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We present an image captioning framework that generates captions under a given topic. The topic candidates are extracted from the caption corpus. A given image’s topics are then selected from these candidates by a CNN-based multi-label classifier. The input to the caption generation model is an image-topic pair, and the output is a caption of the image.
In this paper, we propose a Group-Sparse Representation-based method with applications to Face Recognition (GSR-FR). The novel sparse representation variational model includes a non-convex sparsity-inducing penalty and a robust non-convex loss function. The penalty encourages group sparsity by using an approximation of the
Most variational formulations for structure-texture image decomposition force the structure images to have small norm in some functional spaces and to share a common notion of edges, i.e., large-gradients or large-intensity differences. However, such a definition makes it difficult to distinguish structure edges from oscillations that have fine spatial scale but high contrast. In this paper, we introduce a new model by learning deep variational priors for structure images without explicit training data. An alternating direction method of a multiplier algorithm and its modular structure are adopted to plug deep variational priors into an iterative smoothing process.
Hashing is a promising approach for compact storage and efficient retrieval of big data. Compared to the conventional hashing methods using handcrafted features, emerging deep hashing approaches employ deep neural networks to learn both feature representations and hash functions, which have been proven to be more powerful and robust in real-world applications.
The IEEE Signal Processing Society congratulates the following recipients who will receive the 2018 IEEE Signal Processing Society paper awards for their paper published in the IEEE Transactions on Signal Processing. Presentation of the paper awards will take place at ICASSP 2019 in Brighton, U.K.
A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations.
This paper discusses greedy methods for sensor placement in linear inverse problems. We comprehensively review the greedy methods in the sense of optimizing the mean squared error (MSE), the volume of the confidence ellipsoid, and the worst-case error variance. We show that the greedy method of optimizing an MSE related cost function can find a near-optimal solution.
Linear canonical transforms (LCTs) are of importance in many areas of science and engineering with many applications. Therefore, a satisfactory discrete implementation is of considerable interest. Although there are methods that link the samples of the input signal to the samples of the linear canonical transformed output signal, no widely-accepted definition of the discrete LCT has been established.
The Signal Processing research group at the Universität Hamburg (http://uhh.de/inf-sp) is hiring a research associate (PhD candidate) for a project on Phase-Aware Speech Enhancement.
Lecture Date: May 19, 2019
Chapter: ChengDu
Chapter Chair: Qian He
Topic: Cyber Attacks on Internet of Things Sensor Systems for Inference
Many applications generate large data sets from which information needs to be extracted. The emerging field of structured data science extends signal processing to data science. The opening for an Assistant Professor is intended to further develop this area. Excellently qualified but more senior researchers are also invited to apply.
Lecture Date: April 16, 2019
Chapter: Shanghai
Chapter Chair: Xinbing Wang
Topic: Hyperspectral Unmixing: Insights and Beyond
Lecture Date: April 18-19, 2019
Chapter: Nanjing
Chapter Chair: Luxi Yang
Topic: Hyperspectral Unmixing: Insights and Beyond
Lecture Date: April 2, 2019
Chapter: Republic of Macedonia Joint
Chapter Chair: Konstantinos Drossos
Topic: Radar and Communication Systems in Spectral Overlap
White Paper Due: August 8, 2019
Publication Date: September 2020
CFP Document