In recent years, large models have become one of the most important tools for humans. Prompts play a crucial role as bridges of communication between humans and large models. Although high-quality prompts can fully unleash the potential of large models, their design requires both specialized skills and substantial resources, leading to the emergence of prompt trading markets and licensing service models. However, treating prompts as commodities presents three main challenges: 1) Loss of value of prompts owing to them being easily copied and disseminated once the prompt text is leaked; 2) Lack of objective standards and pricing systems to ensure prompt quality; 3) Difficulty experienced by users in holding providers of false prompts accountable, and rights of providers being affected by the actions of centralized platforms. To address these challenges, this study proposes PromptDEX, a blockchain-based service platform for large model prompts. Through a prompt rental mechanism based on smart contracts and service templates built on LangChain, the platform enables direct interaction between providers and demanders, thereby reducing the impact of centralized institutions on interests and privacy of the participants. All transaction records are transparently stored in the blockchain to ensure security, reliability, and accountability. Additionally, a dynamic pricing mechanism based on demand-side ratings is implemented. The experimental results show that the additional cost incurred by integrating the blockchain is negligible, as calculated in terms of fractions of a cent. Providers can build API services with only approximately 10 lines of code, requiring minimal network bandwidth and demonstrating strong feasibility and practicality.
Object detection is a fundamental task in computer vision, which aims to locate and classify objects within an image. By contrast, multi-attribute recognition involves inputting an image centered around an object and outputting the classification results of multiple attributes of the object within the image. Current detection algorithms face challenges in recognizing small objects, while attribute recognition algorithms fail to locate objects. To address these issues, this paper proposes a multi-attribute small object detection framework, MA-YOLO, that performs small object detection and multi-attribute recognition simultaneously. First, an Attribute Decoupling Module (ADM) decouples attributes of the object and adds multiple parallel attribute classification heads. Second, a simple yet effective attribute-fusion strategy is used to facilitate feature interactions among the attributes. This strategy generates bridging features through a Bridge Feature Generation Module (BFGM) to bridge the gap between the features extracted by the backbone network and all decoupled attribute features, and it utilizes an Attribute Fusion Module (AFM) to obtain fused attribute features. For small object detection, a convolution and self-attention hybrid module, ACmix, is adopted to enhance the feature extraction capability of the network. Additionally, bounding boxes are modeled as two-dimensional Gaussian distributions and measured using the Normalized Wasserstein Distance (NWD). Experimental results show that MA-YOLO achieves mAP@0.5 values of 84.1% and 85.3% on the test and validation sets of the multi-attribute dataset WIDER Attribute and 48.2% as the validation set of the small object dataset VisDrone2019. Extensive ablation and visualization experiments validate the effectiveness of MA-YOLO.
Because of the prolonged examination time and limited therapeutic time window for stroke, the development of a rapid and highly accurate medical image segmentation model for stroke is important for clinical diagnosis. The U-Net architecture based on Mamba, which is known for its low complexity and capability to handle large-scale images, has garnered widespread attention in the field of medical image processing in recent years. The FRactional order Fourier Transform (FRFT) can convert signals to arbitrary fractional domains between the spatial and frequency domains, allowing the observation of features that are not prominent in the spatial or frequency domains. Therefore, by introducing the FRFT, the lesion characteristics can be observed in the fractional domain. Based on the FRFT and Mamba network, a novel model named FRFTMamba-UNet is proposed for stroke medical image segmentation. This model incorporates a fractional domain into the Mamba network and designs a multilevel residual module connected to a U-Net encoder. In addition, a hierarchical feature extraction strategy is implemented in a U-Net-like network, where different feature extraction modules are designed for shallow and deep layers. Specifically, residual convolutions based on Convolutional Neural Networks (CNN) are added to the shallow layers to extract shallow features effectively, whereas the Mamba architecture is utilized in the deep layers to extract deep features further. The proposed method demonstrates superior accuracy and efficiency to existing state-of-the-art models based on the Mamba module across three stroke datasets: AISD, ATLAS, and ISLES 2022. On the AISD dataset, it achieves a Dice Similarity Coefficient (DSC) of 64.27%, 62.24%, whereas on the ISLES 2022 dataset, it achieves a DSC of 85.24%.
Vision-and-Language Navigation (VLN) tasks aim to guide an agent to move to a target location in a 3D or real-world environment based on language instructions. However, the traditional end-to-end VLN algorithms have certain limitations. When an erroneous action occurs during navigation planning, the agent tends to enter incorrect paths, resulting in an inability to continue following the instructions or exploring unnecessary areas. To address this issue, an agent called Nav-Explore is proposed, which is based on a Large Language Model (LLM) and an exploration module. The proposed agent leverages the reasoning capabilities of the LLM to predict the next action according to the language instructions and current visual information and uses an exploration module to balance exploration and utilization. The exploration module employs an ε-greedy strategy for toggling between the normal navigation and exploration modes. When the random probability is below ε, the agent explores possible future paths to assess the feasibility of the next actions, thus avoiding incorrect decisions. If the probability exceeds ε, it directly uses the output of the LLM for navigation. This modular design enables Nav-Explore to effectively enhance navigation success rates and improve the generalization ability of the agent in unseen environments. The experimental results demonstrate that Nav-Explore achieves superior performance on two outdoor VLN benchmark datasets, Touchdown and Map2seq, significantly increasing navigation success rates. Furthermore, Nav-Explore exhibits strong generalization capabilities, effectively completing navigation tasks in different environments.
In the home service scenario, multi-robot systems need to process natural language instructions issued by non-professional users, which poses higher requirements for automated task scheduling. Existing multi-robot scheduling methods present limitations in terms of task understanding, dependency management, and resource optimization. To address these issues, this paper proposes a Directed Acyclic Graph (DAG)-Large Language Model (LLM) scheduling method to achieve full process automation from natural language input to multi-machine collaboration. First, this method first utilizes the LLM combined with environmental information for semantic parsing and task decomposition and generates a set of subtasks with execution constraints through the Chain-of-Abstraction (CoA) mechanism. Second, based on the LLM, a DAG is automatically constructed between subtasks to replace traditional manual modeling processes and accurately represent task dependencies. Finally, a backtracking algorithm is used to match robot skills with subtask requirements, combined with an asynchronous execution strategy to improve execution, and dynamic scheduling is used to reduce waiting time while ensuring dependency order. To verify the effectiveness of this method, three types of household tasks with different complexities (including four sets of scenarios) are designed in an AI2-THOR simulation environment for comparative experiments. Experimental data show that, compared with SMART-LLM and AutoTAMP, the DAG-LLM improves the task success rate by 41.4 and 57.7 percentage points, respectively, and reduces the running time by 24.3% and 6.3%, respectively. The results of an ablation experiment further demonstrate that task dependency modeling and asynchronous execution mechanisms play a crucial role in improving the system performance. The proposed method does not require manual involvement in task decomposition and dependency modeling, and it is suitable for the efficient collaborative scheduling of multi-robot intelligent agents in natural-language-driven application scenarios such as homes.
