Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to navigate with lowlevel actions following natural language instructions in 3D environments. Most existing approaches utilize observation features from the current step to represent the viewpoint. However, these representations often conflate redundant and essential information for navigation, introducing ambiguity into the agent's action prediction. To address the problem of inadequate representation, we propose a Knowledge-andHistory Aware Visual Representation for Continuous Vision-and-Language Navigation (VLN-KHVR). The proposed approach constructs enriched visual representations tailored to navigation instructions, enhancing agents' navigation performance. Specifically, VLN-KHVR extracts image features from the current observation, retrieves relevant knowledge in the knowledge base, and obtains the history of the navigation episode. Subsequently, the knowledge and history features are filtered to eliminate the information irrelevant to navigation instruction. These refined features are integrated with the instruction for further interaction. Finally, the aggregated features are used to guide navigation. Our model outperforms previous methods on the VLN-CE benchmark, demonstrating the effectiveness of the proposed method.

Cloud services have attracted extensive attention due to low cost, agility and mobility. However, when processing data on cloud servers, users may worry about semi-honest third parties stealing private information from them, hence, data encryption is applied for privacy protection. Inpainting is a technique that reconstructs certain undesirable regions in an image through an imperceptible manner, which can be accomplished by searching for well-matching candidate patches and copying them to to-be-inpainted locations. However, when the image is encrypted, the matched candidate patch searching is a challenging dilemma. Therefore, tackling these data-privacy issues for image inpainting over a cloud infrastructure, we propose an image inpainting scheme using Markov random field (MRF) modeling in encrypted domain. In this scheme, the sender encrypts the to-be-inapinted image by using a homomorphic cryptosystem that supports homomorphic ciphertext comparison. Then, the cloud realizes the MRF-based inpainting for encrypted images through some specific homomorphic operations. In addition, secure context descriptors are utilized to improve the inpainting of textures and structures. Finally, the receiver obtains the inpainted result through image decryption. The proposed scheme is proved to be secure through various cryptographic attacks. Qualitative and quantitative results demonstrate our scheme achieves better inpainted results in structure compared with state-of-the-art schemes in encrypted domain.

Vision-and-Language Navigation (VLN) in continuous environments aims to navigate robot agents in unseen environments following natural language instructions. The majority of existing approaches rely on constructing semantic maps or topological maps to record information. However, semantic maps overlook the detailed information of objects and the correspondence among views during navigation, while topological maps lack the spatial representation between entities. To address these limitations, we propose a novel visual feature representation method for continuous VLN, called Multiple Visual Features in Topological Map (MV-Topo). MV-Topo utilizes three distinct visual encoders to extract visual features, which are integrated in the dynamically generated topological map. These fused features actively participate in the subsequent cross-modal planning to derive a long-term path towards a subgoal, effectively guiding the agent to reach the final location. We experimentally demonstrate the effectiveness of our approach and achieve competitive results on the full VLN-CE test splits. Notably, our method outperforms the state-of-the-art by 3.5% in terms of the Navigation Error (NE) metric, indicating that the utilization of multiple visual features significantly enhances the agent’s perception of semantic targets.

In order to achieve the secure, efficient storage and transmission of medical images, we propose a joint lossless compression and encryption (JLCE) scheme. First, according to the intra-block correlation degree, the original medical image is divided into two non-overlapping regions with strong correlation and weak correlation. Then, a linear prediction method is performed on the strong correlation region to generate prediction errors; while the integer discrete Tchebichef transform (iDTT) with the properties of energy compaction and perfect image reconstruction is exploited to compact the energy of the weak correlation region and produce the transformed coefficients. Finally, a secure arithmetic encoding algorithm is presented to encode the prediction errors and transformed coefficients and output the encrypted and compressed bitstream. Without secret keys, the outputted encoded result can be decoded by our scheme, but the decoded result doesn’t disclose the content of the original medical image. Experimental results show that, the proposed scheme has satisfactory format compatibility and security and also achieves better performances of compression ratio and computational efficiency compared with some state-of-the-art schemes.

In recent decades, many perceptual image hashing schemes for content authentication have been proposed. However, existing algorithms cannot provide satisfactory robustness and discrimination in the face of complex manipulations in real scenarios. In this work, we propose a novel perceptual robust image hashing scheme with transformer-based multi-layer constraints. Specifically, we first exploit the Transformer structure into the field of perceptual image hashing, and an integrated loss function is designed to optimize the training of the model. In addition, to solve the issue of the simple content-preserving manipulations used in previous datasets, we construct a more challenging image dataset based on various manipulations, which can deal with complex image authentication scenarios. Experimental results demonstrate that our scheme achieves competitive results compared with existing schemes.