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Table of contents
- Navigation menu
- On the History of Unified Field Theories
- Software-Defined Storage
- Faster development
- On the History of Unified Field Theories
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Open-ended video question answering aims to automatically generate the natural-language answer from referenced video contents according to the given question. Currently, most existing approaches focus on short-form video question answering with multi-modal recurrent encoder-decoder networks.
- On the History of Unified Field Theories;
- A Percent Clean Future - Center for American Progress!
- Sexaholics Orgy!
Although these works have achieved promising performance, they may still be ineffectively applied to long-form video question answering due to the lack of long-range dependency modeling and the suffering from the heavy computational cost. To tackle these problems, we propose a fast hierarchical convolutional self-attention encoder-decoder network.
Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context. We then devise a multi-scale attentive decoder to incorporate multi-layer video representations for answer generation, which avoids the information missing of the top encoder layer.
The extensive experiments show the effectiveness and efficiency of our method. This paper expands the strength of deep convolutional neural networks CNNs to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations.
We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well.
The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition. We study the task of image inpainting, where an image with missing region is recovered with plausible context.
On the History of Unified Field Theories
Recent approaches based on deep neural networks have exhibited potential for producing elegant detail and are able to take advantage of background information, which gives texture information about missing region in the image. However, this kind of replacement is a local strategy and often performs poorly when the background information is misleading.
To this end, in this study, we propose to use a multi-scale image contextual attention learning MUSICAL strategy that helps to flexibly handle richer background information while avoid to misuse of it. However, such strategy may not promising in generating context of reasonable style.
To address this issue, both of the style loss and the perceptual loss are introduced into the proposed method to achieve the style consistency of the generated image. Furthermore, we have also noticed that replacing some of the down sampling layers in the baseline network with the stride 1 dilated convolution layers is beneficial for producing sharper and fine-detailed results.
Software-Defined Storage
Experiments on the Paris Street View, Places, and CelebA datasets indicate the superior performance of our approach compares to the state-of-the-arts. Deep supervised hashing has become an active topic in information retrieval. It generates hashing bits by the output neurons of a deep hashing network. During binary discretization, there often exists much redundancy between hashing bits that degenerates retrieval performance in terms of both storage and accuracy. A graph is constructed to represent the redundancy relationship between hashing bits that is used to guide the learning of a hashing network.
Specifically, it is dynamically learned by a novel mechanism defined in our active and frozen phases. According to the learned relationship, the NMLayer merges the redundant neurons together to balance the importance of each output neuron. Moreover, multiple NMLayers are progressively trained for a deep hashing network to learn a more compact hashing code from a long redundant code. Extensive experiments on four datasets demonstrate that our proposed method outperforms state-of-the-art hashing methods.
In this paper, we propose a novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation. It contains global, local and global-local branches.
They are jointly optimized and thus can capture multiple types of features with complementary information. In each branch, we employ a separate loss for each sub-network to extract the independent features and use a recurrent fusion to explore correlations among those region features. Considering that the pose variations may lead to misalignment in different regions, we design an Aligned Region Pooling operation to generate aligned region features. Moreover, a new large age dataset named Web-FaceAge owning more than K samples is collected under diverse scenes and spanning a large age range.
Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects VFX in film shots. We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0,1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment. We show the effectiveness of our proposed models by extensive numerical tests on our VFX dataset and two additional datasets with values that are also bounded in the [0,1] interval.
A session-based recommender system SBRS suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one implicit purpose.
Faster development
However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes e. Specifically, items e. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose.
On the History of Unified Field Theories
The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity. Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction.
In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model named RNS by considering user's intrinsic preference long-term and sequential patterns short-term. In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews.
Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models. Specifically, for each user, a generator recommends a set of diverse and relevant items by sequentially sampling from a personalized Determinantal Point Process DPP kernel matrix.
This kernel matrix is constructed by two learnable components: the general co-occurrence of diverse items and the user's personal preference to items. To learn the first component, we propose a novel pairwise learning paradigm using training pairs, and each training pair consists of a set of diverse items and a set of similar items randomly sampled from the observed data of all users. The second component is learnt through adversarial training against a discriminator which strives to distinguish between recommended items and the ground-truth sets randomly sampled from the observed data of the target user.
Experimental results show that PD-GAN is superior to generate recommendations that are both diverse and relevant. Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.
In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model DARec that is capable of extracting and transferring patterns from rating matrices only without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity.
Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue.
Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario.
Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.
The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
We study the problem of computing correlated strategies to commit to in games with multiple leaders and followers.