Computer Vision
Content about computer vision at Prime Video.
In this study towards better quality assessment of high-quality videos, a subjective study was conducted focusing on high-quality HD and UHD content with the Degradation Category Rating (DCR) protocol.
We present a new method and a large-scale database to detect audio-video synchronization (A/V sync) errors in tennis videos.
In this work, we present a thorough survey on DNN based VADs on DEC data in terms of their accuracy, Area Under Curve (AUC), noise sensitivity, and language agnostic behavior.
The goal of this work is to assess the importance of spatial and temporal learning for production-related VQA. In particular, it assesses state-of-the-art UGC video quality assessment perspectives on LIVE-APV dataset, demonstrating the importance of learning contextual characteristics from each video frame, as well as capturing temporal correlations between them.
We propose a simple yet effective approach that uses single-frame depth-prior obtained from a pretrained network to significantly improve geometry-based SfM for our small-parallax setting.
We show that, (a) audio based approach results in superior performance compared to other baselines, (b) benefit due to audio model is more pronounced on global multi-lingual data compared to English data and (c) the multi-modal model results in 63% rating accuracy and provides the ability to backfill top 90% Stream Weighted Coverage titles in PV catalog with 88% coverage at 91% accuracy.
In this work, we propose LipNeRF, a lip-syncing NeRF that bridges the gap between the accurate lip synchronization of GAN-based methods and the accurate 3D face modeling of NeRFs.
In this paper, we consider using a multiscale approach to reduce complexity while maintaining coding efficiency.
In this work, we describe the various factors which affect the suitability of a face image for recognition by humans. We propose efficient solutions which can solve the problem without the use of ground truth data. We train a regression model using weak supervision provided by heuristics based on features which affect face quality. Finally, we use professional photography techniques to create standardized and aesthetically pleasing profile images.
We introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft image-text alignments to more efficiently learn robust representations from noisy data.
This work presents a No-Reference model to detect audio artifacts in video. The model, based upon a Pretrained Audio Neural Network, classifies a 1-second audio segment as either No Defect, Audio Hum, Audio Hiss, Audio Distortion or Audio Clicks. The model achieves a balanced accuracy of 0.986 on our proprietary simulated dataset.
In this work, we pose intro and recap detection as a supervised sequence labeling problem and propose a novel end-to-end deep learning framework to this end.