公开(公告)号：US20250045929A1

公开(公告)日：2025-02-06

申请号：US18365060

申请日：2023-08-03

Applicant: Lemon Inc.

Inventor： Qihang Yu ,  Ju He ,  Xueqing Deng ,  Xiaohui Shen ,  Liang-Chieh Chen

IPC: G06T7/12 ,  G06T3/40 ,  G06V10/44 ,  G06V10/764 ,  G06V10/771

Abstract: Single-stage frameworks for open-vocabulary panoptic segmentation are provided. One aspect provides a computing system comprising a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive an image; extract a plurality of feature maps from the image using a convolutional neural network-based vision-language model; generate a plurality of pixel features from the plurality of feature maps; generate a plurality of mask predictions from the plurality of pixel features; generate a plurality of in-vocabulary class predictions corresponding to the plurality of mask predictions using the plurality of pixel features; generate a plurality of out-of-vocabulary class predictions using the plurality of feature maps; perform geometric ensembling on the plurality of in-vocabulary class predictions and the plurality of out-of-vocabulary class predictions to generate a plurality of final class predictions; and output the plurality of mask predictions and the plurality of final class predictions.

公开(公告)号：US12254631B2

公开(公告)日：2025-03-18

申请号：US17585301

申请日：2022-01-26

Applicant: Lemon Inc.

Inventor： Peng Wang ,  Xueqing Deng ,  Xiaochen Lian

IPC: G06T7/11 ,  G06T3/40

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.

公开(公告)号：US20250113087A1

公开(公告)日：2025-04-03

申请号：US18395356

申请日：2023-12-22

Applicant: Lemon Inc.

Inventor： Ju He ,  Qihang Yu ,  Inkyu Shin ,  Xueqing Deng ,  Xiaohui Shen ,  Liang-Chieh Chen

IPC: H04N21/845 ,  H04N21/44

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.

公开(公告)号：US20230237662A1

公开(公告)日：2023-07-27

申请号：US17585301

申请日：2022-01-26

Applicant: Lemon Inc.

Inventor： Peng Wang ,  Xueqing Deng ,  Xiaochen Lian

IPC: G06T7/11 ,  G06T3/40

CPC classification number: G06T7/11 ,  G06T3/40 ,  G06T2207/20132 ,  G06T2207/20084

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.