Abstract: Provided are a trajectory prediction method, an electronic device, and a storage medium. The method including that: a motion intention of an object is determined according to time-series location information and time-series posture information of the object, where the time-series location information is position information of the object at different time points within a preset time period, and the time-series posture information is posture information of the object at different time points within the preset time period, where the posture information at different time points includes orientation information of multiple parts of the object at the different time points; and a future trajectory of the object is determined according to the time-series location information, the time-series posture information and the motion intention.

Abstract: The present disclosure relates to a video repair method and apparatus, an electronic device, and a storage medium.

Abstract: An image processing method and device. In the method, a vector to be edited in a latent space of an image generative network and a first target decision boundary of a first target attribute in the latent space are acquired. The first target attribute includes a first category and a second category, the latent space is divided into a first subspace and a second subspace by the first target decision boundary, the first target attribute of vectors in the first subspace is of the first category and the first target attribute of vectors in the second subspace is of the second category; the vector to be edited in the first subspace is moved to the second subspace, an edited vector is obtained; and the edited vector is input to the image generative network, and a target image is obtained.

Abstract: Provided are embodiments including a computer-implemented method for performing recognition. The computer-implemented method includes receiving video data, and performing, at a pre-attention prediction module, a pre-attention prediction for the video data to generate first prediction priors. The computer-implemented method also includes receiving, at a dual attention module, data including the video data and data from the pre-attention prediction to generate attention maps, wherein the attention maps indicate a region of interest of a frame of the video data, wherein the dual attention module generates enhanced feature representations, and performing, at a post-attention prediction module, a post-attention prediction from data from the dual attention module based at least in part on the enhanced feature representation. Also provided are embodiments for a system and a computer program produce for performing recognition.

Abstract: A search method, a search device, a storage medium and a computer program. The search method includes: determining a first similarity between text and at least one video, the text being used for representing a search condition; determining a first character interaction graph of the text and a second character interaction graph of the at least one video; determining a second similarity between the first character interaction graph and the second character interaction graph; and according to the first similarity and the second similarity, determining a video matching the search condition from the at least one video.

Abstract: The present disclosure relates to a video repair method and apparatus, an electronic device, and a storage medium.

Abstract: Images uploaded to photo sharing websites often include some tags or sentence descriptions. In an example embodiment, these tags or descriptions, which might be relevant to the image contents, become the weak labels of these images. The weak labels can be used to identify concepts for the images using an iterative hard instance learning algorithm to discover visual concepts from the label and visual feature representations in the weakly labeled images. The visual concept detectors can be directly applied to concept recognition and detection.

Abstract: Images uploaded to photo sharing websites often include some tags or sentence descriptions. In an example embodiment, these tags or descriptions, which might be relevant to the image contents, become the weak labels of these images. The weak labels can be used to identify concepts for the images using an iterative hard instance learning algorithm to discover visual concepts from the label and visual feature representations in the weakly labeled images. The visual concept detectors can be directly applied to concept recognition and detection.