Convolutional neural networks (CNNs) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully connected layer of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel locally supervised deep hybrid model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition. First, we notice that the convolutional features capture local objects and fine structures of scene images, which yield important cues for discriminating ambiguous scenes, whereas these features are significantly eliminated in the highly compressed FC representation. Second, we propose a new local convolutional supervision layer to enhance the local structure of the image by directly propagating the label information to the convolutional layers. Third, we propose an efficient Fisher convolutional vector (FCV) that successfully rescues the orderless mid-level semantic information (e.g., objects and textures) of scene image. The FCV encodes the large-sized convolutional maps into a fixed-length mid-level representation, and is demonstrated to be strongly complementary to the high-level FC-features. Finally, both the FCV and FC-features are collaboratively employed in the LS-DHM representation, which achieves outstanding performance in our experiments. It obtains 83.75 and 67.56 accuracies, respectively, on the heavily benchmarked MIT Indoor67 and SUN397 data sets, advancing the state-of-the-art substantially. Semantically labeling every pixel in a video is a very challenging task as video is an information-intensive media with complex spatio-temporal dependence. We present in this paper a novel deep convolutional network architecture, called deep spatio-temporal fully convolutional networks (DST-FCN), which leverages both spatial and temporal dependencies among pixels and voxels by training them in an end-to-end manner. Specifically, we introduce a two-stream network by learning the deep spatio-temporal dependence, in which a 2D FCN followed by the convolutional long short-term memory (ConvLSTM) is employed on the pixel level and a 3-D FCN is exploited on the voxel level. Our model differs from conventional FCN in that it not only extends FCN by adding ConvLSTM on the pixel level for exploring long-term dependence, but also proposes 3-D FCN to enable voxel level prediction. On two benchmarks of A2D and CamVid, our DST-FCN achieves superior results to state-of-the-art techniques. More remarkably, we obtain to-date the best reported results: 45.0 per-label accuracy on A2D and 68.8 mean IoU on CamVid. Generating natural language descriptions for in-the-wild videos is a challenging task. Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video. However, these deep CNN architectures are designed for single-label centered-positioned object classification. While they generate strong semantic features, they have no inherent structure allowing them to detect multiple objects of different sizes and locations in the frame. Our paper tries to solve this problem by integrating the base CNN into several fully convolutional neural networks (FCNs) to form a multi-scale network that handles multiple receptive field sizes in the original image. FCNs, previously applied to image segmentation, can generate class heat-maps efficiently compared to sliding window mechanisms, and can easily handle multiple scales. To further handle the ambiguity over multiple objects and locations, we incorporate the Multiple Instance Learning mechanism (MIL) to consider objects in different positions and at different scales simultaneously. We integrate our multi-scale multi-instance architecture with a sequence-to-sequence recurrent neural network to generate sentence descriptions based on the visual representation. Ours is the first end-to-end trainable architecture that is capable of multi-scale region processing. Evaluation on a Youtube video dataset shows the advantage of our approach compared to the original single-scale whole frame CNN model. Our flexible and efficient architecture can potentially be extended to support other video processing tasks.