Images about active learning for continuous improvement, adversarial robustness in feature learning, attention mechanisms for salient features, contextual similarity and scene understanding, cross-domain retrieval and domain adaptation, deep convolutional neural networks cnns for feature extraction, explainable ai for transparency in retrieval decisions, fine-tuned domain-specific feature representations, generative adversarial networks gans for synthetic data, graph-based and transformer architectures, hashing and binary embedding for efficient retrieval, hierarchical and multi-scale feature representations, incremental and online learning, multi-modal approaches combining visual and textual data, on-device and edge-based retrieval, self-supervised and unsupervised learning methods, semantic segmentation and object-level representations, transfer learning from pre-trained models, triplet loss and metric learning, and user-driven and personalized retrieval.
