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Found 38 papers, 34 papers with code
Extreme Compression of Adaptive Neural Images
The fundamental idea is to represent a signal as a continuous and differentiable neural network.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Event-Based Eye Tracking. AIS 2024 Challenge Survey
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge.
Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey
This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results.
Simple Image Signal Processing using Global Context Guidance
First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images.
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
BSRAW: Improving Blind RAW Image Super-Resolution
Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality.
H2O Open Ecosystem for State-of-the-art Large Language Models
The goal of this project is to boost open alternatives to closed-source approaches.
Efficient Baselines for Motion Prediction in Autonomous Driving
However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for real-time applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information.
Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery
Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region.
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Efficient multi-lens bokeh effect rendering and transformation
Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image.
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report
This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (x2 and x3 factors) in real-time on commercial GPUs.
Towards Real-Time 4K Image Super-Resolution
Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard.
Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices
We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones.
A Brief Overview of AI Governance for Responsible Machine Learning Systems
Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more.
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Perceptual Image Enhancement for Smartphone Real-Time Applications
Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
General Image Descriptors for Open World Image Retrieval using ViT CLIP
The Google Universal Image Embedding (GUIE) Challenge is one of the first competitions in multi-domain image representations in the wild, covering a wide distribution of objects: landmarks, artwork, food, etc.
Exploring Attention GAN for Vehicle Motion Prediction
The design of a safe and reliable Autonomous Driving stack (ADS) is one of the most challenging tasks of our era.
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.
Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction
Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars.
CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.
Conformer and Blind Noisy Students for Improved Image Quality Assessment
Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately.
Model-Based Image Signal Processors via Learnable Dictionaries
Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP).
An Embarrassingly Pragmatic Introduction to Vision-based Autonomous Robots
Autonomous robots are currently one of the most popular Artificial Intelligence problems, having experienced significant advances in the last decade, from Self-driving cars and humanoids to delivery robots and drones.
Weakly-Supervised Classification and Detection of Bird Sounds in the Wild.
It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution.
Exploring Vision Transformers for Fine-grained Classification
In this work, we propose a multi-stage ViT framework for fine-grained image classification tasks, which localizes the informative image regions without requiring architectural changes using the inherent multi-head self-attention mechanism.
Multi-attention Networks for Temporal Localization of Video-level Labels
The model performance is further improved by constructing multiple sets of attention networks.
