원래 이름 기록: Tata Consultancy Services Limited

Conventional robotic systems often fail to provide effective escorting services as they lack awareness of human motion dynamics. Present disclosure provides method and system for providing robot-based escorting service. The system tracks a user utilizing the robot-based escort service using a human re-identification technique and a human movement tracking technique. The human re-identification technique ensures that the same user is identified every time in crowded spaces and human movement tracking technique predicts a user state at intervals indicating whether user is following, lagging, or stopping based on re-identification performed by the human re-identification technique. Thereafter, the system adjust a speed of the robot in case it is determined that the user is either lagging or stopping, thereby enabling the robot to adapt its speed according to user's movements which further helps in providing seamless experience to user. The system also provides opportunities for interaction to resume escorting service if disrupted.

This disclosure relates generally to a system and method for style-based clustering of artworks with natural language style annotations. The conventional methods generate generic image feature representations derived from deep neural networks and do not specifically deal with the artistic style. The present disclosure, generates style-based artwork representations based on caption with style-based keywords and style concept annotations by leveraging image captioning model, vision language model and text encoder. Further style-based latent feature representations are generated from the style-based artwork representations for performing unsupervised clustering. The clustering of style-based latent feature representations is done based on deep embedded clustering using dynamic or static initialization of clusters. The present disclosure helps in discovering finer-grained style concepts within a corpus of artwork in an unsupervised manner. It also helps explore and create the art style evolution-based narratives and curative practices.

The disclosure relates generally to methods and systems for style-based clustering of artworks with preference feedback. Conventional techniques for artwork clustering rely on generic image representations derived from deep neural networks, thus heavily focused on content-level similarity rather than style-based similarity. According to the present disclosure, the plurality of artworks is passed through the artwork feature extractor to obtain the artwork features which are then passed to the autoencoder which encodes these features into lower dimension feature space. The clustering network layer employs the K-Means clustering algorithm to obtain the initial set of clusters. Then a preference feedback mechanism is employed with four operations: sample, expand, merge, and project, to obtain the style-based clusters. The sample operation facilitates the selection of samples for feedback. The preference feedback on the selected subset of the dataset is captured through the expand and merge operations which are projected onto the entire dataset.

The embodiments of present disclosure address unresolved problems of label inconsistency, where outputs of different levels create impossible combinations, and error propagation from previous level outputs can significantly impact its performance. Embodiments provide a method and system for a Progressive Multi-level Training framework with a Logit-masking strategy (PMTL) for a retail taxonomy classification. PMTL enables neural network models to be trained separately for each level to reduce error propagation problems. To further enhance the model's performance at each level and get the label-wise constraint from the previous level, the global representation from model of previous level is augmented. Further, a logit masking strategy is used to restrict model(s) to learning only relevant classes through part of final classification layer, thereby addressing label inconsistency issue, and incorporating benefit of parent node-based local classifier. This framework is generalized irrespective of dataset size and is configured for attaching to any hierarchical classification network.