Beijing Tusen Zhitu Technology Co., Ltd.

Tusimple, Inc.

Zehao Huang

Naiyan Wang

Hongyang Li

Jiawei He

Zhenwei Shen

A method and device for training a neural network are disclosed. The method comprises: selecting, by a training device, a teacher network performing the same functions of a student network; and iteratively training the student network and obtaining a target network, through aligning distributions of features between a first middle layer and a second middle layer corresponding to the same training sample data, so as to transfer knowledge of features of a middle layer of the teacher network to the student network.

Method and device for student training networks with teacher networks

Embodiments of the present invention disclose a binocular image matching method, apparatus, device, and storage medium. The method comprises: performing target detection on a first image to obtain a first bounding box of a target in the first image; determining a second bounding box corresponding to the first bounding box in a second image; and obtaining a third bounding box of the target in the second image by regressing the second bounding box. The technical solutions realize accurate matching of targets in a binocular image without requiring performing target detection on both images in the binocular image, and then use a matching algorithm to match the targets detected in the two images, thereby greatly reducing the calculation overhead of target matching in the binocular image.

Binocular image matching method, device, and storage medium

An embodiment of the present disclosure discloses a training method for a multi-object tracking model and a multi-object tracking method. The multi-object tracking method comprises: constructing an object graph according to objects to be tracked in a current frame, wherein the vertexes of the object graph correspond to the objects to be tracked, and edge features of the edges between the two vertexes comprise an attribute relationship between the two vertexes; performing graph matching on the object graph and a tracklet graph to calculate matching scores between the object to be tracked and the tracked tracklet in the tracklet graph, wherein the vertexes of the tracklet graph correspond to tracked tracklets, and the edge features of the edges between the two vertexes comprise an attribute relationship between the two vertexes; and determining the matched tracklet of the object to be tracked according to the matching scores.

Training method for multi-object tracking model and multi-object tracking method

The present application relates to a method and an apparatus for detecting corner points of a lane line, an electronic device and a storage medium. The method comprises: obtaining a road image to be detected; inputting the road image into a pre-trained neural network model to obtain a heat map prediction result, wherein the heat map prediction result comprises a probability of each pixel belonging to a specific corner point category; determining a plurality of different categories of corner points in the road image according to the probability of each pixel belonging to a specific corner point category in the heat map prediction result; and grouping the plurality of different categories of corner points according to a predetermined rule to obtain at least one corner point group. The present application can solve the problem that the corner points of the lane lines cannot be accurately and quickly detected.

Method and apparatus for detecting corner points of lane lines, electronic device and storage medium

The present disclosure provides a method and an apparatus for training a neural network and a computer server. The method includes: selecting automatically input data for which processing by the neural network fails, to obtain a set of data to be annotated; annotating the set of data to be annotated to obtain a new set of annotated data; acquiring a set of newly added annotated data containing the new set of annotated data, and determining a union of the set of newly added annotated data and a set of training sample data for training the neural network in a previous period as a set of training sample data for a current period; and training the neural network iteratively based on the set of training sample data for the current period, to obtain a neural network trained in the current period.

Method and apparatus for training neural network and computer server

The present disclosure provides a method and an apparatus for sampling training data and a computer server. The method includes: inputting a video to a target detection model to obtain a detection result for each frame of image; inputting the detection results for all frames of images in the video to a target tracking model, to obtain a tracking result for each frame of image; and for each frame of image in the video: matching the detection result and the tracking result for the frame of image, and when the detection result and the tracking result for the frame of image are inconsistent with each other, determining the frame of image as a sample image to be marked, for which processing by the target detection model is not optimal.

Growing community of inventors

Beijing, China

Zehao Huang