82 citations · 250 across the 20 of their papers we have counts for
20 papers
SoccerNet 2023 Challenges Results
Anthony Cioppa, Silvio Giancola, Vladimir Somers +99
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vi…
E2E-LOAD: End-to-End Long-form Online Action Detection
Shuqiang Cao, Weixin Luo, Bairui Wang +2
Recently, there has been a growing trend toward feature-based approaches for Online Action Detection (OAD). However, these approaches have limitations due to their fixed backbone d…
Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
Feng Yan, Weixin Luo, Yujie Zhong +2
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One potential reason is its label assignment strategy d…
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
Feng Yan, Zhiheng Li, Weixin Luo +4
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage ta…
Learning Point-Language Hierarchical Alignment for 3D Visual Grounding
Jiaming Chen, Weixin Luo, Ran Song +3
This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner. We extract key points an…
A Circular Window-based Cascade Transformer for Online Action Detection
Shuqiang Cao, Weixin Luo, Bairui Wang +2
Online action detection aims at the accurate action prediction of the current frame based on long historical observations. Meanwhile, it demands real-time inference on online strea…
Strong electron-phonon coupling and predicted highest known of MXenes revealed in 2H-MoN under biaxial stress
Komsilp Kotmool, Prutthipong Tsuppayakorn-aek, Thiti Bovornratanaraks +5
This letter reports the unexpectedly strong electron-phonon coupling (EPC) and the highest record ( 38 K) among the MXenes revealed in the 2H-MoN under biaxial s…
Towards User-Driven Neural Machine Translation
Huan Lin, Liang Yao, Baosong Yang +5
A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation…
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
Xin Liu, Baosong Yang, Dayiheng Liu +5
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretra…
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Jinpeng Zhang, Baijun Ji, Nini Xiao +4
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word…
G-Transformer for Document-level Machine Translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng +2
Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we furthe…
Adaptive Nearest Neighbor Machine Translation
Xin Zheng, Zhirui Zhang, Junliang Guo +4
kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval…
On Learning Universal Representations Across Languages
Xiangpeng Wei, Rongxiang Weng, Yue Hu +3
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, exis…
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Rongxiang Weng, Heng Yu, Shujian Huang +2
Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a m…
Contrastive Attention Mechanism for Abstractive Sentence Summarization
Xiangyu Duan, Hoongfei Yu, Mingming Yin +3
We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a g…
Improving Neural Machine Translation with Pre-trained Representation
Rongxiang Weng, Heng Yu, Shujian Huang +2
Monolingual data has been demonstrated to be helpful in improving the translation quality of neural machine translation (NMT). The current methods stay at the usage of word-level k…
Code-Switching for Enhancing NMT with Pre-Specified Translation
Kai Song, Yue Zhang, Heng Yu +3
Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags fo…
Improved English to Russian Translation by Neural Suffix Prediction
Kai Song, Yue Zhang, Min Zhang +1
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when deali…
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
Shaohui Kuang, Deyi Xiong, Weihua Luo +1
Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate…
Stabilized Sparse Ordinal Regression for Medical Risk Stratification
Truyen Tran, Dinh Phung, Wei Luo +1
The recent wide adoption of Electronic Medical Records (EMR) presents great opportunities and challenges for data mining. The EMR data is largely temporal, often noisy, irregular a…