Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Abstract Knowledge distillation is a simple yet effective technique for deep model
compression, which aims to transfer the knowledge learned by a large teacher model to a …

This paper investigates the capability of plain Vision Transformers (ViTs) for semantic
segmentation using the encoder–decoder framework and introduce SegViTv2. In this study …

Image and sentence matching is a critical task to bridge the visual and textual discrepancy
due to the heterogeneous modalities. Great progress has been made by exploring the …

Deep hashing has attracted broad interest in cross-modal retrieval because of its low cost
and efficient retrieval benefits. To capture the semantic information of raw samples and …

Recent studies have recently exploited knowledge distillation (KD) technique to address
time-consuming annotation task in semantic segmentation, through which one teacher …

Cross-modal image-text retrieval (CMITR) has been a high-value research topic for more
than a decade. In most of the previous studies, the data for all tasks are trained as a single …

Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous
multi-modal features at both the model training and online retrieval stages to support large …

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent
representations of heterogeneous modalities, thereby to search similar instances in one …

To enable machines to mimic human cognitive abilities and alleviate the catastrophic
forgetting problem in cross-modal image-text retrieval (CMITR), this paper proposes a novel …