Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its sig-nificance, research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this pa-per, we present three novel elements to advance in-vehicle gaze research. Firstly, we introduce IVGaze, a pioneering dataset capturing in-vehicle gaze, collected from 125 sub-jects and covering a large range of gaze and head poses within vehicles. In this dataset, we propose a new vision-based solution for in-vehicle gaze collection, introducing a refined gaze target calibration method to tackle annotation challenges. Second, our research focuses on in-vehicle gaze estimation leveraging the IvGaze. In-vehicle face images often suffer from low resolution, prompting our in-troduction of a gaze pyramid transformer that leverages transformer-based multilevel features integration. Expanding upon this, we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transfor-mation, we rotate virtual cameras to normalize images, uti-lizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR shows state-of-the-art performance on the IVGaze dataset. Thirdly, we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images, we achieve superior performance compared to relying solely on visual features, sub-stantiating the advantage of gaze estimation. The project is available at https://yihua.zone/work/ivgaze.

The purpose of the cross-document event coreference resolution task is to solve multi-text processing. However, cross-document coreference resolution has not been fully explored. None of the existing methods explore the situation where the predicted coreference scores are also low when similar events have a low semantic similarity. To solve such a problem, in this paper, we propose a novel model by focusing on event classes with low event semantic similarity. Specifically, by building the Siamese network framework to enhance the feature representation, we convert the event-to-node into event-to-domain. In addition, by changing the form of the problem from traditional pairwise to listwise, the distance between coreference event nodes is greatly reduced, and the common coreference event resolution is solved. Our model only employs a very small dataset to annotate information, increases the semantic distance of these event pairs in the new vector space, and greatly improves the effect of event clustering. For the cross-document coreference resolution dataset ECB+, our model achieves better results than other models that have not been fine-tuned on more datasets or language models.