Motion cues play a vital role in multi-frame infrared small target detection (MISTD). However, most targets in existing datasets exhibit regular and slow motion, which cannot reflect the complex and diverse motion patterns in real-world scenarios. This biased data distribution makes recent data-driven methods highly rely on simplified motion assumptions that tend to fail in irregular or fast motion, resulting in noisy feature representations cluttered with target-irrelevant factors. Hence, we stress that methods for MISTD should also work when targets are in complex motion. To enable this research, we propose a large-scale dataset called MIST for airborne infrared detection scenarios. The dataset is built on a synthetic data engine that models variations in pose, size, and intensity of moving targets while seamlessly blending them into real backgrounds for physical, geometric, and visual realism. Targets in MIST exhibit low signal-to-clutter ratios and complex motion, making it a promising yet challenging benchmark for developing algorithms focused on motion analysis. To tackle the challenges of MIST, we develop MISTNet, a robust baseline based on the Information Bottleneck theory. To handle irregular and fast motion, we propose a shifted neighborhood compensation block to efficiently model multi-scale correspondences for implicit motion compensation. To distill compact representations free from irrelevant cues, we design a progressive distillation decoder to hierarchically filter out redundancy while preserving target-relevant information. We benchmark 31 state-of-the-art methods and find that their performance on MIST drops significantly compared with that on the widely used NUDT-MIRSDT dataset. Our MISTNet outperforms all other methods by a large margin, with an over 6% gain in the IoU metric, demonstrating its superiority. The dataset, code, and model weights are available at https://github.com/GR-ray/MIST.

The use of dry electrode electroencephalography (EEG) systems holds significant importance in advancing the everyday application of emotion recognition. However, adapting it to real-world applications faces unique challenges due to low signal-to-noise ratios and unreliable emotion labels. To address these challenges, we propose a Cross-Device Representation Consistency (CDRC) pre-training paradigm for dry EEG emotion recognition, where the self-supervised signal is provided by the distance between representations embedded in wet and dry EEG components and trained via contrastive estimation. Specifically, we employ a dual-branch embedding prediction task coupled with contrastive feature alignment module to extract robust and distinctive features from dry electrode EEG signals. We evaluate our model on an available emotional dataset PaDWEED, extensive experiments demonstrate that CDRC performs comparably to fully supervised training and achieves state-of-the-art results compared to several self-supervised approaches. Moreover, the remarkable performance on subject-independent tasks highlights its effectiveness in addressing and mitigating subject variability.

Reliable and efficient overtaking maneuvers are important and challenging for autonomous vehicles. Position information of both involved vehicles during overtaking is important, to avoid collisions. In this paper, we try to perform overtaking based on relative position information, such as the distance, angle and velocity between vehicles, in a non-collaborative scenario. To reduce the complexity of maneuvers, a fuzzy inference system (FIS) is applied to analyze the driving behavior of the preceding vehicle based on the relative position information. An output of “safe” or “dangerous” will be sent to the decision part based on reinforcement learning frameworks. Various overtaking maneuvers including “conservative” and “aggressive” can be obtained accordingly. Numeric results validate our analysis, and show that our proposed strategies can be easily extended to the multiple-vehicle-scenario.