Lemon Inc.

Beijing Youzhuju Network Technology Co., Ltd.

Song Bai

Jun Hao Liew

Chuhui Xue

Wenqing Zhang

Yi Shan Jiang

Yu Hao

Jiashi Feng

Yujun Shi

Dapeng Hu

Junfeng Wu

Zhongcong Xu

Bin Lu

According to embodiments of the disclosure, a method, system, device, medium and product for video processing are provided. The method includes extracting a plurality of feature maps from a plurality of frames of a video respectively; determining a plurality of frame-level features of a video instance in the plurality of frames based on the plurality of feature maps respectively, a frame-level feature in each of the frames representing feature information of the video instance in the frame; determining a video-level feature of the video instance by aggregating the plurality of frame-level features, the video-level feature representing feature information of the video instance across the plurality of frames; and determining an analysis result for the video instance in the plurality of frames based at least on the video-level feature.

Methods, systems, devices, media and products for video processing

A method is proposed for sample processing. A first group of data are received, here data in the first group of data comprises a sample and a classification of the sample, and the classification belonging to a first group of classifications in a plurality of classifications associated with the data. A plurality of data with the classification are selected from the first group of data. A first and a second loss function are determined for training a classification model that represents an association relationship between samples and classifications of the samples based on a plurality of samples comprised in the plurality of data and the classification, the first and second loss functions represent classification accuracy and a feature distribution for the classification model. The classification model is trained based on the first and second loss functions. Therefore, the accuracy of the classification model may be increased.

Systems and methods for generating a classification model using primary and secondary loss functions

Embodiments of the present disclosure relate to validation of unsupervised adaptive models. According to example embodiments of the present disclosure, unlike methods validating with the seen target data, the present disclosure synthesizes new samples by mixing the target samples and pseudo labels. The accuracy between model predictions of mixed samples and the mixed labels are measured for model selection, and the accuracy score may be called PseudoMix. PseudoMix enjoys the combined inductive bias of previous methods. Experiments demonstrate that PseudoMix can keep state-of-the-art performance across different validation settings.

Validation of unsupervised adaptive models

Generating a multi-dimensional video using a multi-dimensional video generative model for, including, but not limited to, at least one of static portrait animation, video reconstruction, or motion editing. The method including providing data into the multi-dimensionally aware generator of the multi-dimensional video generative model, and generating the multi-dimensional video from the data by the multi-dimensionally aware generator. The generating of the multi-dimensional video includes inverting the data into a latent space of the multi-dimensionally aware generator, synthesizing content of the multi-dimensional video using an appearance component of the multi-dimensionally aware generator and corresponding camera pose and formulating an intermediate appearance code, developing a synthesis layer for encoding a motion component of the multi-dimensionally aware generator at a plurality of timesteps and formulating an intermediate motion code, introducing temporal dynamics into the intermediate appearance code and the intermediate motion code, and generating multi-dimensionally aware spatio-temporal representations of the data.

Multi-dimensional generative framework for video generation

Embodiments of the present disclosure relate to a method, device and computer readable storage medium of scene text detection. In the method, a first visual representation of a first image is generated with an image encoding process. A first textual representation of a first text unit in the first image is generated with a text encoding process based on a first plurality of symbols obtained by masking a first symbol of a plurality of symbols in the first text unit. A first prediction of the masked first symbol is determined with a decoding process based on the first visual and textual representations. At least the image encoding process is updating according to at least a first training objective to increase at least similarity of the first prediction and the masked first symbol.

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Singapore, Singapore

Song Bai