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Found 13 papers, 7 papers with code
DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement
To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features, limiting the generalization and adaptability of the model.
Object Segmentation by Mining Cross-Modal Semantics
In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Ranked #7 on
RGB Salient Object Detection
on PASCAL-S
Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling
Objective quality assessment of 3D point clouds is essential for the development of immersive multimedia systems in real-world applications.
Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric
Arbitrary neural style transfer is a vital topic with great research value and wide industrial application, which strives to render the structure of one image using the style of another.
BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload.
Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation
The DDB-Net contains three modules, i. e., an image decomposition module, a feature encoding module, and a bilinear pooling module.
A Brief Survey on Adaptive Video Streaming Quality Assessment
In this paper, we provide a brief overview of adaptive video streaming quality assessment.
Towards Top-Down Just Noticeable Difference Estimation of Natural Images
Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map.
No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness.
Progressive Self-Guided Loss for Salient Object Detection
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images.
Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions.
A Dilated Inception Network for Visual Saliency Prediction
In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction.
