Abstract: Methods for face detection to address privacy in publishing image datasets is described. A method may include face classification in an online marketplace. A server system may receive, from a seller user device, a listing including an image for the online marketplace. The server system may classify, by at least one processor that implement a distribution-balance trained machine learning model, each human face candidate within the image as being one of a private human face or a non-private human face. The server system may receive, from a buyer user device, a search query that is mapped to the listing in the online marketplace. The server system may transmit, to the buyer user device, a query response including the listing that includes the image determined to not include any private human faces or obscures any private human faces within the image based on the classifying.

Abstract: Methods for face detection to address privacy in publishing image datasets is described. A method may include face classification in an online marketplace. A server system may receive, from a seller user device, a listing including an image for the online marketplace. The server system may classify, by at least one processor that implement a distribution-balance trained machine learning model, each human face candidate within the image as being one of a private human face or a non-private human face. The server system may receive, from a buyer user device, a search query that is mapped to the listing in the online marketplace. The server system may transmit, to the buyer user device, a query response including the listing that includes the image determined to not include any private human faces or obscures any private human faces within the image based on the classifying.

Abstract: Methods for face detection to address privacy in publishing image datasets is described. A method may include face classification in an online marketplace. A server system may receive, from a seller user device, a listing including an image for the online marketplace. The server system may classify, by at least one processor that implement a distribution-balance trained machine learning model, each human face candidate within the image as being one of a private human face or a non-private human face. The server system may receive, from a buyer user device, a search query that is mapped to the listing in the online marketplace. The server system may transmit, to the buyer user device, a query response including the listing that includes the image determined to not include any private human faces or obscures any private human faces within the image based on the classifying.

Abstract: Methods and systems for analyzing video-game videos in connection with facilitating video editing and creation and performing automated extraction of information of interest to facilitate labeling of and/or highlight generation for such videos is provided. According to one embodiment, a video, containing content pertaining to a video game, is received by a video-game video analysis system. Information regarding the status of the video game over time is received by retrieving game metadata through an API of the video game or by analyzing audio or visual features within the content. Multiple clips are automatically identified within the video for proposed inclusion within an edited version of the video based on the status of the video game over time. The edited version of the video is then generated by (i) joining the automatically identified clips or (ii) joining multiple user-selected clips, including at least one clip selected from the automatically identified clips.

Abstract: Systems, method, and devices for pairwise multi-task feature learning are described. The systems obtain a set of digital images, obtain a neural network, and select a pair of digital images, which includes a first image and a second image. Also, the systems forward propagate the first image through a first copy of the neural network, thereby generating a first output, and the systems forward propagate the second image through a second copy of the neural network, thereby generating a second output. Furthermore, the systems calculate a gradient of a joint loss function at a pairwise-constraint layer of the neural network based on the first output, on the second output, and on a target. Additionally, the systems modify the neural network based on the gradient.