Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval

Three-Stage Semisupervised Cross-Modal Hashing With Pairwise Relations Exploitation

Hashing methods have sparked a great revolution in cross-modal retrieval due to the low cost of storage and computation. Benefiting from the sufficient semantic information of labeled data, supervised hashing methods have shown better performance compared with unsupervised ones. Nevertheless, it is expensive and labor intensive to annotate the training samples, which restricts the feasibility of supervised methods in real applications. To deal with this limitation, a novel semisupervised hashing method, i.e., three-stage semisupervised hashing (TS3H) is proposed in this article, where both labeled and unlabeled data are seamlessly handled. Different from other semisupervised approaches that learn the pseudolabels, hash codes, and hash functions simultaneously, the new approach is decomposed into three stages as the name implies, in which all of the stages are conducted individually to make the optimization cost-effective and precise. Specifically, the classifiers of different modalities are learned via the provided supervised information to predict the labels of unlabeled data at first. Then, hash code learning is achieved with a simple but efficient scheme by unifying the provided and the newly predicted labels. To capture the discriminative information and preserve the semantic similarities, we leverage pairwise relations to supervise both classifier learning and hash code learning. Finally, the modality-specific hash functions are obtained by transforming the training samples to the generated hash codes. The new approach is compared with the state-of-the-art shallow and deep cross-modal hashing (DCMH) methods on several widely used benchmark databases, and the experiment results verify its efficiency and superiority.

Multi-scale Triplet Hashing for Medical Image Retrieval

For medical image retrieval task, deep hashing algorithms are widely applied in large-scale datasets for auxiliary diagnosis due to the retrieval efficiency advantage of hash codes. Most of which focus on features learning, whilst neglecting the discriminate area of medical images and hierarchical similarity for deep features and hash codes. In this paper, we tackle these dilemmas with a new Multi-scale Triplet Hashing (MTH) algorithm, which can leverage multi-scale information, convolutional self-attention and hierarchical similarity to learn effective hash codes simultaneously. The MTH algorithm first designs multi-scale DenseBlock module to learn multi-scale information of medical images. Meanwhile, a convolutional self-attention mechanism is developed to perform information interaction of the channel domain, which can capture the discriminate area of medical images effectively. On top of the two paths, a novel loss function is proposed to not only conserve the category-level information of deep features and the semantic information of hash codes in the learning process, but also capture the hierarchical similarity for deep features and hash codes. Extensive experiments on the Curated X-ray Dataset, Skin Cancer MNIST Dataset and COVID-19 Radiography Dataset illustrate that the MTH algorithm can further enhance the effect of medical retrieval compared to other state-of-the-art medical image retrieval algorithms.

Toward Effective Domain Adaptive Retrieval

This paper studies the problem of unsupervised domain adaptive hashing, which is less-explored but emerging for efficient image retrieval, particularly for cross-domain retrieval. This problem is typically tackled by learning hashing networks with pseudo-labeling and domain alignment techniques. Nevertheless, these approaches usually suffer from overconfident and biased pseudo-labels and inefficient domain alignment without sufficiently exploring semantics, thus failing to achieve satisfactory retrieval performance. To tackle this issue, we present PEACE, a principled framework which holistically explores semantic information in both source and target data and extensively incorporates it for effective domain alignment. For comprehensive semantic learning, PEACE leverages label embeddings to guide the optimization of hash codes for source data. More importantly, to mitigate the effects of noisy pseudo-labels, we propose a novel method to holistically measure the uncertainty of pseudo-labels for unlabeled target data and progressively minimize them through alternative optimization under the guidance of the domain discrepancy. Additionally, PEACE effectively removes domain discrepancy in the Hamming space from two views. In particular, it not only introduces composite adversarial learning to implicitly explore semantic information embedded in hash codes, but also aligns cluster semantic centroids across domains to explicitly exploit label information. Experimental results on several popular domain adaptive retrieval benchmarks demonstrate the superiority of our proposed PEACE compared with various state-of-the-art methods on both single-domain and cross-domain retrieval tasks. Our source codes are available at https://github.com/WillDreamer/PEACE.