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Found 46 papers, 33 papers with code
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
MarsQE: Semantic-Informed Quality Enhancement for Compressed Martian Image
Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars.
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain.
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
Blind Multimodal Quality Assessment of Low-light Images
Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained model is provided to generate text description.
Neural Characteristic Function Learning for Conditional Image Generation
The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information.
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting
In this paper, we propose a novel method namely PIRNet, which operates privacy-preserving image restoration in the steganographic domain.
DINN360: Deformable Invertible Neural Network for Latitude-Aware 360deg Image Rescaling
To alleviate this issue, we propose the first attempt at 360deg image rescaling, which refers to downscaling a 360deg image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360deg image given the LR variant.
DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus
Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality.
Progressive Training of A Two-Stage Framework for Video Restoration
As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method
E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start.
Ranked #2 on
Saliency Prediction
on SALECI
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i. e., a progressive quality assessment paradigm.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information.
Domain Adaptation for Underwater Image Enhancement
However, these methods ignore the significant domain gap between the synthetic and real data (i. e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios.
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video
To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features.
Saliency-Guided Image Translation
In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map.
Generative Adversarial Network
Image-to-Image Translation
+1
Deep Homography for Efficient Stereo Image Compression
To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution
In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands.
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model
Inspired by the findings of our investigation, we propose a novel multi-modal video saliency model consisting of three branches: visual, audio and face.
Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images
To alleviate this issue, we propose a spatial attention-based perceptual quality prediction network for non-reference quality assessment on ODIs (SAP-net).
HiNet: Deep Image Hiding by Invertible Network
Capacity, invisibility and security are three primary challenges in image hiding task.
Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video
In this paper, we focus on enhancing the perceptual quality of compressed video.
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming.
DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC
In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization.
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods.
Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach
In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos.
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
That is, there is not yet a general evaluation platform for research on multi-agent intelligence.
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i. e., playing games without extrinsic rewards but evaluated with extrinsic rewards.
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning
According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward.
Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment.
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video.
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
High efficiency video coding (HEVC) has brought outperforming efficiency for video compression.
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Ranked #5 on
Video Enhancement
on MFQE v2
Diversity-Driven Extensible Hierarchical Reinforcement Learning
However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Understanding and Predicting the Memorability of Outdoor Natural Scenes
In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores.
DeepVS: A Deep Learning Based Video Saliency Prediction Approach
Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets.
What Makes Natural Scene Memorable?
Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable.
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences.
Multi-Frame Quality Enhancement for Compressed Video
In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE).
Ranked #6 on
Video Enhancement
on MFQE v2
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach
Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
Enhancing Quality for HEVC Compressed Videos
Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos.
Multimedia
Reducing Complexity of HEVC: A Deep Learning Approach
Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network.
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames.
Predicting Salient Face in Multiple-Face Videos
On the other hand, we find that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces.
Learning to Predict Saliency on Face Images
For modeling attention on faces and facial features, the proposed method learns the Gaussian mixture model (GMM) distribution from the fixations of eye tracking data as the top-down features for saliency detection of face images.
