Document-level Relation Extraction with Cross-sentence Reasoning Graph

Relation extraction (RE) has recently moved from the sentence-level to document-level, which requires aggregating document information and using entities and mentions for reasoning. Existing works put entity nodes and mention nodes with similar representations in a document-level graph, whose complex edges may incur redundant information. Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). Specifically, a simplified document-level graph is constructed to model the semantic information of all mentions and sentences in a document, and an entity-level graph is designed to explore relations of long-distance cross-sentence entity pairs. Experimental results show that GRACR achieves excellent performance on two public datasets of document-level RE. It is especially effective in extracting potential relations of cross-sentence entity pairs. Our code is available at https://github.com/UESTC-LHF/GRACR.

Behavior Recognition Based on the Integration of Multigranular Motion Features

The recognition of behaviors in videos usually requires a combinatorial analysis of the spatial information about objects and their dynamic action information in the temporal dimension. Specifically, behavior recognition may even rely more on the modeling of temporal information containing short-range and long-range motions; this contrasts with computer vision tasks involving images that focus on the understanding of spatial information. However, current solutions fail to jointly and comprehensively analyze short-range motion between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behavior recognition method based on the integration of multigranular (IMG) motion features. In particular, we achieve reliable motion information modeling through the synergy of a channel attention-based short-term motion feature enhancement module (CMEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.