1.2k citations · 1.5k across the 38 of their papers we have counts for
38 papers
HOI-aware Adaptive Network for Weakly-supervised Action Segmentation
Runzhong Zhang, Suchen Wang, Yueqi Duan +3
In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of…
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
Donglin Di, Weinan Zhang, Yue Zhang +1
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the m…
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Yanjie Song, Yutong Wu, Yangyang Guo +7
Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance…
DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation
Yue Zhang, Hehe Fan, Yi Yang +1
In this technical report, we present our findings from the research conducted on the Human-Object Interaction 4D (HOI4D) dataset for egocentric action segmentation task. As a relat…
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
Fu Lin, Haonan Gong, Mingkang Li +3
Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem.…
Trajectory Data Collection with Local Differential Privacy
Yuemin Zhang, Qingqing Ye, Rui Chen +2
Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimate…
ConTrack: Contextual Transformer for Device Tracking in X-ray
Marc Demoustier, Yue Zhang, Venkatesh Narasimha Murthy +2
Device tracking is an important prerequisite for guidance during endovascular procedures. Especially during cardiac interventions, detection and tracking of guiding the catheter ti…
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Yulong Chen, Huajian Zhang, Yijie Zhou +8
Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can conta…
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
Ning Lin, Shaocong Wang, Yi Li +19
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains…
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu +3
Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of compl…
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Hongliang He, Junlei Zhang, Zhenzhong Lan +1
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous stud…
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Guangsheng Bao, Zebin Ou, Yue Zhang
Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abs…
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Yue Zhang, Suchen Wang, Shichao Kan +3
Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian in a surveillance system. Existing methods address the PAR problem by training a multi-…
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
Yun Luo, Zihan Liu, Stan Z. Li +1
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{agree, disagree, neutral}) towards a specific text. It is limited for existing methods merely…
Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis
Chenyang Lyu, Linyi Yang, Yue Zhang +2
User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and pro…
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
Jianhao Yan, Jin Xu, Fandong Meng +2
Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising…
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Linyi Yang, Shuibai Zhang, Libo Qin +6
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-t…
Pre-Training a Graph Recurrent Network for Language Representation
Yile Wang, Linyi Yang, Zhiyang Teng +2
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing. Recent work shows that t…
Cross-domain Generalization for AMR Parsing
Xuefeng Bai, Seng Yang, Leyang Cui +2
Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most exi…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
Multi-Granularity Optimization for Non-Autoregressive Translation
Yafu Li, Leyang Cui, Yongjing Yin +1
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption. This assumption is further stre…
Categorizing Semantic Representations for Neural Machine Translation
Yongjing Yin, Yafu Li, Fandong Meng +2
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in composit…
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Jiangbin Zheng, Yile Wang, Ge Wang +5
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an…
Semantic-based Pre-training for Dialogue Understanding
Xuefeng Bai, Linfeng Song, Yue Zhang
Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understa…