Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

originality

埋葬

End-to-End Active Speaker Detection,by utilizing audiovisual data but relying exclusively on audio annotations. We achieve this by modelling the direct relationship between the audio signal and the possible sound sources (speakers), as well as introducing a contrastive loss.

出生

macular-edema

Adaptive Fine-Grained Sketch-Based Image Retrieval,implify the MAML training in the inner loop to make it more stable and tractable. (2) The margin in our contrastive loss is also meta-learned with the rest of the model. (3) Three additional regularisation losses are introduced in the outer loop, to make the meta-learned FG-SBIR model more effective

incite

,Quantized GAN for?Complex Music Generation from?Dance Videos,, we assess the generative qualities of our proposal against alternatives. The attained quantitative results, which measure the music consistency, beats correspondence, and music diversity, demonstrate the effectiveness of our proposed method. Last but not least, we curate a challenging dance-music

藝術(shù)

,Uncertainty-Aware Multi-modal Learning via?Cross-Modal Random Network Prediction,training process. From a technical point of view, CRNP is the first approach to explore random network prediction to estimate uncertainty and to combine multi-modal data. Experiments on two 3D multi-modal medical image segmentation tasks and three 2D multi-modal computer vision classification tasks

緯度

,Localizing Visual Sounds the?Easy Way,or improved precision. Our simple and effective framework achieves state-of-the-art performance on two popular benchmarks, Flickr SoundNet and VGG-Sound Source. In particular, we improve the CIoU on Flickr SoundNet from 76.80% to 83.94%, and on VGG-Sound Source from 34.60% to 38.85%. Code and pretra

容易做

,Remote Respiration Monitoring of?Moving Person Using Radio Signals,mework to capture the mapping from radio signals to respiration while excluding the GM components in a self-supervised manner. We test the proposed model based on the newly collected and released datasets under real-world conditions. This study is the first realization of the nRRM task for moving/oc

認識

lobster

Telepresence Video Quality Assessment,-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels.