Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to, the source data. To address it, we propose a novel approach called Source HypOthesis Transfer (SHOT), which learns the feature extraction module for the target domain by fitting the target data features to the frozen source classification module (representing classification hypothesis). Specifically, SHOT exploits both information maximization and self-supervised learning for the feature extractor learning to ensure the target features are implicitly aligned with the features of unseen source data. Furthermore, we propose a new labeling transfer strategy, which separates the target data into two splits based on the confidence of predictions (labeling information), and then employ semi-supervised learning to improve the accuracy of less-confident predictions in the target domain. Extensive experiments on various domain adaptation tasks show that our methods achieve results surpassing or comparable to the state-of-the-arts.

Hashing has shown enormous potentials in preserving semantic similarity for large-scale data retrieval. Existing methods widely retain the similarity within two binary codes towards their discrete semantic affinity, i.e., 1 or -1. However, such a discrete reconstruction approach has obvious drawbacks. First, two unrelated dissimilar samples would have similar binary codes when both of them are the most dissimilar with an anchor sample. Second, the fine-grained semantic similarity cannot be shown in the generated binary codes among data with multiple semantic concepts. Furthermore, existing approaches generally adopt a point-wise error-minimizing strategy to enforce the real-valued codes close to its associated discrete codes, resulting in the well-learned paired semantic similarity being unintentionally damaged when performing quantization. To address these issues, we propose a novel deep hashing method with pairwise similarity-preserving quantization constraint, termed Deep Semantic Reconstruction Hashing (DSRH), which defines a high-level semantic affinity within each data pair to learn compact binary codes. Specifically, DSRH is expected to learn the specific binary codes whose similarity can reconstruct their high-level semantic similarity. Besides, we adopt a pairwise similarity-preserving quantization constraint instead of the traditional point-wise quantization technique, which is conducive to maintain the well-learned paired semantic similarity when performing quantization. Extensive experiments are conducted on four representative image retrieval benchmarks, and the proposed DSRH outperforms the state-of-the-art deep-learning methods with respect to different evaluation metrics.

The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the complex and changeable environment in space also poses challenges to the management of multi-satellite systems (MSS). To address the above issues, this paper formulates a mixed integer optimization problem to solve the autonomous task planning for MSS. First, we constructed a multi-agent-based on-board autonomous management and multi-satellite collaboration architecture. Based on this architecture, we propose a hybrid genetic algorithm with simulated annealing (H-GASA) to solve the multi-satellite cooperative autonomous task planning (MSCATP). With the H-GASA, a heuristic task scheduling scheme was developed to deal with possible task conflicts in MSCATP. Finally, a simulation scenario was established to validate our proposed H-GASA, which exhibits a superior performance in terms of computational power and success rate compared to existing algorithms.

Cross-media retrieval (CMR) offers a flexible retrieval experience across multiple modalities. Existing CMR approaches are constrained by the assumption that the paired modalities are available in training, and they leverage the data of all modalities to obtain a common representation. However, as dealing with the data from new modality, the previous all modalities need to be re-trained, compromising the flexibility and practicality of CMR. In this paper, we propose an approach termed learning Modality-Agnostic Representation for Scalable cross-media retrieval (MARS), which allows each modality to be trained independently. To be specific, MARS treats the label information as a distinct modality, and introduces a label parsing module LabNet to generate semantic representation for correlating different modalities. Meanwhile, MARS constructs the modality-specific representation module DataNet to obtain the modality-shared representation and modality-exclusive representation equipped with unbiased semantic classification. Technically, for the first modality, we jointly train the LabNet and its DataNet to preserve the semantic similarity between the Label-derived representation and the modality-shared representation. For new modalities, MARS employs the well-learned LabNet to extract the representation in labels, and then such representation is served as the privilege to guide the associated DataNet training via the same objective. Furthermore, we assign the same classifier to the representation module of all modalities for better semantic alignment. With the above schema, the obtained modality-shared representation is considered to be modality-agnostic. Extensive experiments on several benchmark multi-modality datasets demonstrate that the proposed MARS achieves better results than existing methods.

Hashing has attracted increasing attention due to its tremendous potential for efficient image retrieval and data storage. Compared with conventional hashing methods with a handcrafted feature, emerging deep hashing approaches employ deep neural networks to learn feature representations as well as hash functions, which have already been proved to be more powerful and robust in real-world applications. Currently, most of the existing deep hashing methods construct pairwise or triplet-wise constraint to obtain similar binary codes between similar data pair or relative similar binary codes within a triplet. However, some critical local structures of the data are lack of exploiting, thus the effectiveness of hash learning is not fully shown. To address this limitation, we propose a novel deep hashing method named local semantic-aware deep hashing with Hamming-isometric quantization (LSDH), where local similarity of the data is intentionally integrated into hash learning. Specifically, in the Hamming space, we exploit the potential semantic relation of the data to robustly preserve their local similarity. In addition to reducing the error introduced by binary quantizing, we further develop a Hamming-isometric objective to maximize the consistency of similarity between the pairwise binary-like feature and its binary codes pair, which is shown to be able to enhance the quality of binary codes. Extensive experimental results on several benchmark datasets, including three singlelabel datasets (i.e., CIFAR-10, CIFAR-20, and SUN397) and one multi-label dataset (NUS-WIDE), demonstrate that the proposed LSDH achieves superior performance over the latest state-of-theart hashing methods.