Text-guided style transfer aims to repaint a content image with the target style described by a text prompt, offering greater flexibility and creativity compared to traditional image-guided style transfer. Despite the potential, existing text-guided style transfer methods often suffer from many issues, including insufficient visual quality, poor generalization ability, or a reliance on large amounts of paired training data. To address these limitations, we leverage the inherent strengths of transformers in handling multimodal data and propose a novel transformer-based framework called TRTST that not only achieves unpaired arbitrary text-guided style transfer but also significantly improves the visual quality. Specifically, TRTST explores combining a text transformer encoder with an image transformer encoder to project the input text prompt and content image into a joint embedding space and extract the desired style and content features. These features are then input into a multimodal co-attention module to stylize the image sequence based on the text sequence. We also propose a new adaptive parametric positional encoding (APPE) scheme which can adaptively produce different positional encodings to optimally match different inputs with a position encoder. In addition, to further improve content preservation, we introduce a text-guided identity loss to our model. Extensive results and comparisons are conducted to demonstrate the effectiveness and superiority of our method.

UAV Ad-hoc Network (UANET) has been widely used in many fields. However, the collaborative communication and data sharing among multiple UAVs in UANET are often attacked and threatened, due to the limited software and hardware capability of UAVs and the openness of wireless network environment. In this work, we introduce blockchain technology into UANET to enhance its security. Instead of directly adopting traditional blockchain which require huge storage, computation and communication resources, we propose a lightweight reputation-based blockchain scheme for resource-constrained UANET, named AirBC. Firstly, we present a lightweight storage strategy by elimination and compression to reduce storage overhead for UAV nodes. Secondly, we propose an improved reputation-enhanced Practical Byzantine Fault Tolerance (PBFT) consensus, as well as a reputation evaluation scheme based on reliable recording of UAV behaviors. In our scheme, UAVs with high reputation are selected into a miner committee to perform the consensus, thus improving efficiency. Meanwhile, the committee is updated at regular intervals to ensure scalability of UANET. Thirdly, we adopt a weighted proposal voting scheme to enhance the ability of group decision-making for UANET. Finally, to evaluate our approach, simulations are conducted and their results demonstrate that AirBC can reduce 63% storage overhead and 69% consensus latency on average for different scale UANET.