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Found 47 papers, 15 papers with code
Harnessing the Power of Large Vision Language Models for Synthetic Image Detection
This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2.
Bi-LORA: A Vision-Language Approach for Synthetic Image Detection
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images.
Generative AI for Immersive Communication: The Next Frontier in Internet-of-Senses Through 6G
Over the past two decades, the Internet-of-Things (IoT) has been a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging.
ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks
Omnidirectional or 360-degree video is being increasingly deployed, largely due to the latest advancements in immersive virtual reality (VR) and extended reality (XR) technology.
NERV++: An Enhanced Implicit Neural Video Representation
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint.
UAV Immersive Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges
However, achieving this immersive experience necessitates encoding omnidirectional videos in high resolution, leading to increased bitrates.
Bitrate Ladder Prediction Methods for Adaptive Video Streaming: A Review and Benchmark
A key component of HAS is the bitrate ladder, which provides the encoding parameters (e. g., bitrate-resolution pairs) to encode the source video.
Video Quality Assessment and Coding Complexity of the Versatile Video Coding Standard
The results consistently demonstrate that VVC outperforms HEVC, achieving bit-rate savings of up to 40% on the subjective quality scale, particularly at realistic bit-rates and quality levels.
Efficient Predictive Coding of Intra Prediction Modes
The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential for efficient compression of Intra coded blocks.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression
Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their conventional counterparts in rate-distortion performance.
AICT: An Adaptive Image Compression Transformer
Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT).
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression
Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs.
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images.
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
Recently, with the growing popularity of mobile devices as well as video sharing platforms (e. g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet.
Ranked #9 on
Video Quality Assessment
on LIVE-VQC
(using extra training data)
Ensemble Learning for Efficient VVC Bitrate Ladder Prediction
When compared to static bitrate ladder, the proposed method can offer about 13% bitrate reduction in terms of BD-BR with a negligible additional computational overhead.
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.
Performance Analysis of Optimized Versatile Video Coding Software Decoders on Embedded Platforms
Hence, in July 2020 the Versatile Video Coding (VVC) standard was released providing up to 50% bit-rate saving for the same video quality compared to its predecessor High Efficiency Video Coding (HEVC).
Deep-based Film Grain Removal and Synthesis
In the context of video coding, the random nature of film grain makes it both difficult to preserve and very expensive to compress.
Federated Adversarial Training with Transformers
Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studied for convolutional neural network (CNN).
OpenVVC: a Lightweight Software Decoder for the Versatile Video Coding Standard
The new generation video coding standard, called Versatile Video Coding (VVC), has been developed by the Joint Video Experts Team, and offers coding capability beyond the previous generation High Efficiency Video Coding (HEVC) standard.
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability
Our framework considers a low-resolution signal encoded with VVC intra-mode as a base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as an enhancement-layer (EL) model.
Prediction-Aware Quality Enhancement of VVC Using CNN
The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate saving.
Lightweight Hardware Transform Design for the Versatile Video Coding 4K ASIC Decoders
The proposed module has been integrated in an ASIC UHD decoder targeting energy-aware decoding of VVC videos on consumer devices.
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising
Despite the enormous performance of deepneural networks (DNNs), recent studies have shown theirvulnerability to adversarial examples (AEs), i. e., care-fully perturbed inputs designed to fool the targetedDNN.
Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition
The multiple sources of the available databases raised several challenges for facial recognition task.
Ranked #1 on
Facial Expression Recognition (FER)
on RaFD
Versatile Video Coding Standard: A Review from Coding Tools to Consumers Deployment
The amount of video content and the number of applications based on multimedia information increase each day.
Reveal of Vision Transformers Robustness against Adversarial Attacks
For instance, we found that 1) Vanilla ViTs or hybrid-ViTs are more robust than CNNs under Lp-based attacks and under adaptive attacks.
SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos
Salient human detection (SHD) in dynamic 360{\deg} immersive videos is of great importance for various applications such as robotics, inter-human and human-object interaction in augmented reality.
A CNN-based Prediction-Aware Quality Enhancement Framework for VVC
This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Model Selection CNN-based VVC QualityEnhancement
Moreover, an optional Model Selection (MS) strategy is adopted to pick the best trained model among available ones at the encoder side and signal it to the decoder side.
CMA-Net: A Cascaded Mutual Attention Network for Light Field Salient Object Detection
In the past few years, numerous deep learning methods have been proposed to address the task of segmenting salient objects from RGB images.
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison
In this paper, we focus on image classification task and attempt to provide a survey for detection methods of test-time evasion attacks on neural network classifiers.
Learning Synergistic Attention for Light Field Salient Object Detection
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
Conditional Coding and Variable Bitrate for Practical Learned Video Coding
This paper introduces a practical learned video codec.
Multitask Learning for VVC Quality Enhancement and Super-Resolution
The proposed solution enables a good performance in both mitigating coding artifacts and super-resolution with fewer network parameters compared to traditional specialized architectures.
Conditional Coding for Flexible Learned Video Compression
This paper introduces a novel framework for end-to-end learned video coding.
Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
This new approach leads to five possible splits at each block depth and thereby improves the coding efficiency of VVC over that of the preceding High Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT) partitioning with a single split per block depth.
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
Moreover, the state-of-the-art detection techniques have been designed for specific attacks or broken by others, need knowledge about the attacks, are not consistent, increase model parameters overhead, are time-consuming, or have latency in inference time.
Quality-driven Variable Frame-Rate for Green Video Coding in Broadcast Applications
In this paper, we propose a Variable Frame Rate (VFR) solution to determine the minimum (critical) frame-rate that preserves the perceived video quality of HFR video.
Optical Flow and Mode Selection for Learning-based Video Coding
MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection.
ModeNet: Mode Selection Network For Learned Video Coding
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression.
Binary Probability Model for Learning Based Image Compression
In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables.
A Fixation-based 360° Benchmark Dataset for Salient Object Detection
Experimental results show a limitation of the current methods when used for SOD in panoramic images, which indicates the proposed dataset is challenging.
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification
However, all the works proposed in the literature for generating adversarial examples have used the $L_{p}$ norms ($L_{0}$, $L_{2}$ and $L_{\infty}$) as distance metrics to quantify the similarity between the original image and the adversarial example.
