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Found 8 papers, 3 papers with code
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human understandable concepts.
Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing
The proposed model makes use of cross-stitch units that enable multi-task learning across two separate DRN models, each tasked for single image deraining and desnowing, respectively.
Towards Full-to-Empty Room Generation with Structure-Aware Feature Encoding and Soft Semantic Region-Adaptive Normalization
In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module.
Serverless Streaming for Emerging Media: Towards 5G Network-Driven Cost Optimization
Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference, entertainment, gaming and other fields.
Networking and Internet Architecture Multimedia
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
This is accomplished by a soft, differentiable procrustes analysis that regularizes the segmentation and achieves higher extrinsic calibration performance in expanded sensor placement configurations, while being unrestricted by the number of sensors of the volumetric capture system.
A Low-Cost, Flexible and Portable Volumetric Capturing System
Multi-view capture systems are complex systems to engineer.
Self-Supervised Deep Depth Denoising
Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference.
Non-linear Convolution Filters for CNN-based Learning
However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms.
