Yuying Zhu

Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang et al.

Artificial intelligence has advanced quantitative remote sensing, yet its effectiveness is constrained by imbalanced label distribution. This imbalance leads conventionally trained models to favor common samples, which in turn degrades retrieval performance for rare ones. Rainfall retrieval exemplifies this issue, with performance particularly compromised for heavy rain. This study proposes Hurdle-Inversion Model Debiasing Learning (IMDL) framework. Following a divide-and-conquer strategy, imbalance in the rain distribution is decomposed into two components: zero inflation, defined by the predominance of non-rain samples; and long tail, defined by the disproportionate abundance of light-rain samples relative to heavy-rain samples. A hurdle model is adopted to handle the zero inflation, while IMDL is proposed to address the long tail by transforming the learning object into an unbiased ideal inverse model. Comprehensive evaluation via statistical metrics and case studies investigating rainy weather in eastern China confirms Hurdle-IMDL's superiority over conventional, cost-sensitive, generative, and multi-task learning methods. Its key advancements include effective mitigation of systematic underestimation and a marked improvement in the retrieval of heavy-to-extreme rain. IMDL offers a generalizable approach for addressing imbalance in distributions of environmental variables, enabling enhanced retrieval of rare yet high-impact events.

Jianing Yang, Yongxin Wang, Ruitao Yi et al.

Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed. Data in this domain exhibits complex multi-relational and temporal interactions. Learning from this data is a fundamentally challenging research problem. In this paper, we propose Modal-Temporal Attention Graph (MTAG). MTAG is an interpretable graph-based neural model that provides a suitable framework for analyzing multimodal sequential data. We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions across modalities and through time. Then, a novel graph fusion operation, called MTAG fusion, along with a dynamic pruning and read-out technique, is designed to efficiently process this modal-temporal graph and capture various interactions. By learning to focus only on the important interactions within the graph, MTAG achieves state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks, while utilizing significantly fewer model parameters.

Jianing Yang, Yuying Zhu, Yongxin Wang et al.

Question answering biases in video QA datasets can mislead multimodal model to overfit to QA artifacts and jeopardize the model's ability to generalize. Understanding how strong these QA biases are and where they come from helps the community measure progress more accurately and provide researchers insights to debug their models. In this paper, we analyze QA biases in popular video question answering datasets and discover pretrained language models can answer 37-48% questions correctly without using any multimodal context information, far exceeding the 20% random guess baseline for 5-choose-1 multiple-choice questions. Our ablation study shows biases can come from annotators and type of questions. Specifically, annotators that have been seen during training are better predicted by the model and reasoning, abstract questions incur more biases than factual, direct questions. We also show empirically that using annotator-non-overlapping train-test splits can reduce QA biases for video QA datasets.

Yuying Zhu, Guoxin Wang, Börje F. Karlsson

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.