Unmanned surface vehicles (USVs) have been widely used in research and exploration, patrol, and defense. Autonomous navigation and obstacle avoidance, as the essential technology of USVs, are the key conditions for successful mission execution. However, fine modeling of conventional algorithms cannot meet the real-time precise behavior control strategy of USVs in complex environments, which poses a great challenge to autonomous control policy. In this paper, a deep reinforcement learning-based UANOA (USVs autonomous navigation and obstacle avoidance) method is proposed. The UANOA achieves the autonomous navigation task of USVs by real-time sensing of partially complex ocean information around and real-time output of rudder angle control commands of USVs. In our work, we employ a double Q-network to achieve end-to-end control from raw sensor input to output of discrete rudder action, and design a set of reward functions that can be adapted to USV navigation and obstacle avoidance. To alleviate the decision bias caused by partial observable of USVs, we use the long short-term memory (LSTM) networks to enhance the ability to remember the ocean environment of USVs. Experiments demonstrate that UANOA ensures a USV arrives at the target points with optimal path planning in complex ocean environments without any collisions occurring, and UANOA outperforms deep Q-network (DQN) and random control policy in convergence speed, sailing distance, rudder angle steering consumption, and other performance measurements.

The accuracy of autonomous navigation and obstacle avoidance of unmanned aerial vehicles (UAVs) in complex environments has become one challenging task. In this paper, an autonomous navigation and obstacle avoidance of the UAV (ANOAU) algorithm based on deep reinforcement learning (DRL) has been proposed to achieve accurate path planning in complex environments. In our work, we use an actor–critic‐based DRL framework to achieve autonomous UAV control from sensor input to the output of the UAV’s action and design a set of reward functions that can be adapted to autonomous navigation and obstacle avoidance for the UAV in the complex environment. Meanwhile, to alleviate the decision‐making bias caused by the incomplete observables of the UAV, we use a gate recurrent unit network to enhance the ability to perceive the uncertain environment, enhance the perception representation and improve the accuracy of UAV real‐time decision‐making. Experimental simulation results verify that the ANOAU algorithm achieves good UAV flight attitude adaptive adjustment in navigation and obstacle avoidance tasks and significantly improves the generalization ability and training efficiency of the UAV navigation controller in a complex environment.

Sarcasm in social media, frequently conveyed through the interplay of text and images, presents significant challenges for sentiment analysis and intention mining. Existing multi-modal sarcasm detection approaches have been shown to excessively depend on superficial cues within the textual modality, exhibiting limited capability to accurately discern sarcasm through subtle text–image interactions. To address this limitation, a novel framework, InterCLIP-MEP, is proposed. This framework integrates Interactive CLIP (InterCLIP), which employs an efficient training strategy to derive enriched cross-modal representations by embedding inter-modal information directly into each encoder, while using approximately 20.6 \(\times\) fewer trainable parameters compared with existing state-of-the-art (SOTA) methods. Furthermore, a Memory-Enhanced Predictor (MEP) is introduced, featuring a dynamic dual-channel memory mechanism that captures and retains valuable knowledge from test samples during inference, serving as a nonparametric classifier to enhance sarcasm detection robustness. Extensive experiments on MMSD, MMSD2.0, and DocMSU show that InterCLIP-MEP achieves SOTA performance, specifically improving accuracy by 1.08% and F1-score by 1.51% on MMSD2.0. Under distributional shift evaluation, it attains 73.96% accuracy, exceeding its memory-free variant by nearly 10% and the previous SOTA by over 15%, demonstrating superior stability and adaptability. The implementation of InterCLIP-MEP is publicly available at https://github.com/CoderChen01/InterCLIP-MEP .

With the advent of large vision-language models (LVLMs) demonstrating increasingly human-like abilities, a pivotal question emerges: do different LVLMs interpret multimodal sarcasm differently, and can a single model grasp sarcasm from multiple perspectives like humans? To explore this, we introduce an analytical framework using systematically designed prompts on existing multimodal sarcasm datasets. Evaluating 12 state-of-the-art LVLMs over 2409 samples, we examine interpretive variations within and across models, focusing on confidence levels, alignment with dataset labels, and recognition of ambiguous “neutral” cases. We further validate our findings on a diverse 100-sample mini-benchmark, incorporating multiple datasets, expanded prompt variants, and representative commercial LVLMs. Our findings reveal notable discrepancies—across LVLMs and within the same model under varied prompts. While classification-oriented prompts yield higher internal consistency, models diverge markedly when tasked with interpretive reasoning. These results challenge binary labeling paradigms by highlighting sarcasm’s subjectivity. We advocate moving beyond rigid annotation schemes toward multiperspective, uncertainty-aware modeling, offering deeper insights into multimodal sarcasm comprehension.