- Read more about PLNL-3DSSD: Part-Aware 3D Single Stage Detector Using Local And Non-Local Attention
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Poster.pdf
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- Read more about Adaptive Signal Variances: CNN Initialization Through Modern Architectures
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Deep convolutional neural networks (CNNs), renowned for their consistent performance, are widely understood by practitioners that the stability of learning depends on the initialization of the model parameters in each layer. Kaiming initialization, the de facto standard, is derived from a much simpler CNN model which consists of only the convolution and fully connected layers. Compared to the current CNN models, the basis CNN model for the Kaiming initialization does not include the max pooling or global average pooling layers.
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- Read more about PSEUDO-LABEL GENERATION-EVALUATION FRAMEWORK FOR CROSS DOMAINWEAKLY SUPERVISED OBJECT DETECTION slides
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- Read more about SINGLE IMAGE SUPER-RESOLUTION VIA GLOBAL-CONTEXT ATTENTION NETWORKS
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In the last few years, single image super-resolution (SISR) has benefited a lot from the rapid development of deep convolutional neural networks (CNNs), and the introduction of attention mechanisms further improves the performance of SISR. However, previous methods use one or more types of attention independently in multiple stages and ignore the correlations between different layers in the network.
1950_poster.pdf
1950_ICIP21.pdf
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- Read more about Subband Adaptive Enhancement Of Low Light Images Using Wavelet-Based Convolutional Neural Networks
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Images captured in low light condition have a narrow dynamic range with a dark tone, which are seriously degraded by noise due to the low signal-to-noise ratio (SNR). Discrete wavelet transform (DWT) is invertible and thus is able to decompose an image into subbands without information loss minimizing redundancy. In this paper, we propose subband adaptive enhancement of low light images using wavelet-based convolutional neural networks. We adopt DWT to achieve joint contrast enhancement and noise reduction. We combine DWT with convolutional neural networks (CNNs), i.e.
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Tone-mapping is one of the prevailing methods to overcome high dynamic range imaging limitations over low dynamic range display devices, but the tone-mapped output image may suffer from saturated regions with texture and color information loss. In this paper, a novel approach is proposed to solve the so-called clipping problem in tone-mapped high dynamic range images. A successful saturation correction framework, which relies on linear embeddings, difference of pixel intensities and gradient-guided block-search, is developed as a post-processing technique to tone-mapping.
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- Read more about IMAGE FUSION THROUGH LINEAR EMBEDDINGS
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This paper proposes an effective technique for multi-exposure image fusion and visible-infrared image fusion problems. Multi-exposure fusion algorithms generally extract faulty weight maps when the input stack contains multiple and/or severely over-exposed images. To overcome this issue, an alternative method is developed for weight map characterization and refinement in addition to the perspectives of linear embeddings of images and adaptive morphological masking. This framework has then been extended to the visible and infrared image fusion problem.
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- Read more about Training an Embedded Object Detector for Industrial Settings Without Real Images
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In an industrial environment, object detection is a challenging task due to the absence of real images and real-time requirements for the object detector, usually embedded in a mobile device. Using 3D models, it is however possible to create a synthetic dataset to train a neural network, although the performance on real images is limited by the domain gap. In this paper, we study the performance of a Convolutional Neural Network (CNN) designed to detect objects in real-time: Single-Shot Detector (SSD) with a MobileNet backbone.
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Erasing text from images is a common image-editing task in film industry and shared media. Existing text-erasing models either tend to produce artifacts or fail to remove all the text in real-world images. In this paper, we follow a two-stage text erasing framework that first masks the text by segmentation, and then inpaints the masked region to create a text-erased image. Our proposed text mask generator is designed to accurately cover text, which combined with inpainting, can produce reliable text-erased results.
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- Read more about A Fast and Efficient Super-Resolution Network using Hierarchical Dense Residual Learning
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