
- Read more about DEEP UNFOLDING NETWORK WITH PHYSICS-BASED PRIORS FOR UNDERWATER IMAGE ENHANCEMENT
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We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the underwater image enhancement task as a joint optimization problem, based on the image formation model with physical model and underwater-related priors. Then, we solve the optimization problem iteratively. Finally, we unfold the iterative algorithm so that, at each iteration, the optimization variables and regularizers for image priors are updated by closed-form solutions and learned deep networks, respectively.
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- Read more about Feature integration via back-projection orderingmulti-modal Gaussian process latent variable modelfor rating prediction
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In this paper, we present a method of feature integration via backprojection ordering multi-modal Gaussian process latent variable model (BPomGP) for rating prediction. In the proposed method, to extract features reflecting the users’ interest, we use the known ratings assigned to the viewed contents and users’ behavior information while viewing the contents which is related to the users’ interest. BPomGP has two important approaches. Unlike the training phase, where the above two types of heterogeneous information are available, behavior information is not given in the test phase.
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- Read more about SELECTING A DIVERSE SET OF AESTHETICALLY-PLEASING AND REPRESENTATIVE VIDEO THUMBNAILS USING REINFORCEMENT LEARNING
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This paper presents a new reinforcement-based method for video thumbnail selection (called RL-DiVTS), that relies on estimates of the aesthetic quality, representativeness and visual diversity of a small set of selected frames, made with
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- Read more about WHEN VISIBLE-TO-THERMAL FACIAL GAN BEATS CONDITIONAL DIFFUSION
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Thermal facial imagery offers valuable insight into physiological states such as inflammation and stress by detecting emitted radiation in the infrared spectrum, which is unseen in the visible spectra. Telemedicine applications could benefit from thermal imagery, but conventional computers are reliant on RGB cameras and lack thermal sensors. As a result, we propose the Visible-to-Thermal Facial GAN (VTF-GAN) that is specifically designed to generate high-resolution thermal faces by learning both the spatial and frequency domains of facial regions, across spectra.
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- Read more about Early Detection of Cars Exiting Road-side Parking
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Vehicles suddenly exiting road-side parking constitute a hazardous situation for vehicle drivers as well as for Connected and Autonomous Vehicles (CAV). In order to improve the awareness of road users, we propose an original cooperative information system based on image processing to monitor vehicles parked on the road-side and on communication for sending early warning to vehicles on the road about vehicles leaving their parking space.
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- Read more about SEM-CS: SEMANTIC CLIPSTYLER FOR TEXT-BASED IMAGE STYLE TRANSFER
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CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requir- ing a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill- over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIP- Styler (Sem-CS), that performs semantic style transfer.
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- Read more about Simple Self-Distillation Learning for Noisy Image Classification
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- Read more about Simple Self-Distillation Learning for Noisy Image Classification
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- Read more about Annotated Pedestrians: A Dataset for Soft Biometrics Estimation for Varying Distances
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Following the significance of soft biometrics to facilitate seamless recognition or retrieval, the need for multi-modality annotated datasets is increasing - to evaluate any standalone soft biometrics system. Although, large-size datasets like PETA were annotated to evaluate soft biometrics systems, however, they were mainly annotated for global soft biometrics such as gender and age and for clothing modality.
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- Read more about Multi-dimensional Signal Recovery Using Low-rank Deconvolution
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In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties from convolutional sparse coding and low-rank approximation methods as in this setting signals are decomposed in a set of filters convolved with a set of low-rank tensors. We show its advantages by learning compressed video representations and solving image in-painting problems.
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