Lightweight transformer for high resolution images

    公开(公告)号:US11983239B2

    公开(公告)日:2024-05-14

    申请号:US17342483

    申请日:2021-06-08

    Applicant: Lemon Inc.

    CPC classification number: G06F18/213 G06F18/24 G06N3/04 G06N3/08 G06V10/82

    Abstract: Systems and methods for obtaining attention features are described. Some examples may include: receiving, at a projector of a transformer, a plurality of tokens associated with image features of a first dimensional space; generating, at the projector of the transformer, projected features by concatenating the plurality of tokens with a positional map, the projected features having a second dimensional space that is less than the first dimensional space; receiving, at an encoder of the transformer, the projected features and generating encoded representations of the projected features using self-attention; decoding, at a decoder of the transformer, the encoded representations and obtaining a decoded output; and projecting the decoded output to the first dimensional space and adding the image features of the first dimensional space to obtain attention features associated with the image features.

    DISENTANGLED FEATURE TRANSFORMS FOR VIDEO OBJECT SEGMENTATION

    公开(公告)号:US20220284590A1

    公开(公告)日:2022-09-08

    申请号:US17192599

    申请日:2021-03-04

    Applicant: Lemon Inc.

    Abstract: Systems and method directed to performing video object segmentation are provided. In examples, video data representing a sequence of image frames and video data representing an object mask may be received at a video object segmentation server. Image features may be generated based on a first image frame of the sequence of image frames, image features may be generated based on a second image frame of the sequence of image frames; and object features may be generated based on the object mask. A transform matrix may be computed based on the image features of the first image frame and image features of the second image frame; the transform matrix may be applied to the object features resulting in transformed object features. A predicted object mask associated with the second image frame may be obtained by decoding the transformed object features.

    Neural architecture search system using training based on a weight-related metric

    公开(公告)号:US11836595B1

    公开(公告)日:2023-12-05

    申请号:US17816167

    申请日:2022-07-29

    Applicant: Lemon Inc.

    CPC classification number: G06N3/04 G06N3/08

    Abstract: Systems and methods for performing neural architecture search are provided. In one aspect, the system includes a processor configured to select a plurality of candidate neural networks within a search space, evaluate a performance of each of the plurality of candidate neural networks by: training each candidate neural network on a training dataset to perform the predetermined task and determining a ranking metric for each candidate neural network based on an objective function. The ranking metric includes a weight-related metric that is determined based on weights of a prediction layer of each respective candidate neural network before and after the respective candidate neural network is trained. The processor is configured to rank the plurality of candidate neural networks based on the determined ranking metrics.

    AUTOMATICALLY AND EFFICIENTLY GENERATING SEARCH SPACES FOR NEURAL NETWORK

    公开(公告)号:US20220398450A1

    公开(公告)日:2022-12-15

    申请号:US17348246

    申请日:2021-06-15

    Applicant: Lemon Inc.

    Abstract: A super-network comprising a plurality of layers may be generated. Each layer may comprise cells with different structures. A predetermined number of cells from each layer may be selected. A plurality of cells may be generated based on selected cells using a local mutation model, wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell. Performance of the plurality of cells may be evaluated using a differentiable fitness scoring function. The operations of the generating a plurality of cells using the local mutation model, the evaluating performance of the plurality of cells using the differentiable fitness scoring function and the selecting the subset of cells based on the evaluation results may be iteratively performed until the super-network converges. A search space for each layer may be generated based on a predetermined top number of cells with largest fitness scores after the super-network converges.

    TEMPORAL FEATURE ALIGNMENT NETWORK FOR VIDEO INPAINTING

    公开(公告)号:US20220284552A1

    公开(公告)日:2022-09-08

    申请号:US17192549

    申请日:2021-03-04

    Applicant: Lemon Inc.

    Abstract: Systems and methods are directed to inpainting video. More specifically, initial video data including a sequence of image frames containing missing or corrupted pixel information may be received. Optical flow displacement values and optical flow validity masks may be generated for neighboring image frames of initial video data. Image features from image feature maps of one or more neighboring image frames may be warp-shifted to image feature maps of a current image frame using the optical flow displacement values and warp-shifted image features from the feature maps of the one or more neighboring image frames may be selected based on one or more of the optical flow validity masks. A sequence of complete image frames may be generated based on the selected warp-shifted image features from the feature maps of the one or more neighboring image frames and image features from the image feature maps of the current image frame.

    Video matting
    6.
    发明授权

    公开(公告)号:US12205299B2

    公开(公告)日:2025-01-21

    申请号:US17396055

    申请日:2021-08-06

    Applicant: Lemon Inc.

    Abstract: The present disclosure describes techniques of improving video matting. The techniques comprise extracting features from each frame of a video by an encoder of a model, wherein the video comprises a plurality of frames; incorporating, by a decoder of the model, into any particular frame temporal information extracted from one or more frames previous to the particular frame, wherein the particular frame and the one or more previous frames are among the plurality of frames of the video, and the decoder is a recurrent decoder; and generating a representation of a foreground object included in the particular frame by the model, wherein the model is trained using segmentation dataset and matting dataset.

    MULTI-RESOLUTION NEURAL NETWORK ARCHITECTURE SEARCH SPACE FOR DENSE PREDICTION TASKS

    公开(公告)号:US20220391636A1

    公开(公告)日:2022-12-08

    申请号:US17342486

    申请日:2021-06-08

    Applicant: Lemon Inc.

    Abstract: Systems and methods for searching a search space are disclosed. Some examples may include using a first parallel module including a first plurality of stacked searching blocks and a second plurality of stacked searching blocks to output first feature maps of a first resolution and to output second feature maps of a second resolution. In some examples, a fusion module may include a plurality of searching blocks, where the fusion module is configured to generate multiscale feature maps by fusing one or more feature maps of the first resolution received from the first parallel module with one or more feature maps of the second resolution received from the first parallel module, and wherein the fusion module is configured to output the multiscale feature maps and output third feature maps of a third resolution.

    LIGHTWEIGHT TRANSFORMER FOR HIGH RESOLUTION IMAGES

    公开(公告)号:US20220391635A1

    公开(公告)日:2022-12-08

    申请号:US17342483

    申请日:2021-06-08

    Applicant: Lemon Inc.

    Abstract: Systems and methods for obtaining attention features are described. Some examples may include: receiving, at a projector of a transformer, a plurality of tokens associated with image features of a first dimensional space; generating, at the projector of the transformer, projected features by concatenating the plurality of tokens with a positional map, the projected features having a second dimensional space that is less than the first dimensional space; receiving, at an encoder of the transformer, the projected features and generating encoded representations of the projected features using self-attention; decoding, at a decoder of the transformer, the encoded representations and obtaining a decoded output; and projecting the decoded output to the first dimensional space and adding the image features of the first dimensional space to obtain attention features associated with the image features.

    Techniques for using dynamic proposals in object detection

    公开(公告)号:US12165379B2

    公开(公告)日:2024-12-10

    申请号:US17581423

    申请日:2022-01-21

    Applicant: Lemon Inc.

    Abstract: Described are examples for detecting objects in an image on a device including setting, based on a condition, a number of sparse proposals to use in performing object detection in the image, performing object detection in the image based on providing the sparse proposals as input to an object detection process to infer object location and classification of one or more objects in the image, and indicating, to an application and based on an output of the object detection process, the object location and classification of the one or more objects.

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