Systems and methods described herein which can involve for a first input of a plurality of labeled images of a new domain task, processing the first plurality of labeled images through a plurality of backbone snapshots, each of the backbone snapshots representative of a model trained across a plurality of other domain tasks, each of the plurality of backbone snapshots configured to output a first plurality of features responsive to the input; processing a second input of second plurality of unlabeled images through the plurality of backbone snapshots to output a second plurality of features responsive to the second input; and generating a representative model for the new domain task from the clustering and transformation of the first plurality of features and as associated from the clustered and transformed second plurality of features.

An object region segmentation device and an object region segmentation method thereof are provided. The object region segmentation device includes a processor and storage. The storage stores a deep-learning network model for segmenting an object region in an image. The deep-learning network model includes a first network model for generating a pseudo label, a second network model for generating a confidence map for the pseudo label, and a third network model for segmenting the object region in the image. The processor inputs an unlabeled image to the first network model to generate the pseudo label, inputs the pseudo label to the second network model to generate the confidence map, and trains the third network model using a pseudo label corresponding to at least one pixel, a confidence level of which is greater than or equal to a threshold, on the confidence map.

Apparatuses, systems, and techniques to train one or more neural networks using unannotated images. In at least one embodiment, the one or more neural networks are trained based, at least in part, on one or more loss functions calculated using a randomly selected portion pair from two different images and a randomly selected portion pair from the same image.

The present disclosure relates to a device for object-centric representation learning through unsupervised semantic segmentation, and includes a video encoding module that receives an input video and generate a feature map, an eigen clustering module that calculates an eigenvector representing a semantic structure of patches in the input video based on color affinity and semantic similarity of the input video, and generates a patch cluster for the patches in the input video through the eigenvector, and an object-centric contrastive learning module that generates an object prototype based on the patch cluster and distinguishes objects in the input video through semantic coherence based on the contrastive learning for the object prototype.

A method and apparatus for adapting a pretrained machine learning model using active learning for improved task performance in a target domain includes embedding a vector representation of at least one unlabeled target class data in an embedding space associated with the pretrained machine learning model, analyzing the embedding space to select, for labeling, at least one unlabeled class data representation based on a distance measurement in the embedding space that identifies an unlabeled class data representation that, if labeled, improves a coverage for at least one unlabeled target class data representation in the embedding space, labeling the selected at least one unlabeled class data representation, and adapting the pretrained machine learning model for improved task performance in a domain of the at least one unlabeled target class data for which coverage was improved by retraining the pretrained machine learning model using the labeled unlabeled class data representation.

A facial image de-identification method and system are provided. A facial image de-identification method according to some embodiments may include acquiring a facial image, detecting one or more facial feature from the facial image, determining at least some of the detected facial features as a de-identification region of the facial image, and applying an image transformation technique to the determined de-identification region to generate a de-identification image. According to the method, a de-identification image can be created that preserves anatomical structure information such as a facial skeleton as it is while reducing the possibility of individual identification (that is, risk of re-identification).

A recycling-material sorting system includes an image capture system, an RFID reader system, and a vision detection system. A controller receives item identifier information from an RFID IC associated with a recyclable item through the RFID reader system. If the controller is able to derive recycling information for the recyclable item, it is used to sort the recyclable item. The item identifier information is further used to train a vision detection system regardless of whether the recycling information can be derived for the recyclable item or not.