Abstract: The present application discloses a method and an apparatus for training a backbone network, an image processing method and apparatus, and a device. A weight selection cycle is set, where the weight selection cycle may include at least one backbone network training cycle. The backbone network is trained with sample data in the current weight selection cycle, and a cumulative weight adjustment amount for each weight in the backbone network in the current weight selection cycle is recorded. A target weight for which the cumulative weight adjustment amount meets a preset condition is selected from the backbone network based on the cumulative weight adjustment amount for each weight, and only the target weight in the backbone network is adjusted in a next weight selection cycle, to complete training of the backbone network in the next weight selection cycle based on the adjusted target weight.

Abstract: The present disclosure describes techniques for implementing video segmentation. A video is divided into a plurality of clips. Each of the plurality of clips comprises several frames. Axial-trajectory attention is applied to each of the plurality of clips by a first sub-model. Clip features corresponding to each of the plurality of clips are generated by the first sub-model. A set of object queries corresponding to each of the plurality of clips is generated based on the clip features by a transformer decoder. Trajectory attention is applied to refine sets of object queries corresponding to the plurality of clips by a second sub-model. Video-level segmentation results are generated based on the refined object queries.

Abstract: The techniques for dual-level semantic segmentation are provided. Data may be input to a first segmentation network. The input data comprises an image and label information associated with the image. The image may be captured at nighttime and may comprise a plurality of regions. At least one region among the plurality of regions may be determined based at least in part on output of the first segmentation network. The at least one region of the image may be cropped. The cropped at least one region may be input to a second segmentation network. A final output may be produced based on the output of the first segmentation network and output of the second segmentation network.

Abstract: Single-stage frameworks for open-vocabulary panoptic segmentation are provided.

Abstract: The present disclosure describes techniques for dual-level semantic segmentation. Data may be input to a first segmentation network. The input data comprises an image and label information associated with the image. The image may be captured at nighttime and may comprise a plurality of regions. At least one region among the plurality of regions may be determined based at least in part on output of the first segmentation network. The at least one region of the image may be cropped. The cropped at least one region may be input to a second segmentation network. A final output may be produced based on the output of the first segmentation network and output of the second segmentation network.