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Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions (< 1 0), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant. [ABSTRACT FROM AUTHOR]

Summary: Network‐on‐chip (NoC) is adopted as a flexible and effective communication backbone by multiprocessor systems with core counts ranging from a few to hundreds. The performance of NoC based systems greatly rely on the communication between cores when two dependent tasks of an application are mapped to these cores. Hence, application mapping becomes a critical issue in NoC‐based systems as it affects the overall performance of the system. Application mapping aims to reduce the communication among the cores by carefully mapping the highly communicating tasks. Most of the existing static mapping approaches consider only single‐task platforms. In this article, we propose an efficient multi‐task static mapping algorithm called SpecMap$$ SpecMap $$, for regular 2D NoC systems. SpecMap$$ SpecMap $$ consists of three stages namely, (i) graph partitioning, (ii) topology clustering, and (iii) mapping. In the graph partitioning stage, SpecMap$$ SpecMap $$ accurately identifies and groups the highly communicating tasks based on spectral graph partitioning. Then, the clustering and mapping stages allocate the most communicating tasks in the same or adjacent multi‐tasking cores. It also eliminates the interference between multiple applications by mapping them into different clusters of the NoC network. Simulations of SpecMap$$ SpecMap $$ have been conducted on different synthetic and real‐life applications using Gem5 simulator. The simulation results show considerable improvements for SpecMap in terms of communication cost, average network latency, energy consumption, throughput, and execution time as compared to the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Resolution enhancement of Earth’s images from synthetic aperture radars (SARs), used for applications that require scene interpretations and detailed analysis, fails due to the presence of inherent speckle noise. An inexpensive alternative solution to the problem is to use super-resolution (SR) algorithms that deal with speckle. A novel approach to augment kernel regression into the Adaptive Importance Sampling Unscented Kalman Filter (AISUKF) framework for SAR image SR has been presented in this letter. We have experimented with three different nonlinear kernel regressions, namely, arc-cosine kernel, radial basis function kernel, and steering kernel (SK) regressions. Empirical results suggest that AISUKF with SK regression is more appropriate for the abovementioned SR problem resulting in a better denoised and more detail-preserved output. [ABSTRACT FROM AUTHOR]