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Found 24 papers, 12 papers with code
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.
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems.
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection.
TIP: Text-Driven Image Processing with Semantic and Restoration Instructions
Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement.
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
Our conditional-task learning and distillation approach outperforms previous distillation methods, achieving a new state-of-the-art in producing high-quality images with very few steps (e. g., 1-4) across multiple tasks, including super-resolution, text-guided image editing, and depth-to-image generation.
MULLER: Multilayer Laplacian Resizer for Vision
Specifically, we select a state-of-the-art vision Transformer, MaxViT, as the baseline, and show that, if trained with MULLER, MaxViT gains up to 0. 6% top-1 accuracy, and meanwhile enjoys 36% inference cost saving to achieve similar top-1 accuracy on ImageNet-1k, as compared to the standard training scheme.
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception.
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers
The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
MaxViT: Multi-Axis Vision Transformer
We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module.
Ranked #1 on
Object Detection
on COCO 2017
Perceptual Quality Assessment of UGC Gaming Videos
In recent years, with the vigorous development of the video game industry, the proportion of gaming videos on major video websites like YouTube has dramatically increased.
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles.
Ranked #1 on
3D Object Detection
on V2XSet
ROMNet: Renovate the Old Memories
Renovating the memories in old photos is an intriguing research topic in computer vision fields.
MAXIM: Multi-Axis MLP for Image Processing
In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
Ranked #1 on
Deblurring
on HIDE (trained on GOPRO)
FAVER: Blind Quality Prediction of Variable Frame Rate Videos
Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales.
Predicting Eye Fixations Under Distortion Using Bayesian Observers
Visual attention is very an essential factor that affects how human perceives visual signals.
Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus.
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications.
Ranked #4 on
Video Quality Assessment
on LIVE Livestream
Adaptive Debanding Filter
Banding artifacts, which manifest as staircase-like color bands on pictures or video frames, is a common distortion caused by compression of low-textured smooth regions.
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms.
Ranked #11 on
Video Quality Assessment
on YouTube-UGC
BBAND Index: A No-Reference Banding Artifact Predictor
Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos.
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores.
Fitness Done Right: a Real-time Intelligent Personal Trainer for Exercise Correction
Finally, key values for key features of the two poses are computed correspondingly in the pose error detection part, which helps give correction advice.
