Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the other hand, transformers effectively capture global dependencies through self-attention but suffer from high redundancy and computational costs. Thus, to leverage the advantages of both CNNs and transformers, we propose a unified model (UniCon-Former) that aims to provide robust and efficient performance on dynamic hand gesture recognition. The unified approach helps the model to learn both local and global features. At the beginning of each transformer stage, the convolution projections help in decreasing the dimension of the input vectors of the transformer block. This creates a pyramidal structure at each transformer stage. These features enable the UniCon-Former to reduce resource usage than vanilla transformers, making it flexible for learning multi-scale and high-resolution features, which is required in hand gesture recognition. We have performed experiments with NVGesture and Briareo datasets and achieved state-of-the-art results with fewer parameters and MACs.
Mallika Garg, Debashis Ghosh, P. Pradhan· 0 citations
Images captured in hazy environment are often degraded, thereby impacting several applications of computer vision, like image segmentation, classification, object recognition, and many others. Accordingly, various techniques and algorithms for image dehazing were developed over the years - from old days traditional to the recent deep learning methods. Although deep learning technology have been proved to be better performing than the traditional methods, they are plagued by several drawbacks like high computational demand, limiting their usage for real-time applications. In view of this, here we develop a lightweight encoder-decoder network (LED-Net) for single-image haze removal in real-time using deep learning technology. To make it lightweight, we incorporate the depth-wise convolution in each convolution block. The performance of LED-Net is assessed on the basis of quantitative performance metrics, viz., PSNR and SSIM), qualitative analysis (visual quality), and real-time performance metrics such as inference time and throughput. Results obtained in our experiments show that, in comparison to some other previous state-of-the-art methods, our model offers a good compromise between output image quality and architectural complexity.
Ayush Tripathy, Debashis Ghosh, M. Purohit· 2026 6th International Confe...· 0 citations
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