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Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Once you do, it is fast and easy! Furthermore, to evaluate our method, we propose four new surveillance datasets that contain videos with obstacles. Thanks so much for the nice editing job on this one. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. But, life is tragic, and they noose the poor girl and one of the guys pulls her up so she is up on her tip toes. Dani Dupree.

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Blonde French Kissing Kiss. Kissing AC Part3 Video1. Human conversation analysis is challenging because the meaning can be expressed through words, intonation, or even body language and facial expression. We introduce a hierarchical encoder-decoder structure with attention mechanism for conversation analysis.

The hierarchical encoder learns word-level features from video, audio, and text data that are then formulated into conversation-level features. The corresponding hierarchical decoder is able to predict different attributes at given time instances. To integrate multiple sensory inputs, we introduce a novel fusion strategy with modality attention. We evaluated our system on published emotion recognition, sentiment analysis, and speaker trait analysis datasets.

Our system outperformed previous state-of-the-art approaches in both classification and regressions tasks on three datasets. We also outperformed previous approaches in generalization tests on two commonly used datasets. We achieved comparable performance in predicting co-existing labels using the proposed model instead of multiple individual models.

In addition, the easily-visualized modality and temporal attention demonstrated that the proposed attention mechanism helps feature selection and improves model interpretability. Our model uses a multi-task DNN framework that not only estimates the perceptual quality of the test video but also provides a probabilistic prediction of its codec type.

This framework allows us to train the network with two complementary sets of labels, both of which can be obtained at low cost. The training process is composed of two steps. In the first step, early convolutional layers are pre-trained to extract spatiotemporal quality-related features with the codec classification subtask.

In the second step, initialized with the pre-trained feature extractor, the whole network is jointly optimized with the two subtasks together. An additional critical step is the adoption of 3D convolutional layers, which creates novel spatiotemporal features that lead to a significant performance boost. Experimental results show that the proposed model clearly outperforms state-of-the-art BVQA methods.

FlexStream exploits the benefits of both centralized and distributed components to achieve dynamic management of end devices, as required and in accordance with specified policies. We evaluate FlexStream on one example use case -- the adaptive video streaming, where bandwidth control is employed to drive selection of video bitrates, improve stability and increase robustness against background traffic.

In addition, we report the first implementation of SDN-based control in Android devices running in real Wi-Fi and live cellular networks. Viewport adaptive streaming is emerging as a promising way to deliver high quality degree video. It is still a critical issue to predict user's viewpoint and deliver partial video within the viewport.

Current widely-used motion-based or content-saliency methods have low precision, especially for long-term prediction. In this paper, benefiting from data-driven learning, we propose a Cross-user Learning based System CLS to improve the precision of viewport prediction. Since users have similar region-of-interest ROI when watching a same video, it is possible to exploit cross-users' ROI behavior to predict viewport.

We use a machine learning algorithm to group users according to historical fixations, and predict the viewing probability by the class. Additionally, we present a QoE-driven rate allocation to minimize the expected streaming distortion under bandwidth constraint, and give a Multiple-Choice Knapsack solution.

Experiments demonstrate that CLS provides 2dB quality improvement than full-image streaming and 1. Past research has shown that concurrent HTTP adaptive streaming HAS players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience.

While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks.

Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players. We address the problem of correcting the exposure of underexposed photos.

Previous methods have tackled this problem from many different perspectives and achieved remarkable progress. However, they usually fail to produce natural-looking results due to the existence of visual artifacts such as color distortion, loss of detail, exposure inconsistency, etc. We find that the main reason why existing methods induce these artifacts is because they break a perceptually similarity between the input and output.

Based on this observation, an effective criterion, termed as perceptually bidirectional similarity PBS is proposed. Based on this criterion and the Retinex theory, we cast the exposure correction problem as an illumination estimation optimization, where PBS is defined as three constraints for estimating illumination that can generate the desired result with even exposure, vivid color and clear textures.

Qualitative and quantitative comparisons, and the user study demonstrate the superiority of our method over the state-of-the-art methods. A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data.

In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate i. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate MLE method.

We prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods.

The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-of-the-art algorithms. Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments.

However, this "interestingness" of a video segment or image is subjective. Thus, such highlight models provide results of limited relevance for the individual user. On the other hand, training one model per user is inefficient and requires large amounts of personal information which is typically not available. To overcome these limitations, we present a global ranking model which can condition on a particular user's interests.

Rather than training one model per user, our model is personalized via its inputs, which allows it to effectively adapt its predictions, given only a few user-specific examples. To train this model, we create a large-scale dataset of users and the GIFs they created, giving us an accurate indication of their interests.

Our experiments show that using the user history substantially improves the prediction accuracy. Furthermore, our method proves more precise than the user-agnostic baselines even with only one single person-specific example. Under person re-identification Re-ID , a query photo of the target person is often required for retrieval. However, one is not always guaranteed to have such a photo readily available under a practical forensic setting.

In this paper, we define the problem of Sketch Re-ID, which instead of using a photo as input, it initiates the query process using a professional sketch of the target person. This is akin to the traditional problem of forensic facial sketch recognition, yet with the major difference that our sketches are whole-body other than just the face. This problem is challenging because sketches and photos are in two distinct domains.

Specifically, a sketch is the abstract description of a person. Besides, person appearance in photos is variational due to camera viewpoint, human pose and occlusion. We address the Sketch Re-ID problem by proposing a cross-domain adversarial feature learning approach to jointly learn the identity features and domain-invariant features.

We employ adversarial feature learning to filter low-level interfering features and remain high-level semantic information. We also contribute to the community the first Sketch Re-ID dataset with persons, where each person has one sketch and two photos from different cameras associated. Results show that the proposed method outperforms the state-of-the-arts.

Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes them difficult to be applied to large scale data or time-sensitive scenarios.

In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints learned from data and propose an automatic human matting algorithm Semantic Human Matting SHM. SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and fine details is challenging.

We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha matte. We also construct a very large dataset with high quality annotations consisting of 35, unique foregrounds to facilitate the learning and evaluation of human matting. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods.

Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations.

We employ facial geometry fiducial points as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks is jointly trained towards opposite tasks: The paired networks form a mapping cycle between neutral expression and arbitrary expressions, with which the proposed approach can be conducted among unpaired data.

The proposed paired networks also facilitate other applications such as face transfer, expression interpolation and expression-invariant face recognition. Experimental results on several facial expression databases show that our method can generate compelling perceptual results on different expression editing tasks. Abnormal event detection in video surveillance is a valuable but challenging problem.

Most methods adopt a supervised setting that requires collecting videos with only normal events for training. However, very few attempts are made under unsupervised setting that detects abnormality without priorly knowing normal events. Existing unsupervised methods detect drastic local changes as abnormality, which overlooks the global spatio-temporal context.

This paper proposes a novel unsupervised approach, which not only avoids manually specifying normality for training as supervised methods do, but also takes the whole spatio-temporal context into consideration. Our approach consists of two stages: First, normality estimation stage trains an autoencoder and estimates the normal events globally from the entire unlabeled videos by a self-adaptive reconstruction loss thresholding scheme.

Second, normality modeling stage feeds the estimated normal events from the previous stage into one-class support vector machine to build a refined normality model, which can further exclude abnormal events and enhance abnormality detection performance.

Experiments on various benchmark datasets reveal that our method is not only able to outperform existing unsupervised methods by a large margin up to Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity.

Such an instance-level transfer problem is more challenging than conventional domain-level transfer tasks, especially when paired data is unavailable. Makeup style is also different from global styles e. Extracting and transferring such local and delicate makeup information is infeasible for existing style transfer methods.

Specifically, the domain-level transfer is ensured by discriminators that distinguish generated images from domains' real samples. The instance-level loss is calculated by pixel-level histogram loss on separate local facial regions. We further introduce perceptual loss and cycle consistency loss to generate high quality faces and preserve identity. The overall objective function enables the network to learn translation on instance-level through unsupervised adversarial learning.

We also build up a new makeup dataset that consists of high-resolution face images. Extensive experiments show that BeautyGAN could generate visually pleasant makeup faces and accurate transferring results. Data and code are available at http: Human parsing, which segments a human-centric image into pixel-wise categorization, has a wide range of applications.

However, none of the existing methods can productively solve the issue of label parsing fragmentation due to confused and complicated annotations. Based on a pyramid architecture, we design a Pyramid Residual Pooling PRP module setting at the end of a bottom-up approach to capture both global and local level context. In the top-down approach, we propose a Trusted Guidance Multi-scale Supervision TGMS that efficiently integrates and supervises multi-scale contextual information.

Furthermore, we present a simple yet powerful Trusted Guidance Framework TGF which imposes global-level semantics into parsing results directly without extra ground truth labels in model training. Extensive experiments on two public human parsing benchmarks well demonstrate that our TGPNet has a strong ability in solving label parsing fragmentation problem and has an obtained improvement than other methods.

In visual classification tasks, it is hard to tell the subtle differences from one species to another similar breeds. TA-FGVC reads from texts to gain attention, sees the images with the gained attention and then tells the subtle differences. Technically, we propose a deep neural network which learns a visual-semantic embedding model. The proposed deep architecture mainly consists of two parts: The model is fed with both visual features which are extracted from raw images and semantic information which are learned from two sources: At the very last layer of the model, each image is embedded into the semantic space which is related to class labels.

Finally, the categorization results from both visual stream and visual-semantic stream are combined to achieve the ultimate decision. Extensive experiments on open standard benchmarks verify the superiority of our model against several state of the art work. With the widespread availability of image captioning at a sentence level, how to automatically generate image paragraphs is yet well explored.

Describing an image by a full paragraph involves organising sentences orderly, coherently and diversely, inevitably leading higher complexity than by a single sentence. Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.

This strategy is difficult to generate the descriptions that guarantee the stereoscopic hierarchy and non-overlapping objects. The depths of image areas are firstly estimated in order to discriminate objects in a range of spatial locations, which can further guide the linguistic decoder to reveal spatial relationships among objects.

This model completes the paragraph in a logical and coherent manner. By incorporating the attention mechanism, the learned model swiftly shifts the sentence focus during paragraph generation, whilst avoiding verbose descriptions on a same object. Extensive quantitative experiments and the user study have been conducted on the Visual Genome dataset, which demonstrate the effectiveness and the interpretability of the proposed model.

Taxonomy learning is an important problem and facilitates various applications such as semantic understanding and information retrieval. Previous work for building semantic taxonomies has primarily relied on labor-intensive human contributions or focused on text-based extraction.

In this paper, we investigate the problem of automatically learning multimodal taxonomies from the multimedia data on the Web. A systematic framework called Variational Deep Graph Embedding and Clustering VDGEC is proposed consisting of two stages as concept graph construction and taxonomy induction via variational deep graph embedding and clustering. VDGEC discovers hierarchical concept relationships by exploiting the semantic textual-visual correspondences and contextual co-occurrences in an unsupervised manner.

The unstructured semantics and noisy issues of multimedia documents are carefully addressed by VDGEC for high quality taxonomy induction. We conduct extensive experiments on the real-world datasets. Experimental results demonstrate the effectiveness of the proposed framework, where VDGEC outperforms previous unsupervised approaches by a large gap.

However, most existing algorithms only exploit the visual cues of these concepts but ignore external knowledge information for modeling their relationships during the evolution of videos. In fact, humans have remarkable ability to utilize acquired knowledge to reason about the dynamically changing world.

To narrow the knowledge gap between existing methods and humans, we propose an end-to-end video classification framework based on a structured knowledge graph, which can model the dynamic knowledge evolution in videos overtime. Here, we map the concepts of videos to the nodes of the knowledge graph.

To effectively leverage the knowledge graph, we adopt a graph convLSTM model to not only identify local knowledge structures in each video shot but also model dynamic patterns of knowledge evolution across these shots. Furthermore, a novel knowledge-based attention model is designed by considering the importance of each video shot and relationships between concepts.

We show that by using knowledge graphs, our framework is able to improve the performance of various existing methods. Extensive experimental results on two video classification benchmarks UCF and Youtube-8M demonstrate the favorable performance of the proposed framework. Multi-label image classification is a fundamental but challenging task towards general visual understanding.

Existing methods found the region-level cues e. Nevertheless, such methods usually require laborious object-level annotations i. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, 1 we first develop a weakly-supervised detection WSD model, and then 2 construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.

The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. With the development of deep neural networks, recent years have witnessed the increasing research interest on generative models.

VAE is well established and theoretically elegant, but tends to generate blurry samples. In contrast, GAN has shown the advantage in visual quality of generated images, but suffers the difficulty in translating a random vector into a desired high-dimensional sample. As a result, the training dynamics in GAN are often unstable and the generated samples could collapse to limited modes.

In our approach, instead of matching the encoded distribution of training samples to the prior P z as in VAE, we map the random vector into the encoded latent space by adversarial training based on GAN. Besides, we also match the decoded distribution of training samples with that from random vectors.

To evaluate our approach, we make comparison with other encoder-decoder based generative models on three public datasets. The experiments with both qualitative and quantitative results demonstrate the superiority of our algorithm over the comparison generative models.

Subspace clustering aims at clustering data points drawn from a union of low-dimensional subspaces. Recently deep neural networks are introduced into this problem to improve both representation ability and precision for non-linear data. However, such models are sensitive to noise and outliers, since both difficult and easy samples are treated equally.

On the contrary, in the human cognitive process, individuals tend to follow a learning paradigm from easy to hard and less to more. In other words, human beings always learn from simple concepts, then absorb more complicated ones gradually. Inspired by such learning scheme, in this paper, we propose a robust deep subspace clustering framework based on the principle of human cognitive process.

Specifically, we measure the easinesses of samples dynamically so that our proposed method could gradually utilize instances from easy to more complex ones in a robust way. Meanwhile, a promising solution is designed to update the weights and parameters using an alternative optimization strategy, followed by a theoretical analysis to demonstrated the rationality of the proposed method.

Experimental results on three popular benchmark datasets demonstrate the validity of the proposed method. Key to automatically generate natural scene images is to properly arrange amongst various spatial elements, especially in the depth cue. To this end, we introduce a novel depth structure preserving scene image generation network DSP-GAN , which favors a hierarchical architecture, for the purpose of depth structure preserving scene image generation.

The main trunk of the proposed infrastructure is built upon a Hawkes point process that models high-order spatial dependency between different depth layers. Within each layer generative adversarial sub-networks are trained collaboratively to generate realistic scene components, conditioned on the layer information produced by the point process.

We experiment our model on annotated natural scene images collected from SUN dataset and demonstrate that our models are capable of generating depth-realistic natural scene image. Person re-identification aims to identify the same pedestrian across non-overlapping camera views. Deep learning techniques have been applied for person re-identification recently, towards learning representation of pedestrian appearance.

It concentrates the network on latent image regions related to each attribute as well as exploits the semantic context among attributes by a LSTM module. An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts.

Extensive experiments on two challenging benchmarks, i. Point cloud, an efficient 3D object representation, has become popular with the development of depth sensing and 3D laser scanning techniques. It has attracted attention in various applications such as 3D tele-presence, navigation for unmanned vehicles and heritage reconstruction. The understanding of point clouds, such as point cloud segmentation, is crucial in exploiting the informative value of point clouds for such applications.

Due to the irregularity of the data format, previous deep learning works often convert point clouds to regular 3D voxel grids or collections of images before feeding them into neural networks, which leads to voluminous data and quantization artifacts. In this paper, we instead propose a regularized graph convolutional neural network RGCNN that directly consumes point clouds.

Leveraging on spectral graph theory, we treat features of points in a point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial approximation. In particular, we update the graph Laplacian matrix that describes the connectivity of features in each layer according to the corresponding learned features, which adaptively captures the structure of dynamic graphs.

Further, we deploy a graph-signal smoothness prior in the loss function, thus regularizing the learning process. Experimental results on the ShapeNet part dataset show that the proposed approach significantly reduces the computational complexity while achieving competitive performance with the state of the art.

Also, experiments show RGCNN is much more robust to both noise and point cloud density in comparison with other methods. Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding solution, focusing on deep learning to quantization approach that has shown superior performance over hashing solutions for similarity retrieval.

We propose Deep Triplet Quantization DTQ , a novel approach to learning deep quantization models from the similarity triplets. To enable more effective triplet training, we design a new triplet selection approach, Group Hard, that randomly selects hard triplets in each image group. To generate compact binary codes, we further apply a triplet quantization with weak orthogonality during triplet training.

The quantization loss reduces the codebook redundancy and enhances the quantizability of deep representations through back-propagation. What can multi-media systems design learn from art? How can the research agenda be advanced by looking at art? How can we improve creativity support and the amplification of that important human capability?

Interactive art has become a common part of life as a result of the many ways in which the computer and the Internet have facilitated it. Multi-media computing is as important to interactive art as mixing the colors of paint are to painting. This talk reviews recent work that looks at these issues through art research.

In interactive digital art, the artist is concerned with how the artwork behaves, how the audience interacts with it, and, ultimately, how participants experience art as well as their degree of engagement. The talk examines these issues and brings together a collection of research results from art practice that illuminates this significant new and expanding area.

In particular, this work points towards a much-needed critical language that can be used to describe, compare and frame research into the support of creativity. Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture.

GestureGAN consists of a single generator G and a discriminator D, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients.

Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier.

Our model and code are available at https: Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language with multiple lines to an image for automatic poetry creation.

This task involves multiple challenges, including discovering poetic clues from the image e. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator.

To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: Extensive experiments are conducted with 8K images, among which 1. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images.

Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task.

In this paper, we present a new large-scale database " M ulti- H uman P arsing MHP " for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25, elaborately annotated images with 58 fine-grained semantic category labels, involving persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background.

NAN consists of three G enerative A dversarial N etwork GAN -like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.

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French Kissing Kiss Kissing. Well, that didn't take long. Inspired by the recent study of Generative Adversarial Networks GAN in domain adaptation, this paper proposes a new model based on Generative Adversarial Network, named Hierarchical Adversarial Deep Network HADN , which jointly optimizes the feature-level and pixel-level adversarial adaptation within a hierarchical network structure. I know that one. In this paper, we take one step further to investigate generation of poetic language with multiple lines to an image for automatic poetry creation. Looks like Sharia law to me. Welcome to Europe Extensive experiments demonstrate that MEDA shows significant improvements in classification accuracy compared to state-of-the-art traditional and deep methods.

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