no code implementations • ACL (RepL4NLP) 2021 • Kevin Huang, Peng Qi, Guangtao Wang, Tengyu Ma, Jing Huang
In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder.
no code implementations • 19 Mar 2024 • Hongzhe Zhang, Jiasheng Shi, Jing Huang
We proposed a novel one-to-one matching algorithm based on a quadratic score function $S_{\beta}(x_i, x_j)= \beta^T (x_i-x_j)(x_i-x_j)^T \beta$.
3 code implementations • 12 Mar 2024 • Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah D. Goodman, Christopher D. Manning, Christopher Potts
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability.
no code implementations • 11 Mar 2024 • Zhengyi Luo, Jinkun Cao, Rawal Khirodkar, Alexander Winkler, Jing Huang, Kris Kitani, Weipeng Xu
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets.
1 code implementation • 27 Feb 2024 • Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, Atticus Geiger
Individual neurons participate in the representation of multiple high-level concepts.
1 code implementation • 23 Jan 2024 • Zhengxuan Wu, Atticus Geiger, Jing Huang, Aryaman Arora, Thomas Icard, Christopher Potts, Noah D. Goodman
We respond to the recent paper by Makelov et al. (2023), which reviews subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al. 2023) and claims that these methods potentially cause "interpretability illusions".
no code implementations • 22 Dec 2023 • Nikhil Mehta, Kevin J Liang, Jing Huang, Fu-Jen Chu, Li Yin, Tal Hassner
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 30 Nov 2023 • Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei HUANG, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
no code implementations • 16 Oct 2023 • Dustin Axman, Avik Ray, Shubham Garg, Jing Huang
While dialog response generation has been widely studied on the agent side, it is not evident if similar generative models can be used to generate a large variety of, and often unexpected, user inputs that real dialog systems encounter in practice.
no code implementations • 6 Oct 2023 • Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris Kitani, Weipeng Xu
We close this gap by significantly increasing the coverage of our motion representation space.
no code implementations • 4 Oct 2023 • Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni de Fabritiis, Thomas E. Markland
Machine learning plays an important and growing role in molecular simulation.
no code implementations • 19 Sep 2023 • Jing Huang, Atticus Geiger, Karel D'Oosterlinck, Zhengxuan Wu, Christopher Potts
Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging.
no code implementations • 25 Aug 2023 • Tianyi Zhang, Zheng Wang, Jing Huang, Mohiuddin Muhammad Tasnim, Wei Shi
Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework.
1 code implementation • 30 May 2023 • Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, YiWen Chen, Tagyoung Chung, Jing Huang, Nanyun Peng
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
no code implementations • 26 May 2023 • I-Hung Hsu, Avik Ray, Shubham Garg, Nanyun Peng, Jing Huang
In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data.
1 code implementation • 26 May 2023 • Kai Zhang, Jun Yu, Eashan Adhikarla, Rong Zhou, Zhiling Yan, Yixin Liu, Zhengliang Liu, Lifang He, Brian Davison, Xiang Li, Hui Ren, Sunyang Fu, James Zou, Wei Liu, Jing Huang, Chen Chen, Yuyin Zhou, Tianming Liu, Xun Chen, Yong Chen, Quanzheng Li, Hongfang Liu, Lichao Sun
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
Ranked #1 on Text Summarization on MeQSum
no code implementations • 20 May 2023 • Bing Liu, Wei Luo, Gang Li, Jing Huang, Bo Yang
As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks.
no code implementations • 12 May 2023 • Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
no code implementations • CVPR 2023 • Hao Wen, Jing Huang, Huili Cui, Haozhe Lin, Yukun Lai, Lu Fang, Kun Li
However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution.
no code implementations • ICCV 2023 • Peri Akiva, Jing Huang, Kevin J Liang, Rama Kovvuri, Xingyu Chen, Matt Feiszli, Kristin Dana, Tal Hassner
Understanding the visual world from the perspective of humans (egocentric) has been a long-standing challenge in computer vision.
1 code implementation • 19 Dec 2022 • Jing Huang, Zhengxuan Wu, Kyle Mahowald, Christopher Potts
Language tasks involving character-level manipulations (e. g., spelling corrections, arithmetic operations, word games) are challenging for models operating on subword units.
1 code implementation • 24 Oct 2022 • Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.
1 code implementation • 24 Oct 2022 • Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master.
1 code implementation • 13 Oct 2022 • Jing Huang, Kevin J Liang, Rama Kovvuri, Tal Hassner
Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets.
no code implementations • 3 Aug 2022 • Peng Qi, Guangtao Wang, Jing Huang
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output.
no code implementations • 27 Jun 2022 • Jiyang Yu, Jingen Liu, Jing Huang, Wei zhang, Tao Mei
To this end, we propose a novel network to encode face videos into the latent space of StyleGAN for semantic face video manipulation.
1 code implementation • CVPR 2022 • Kehong Gong, Bingbing Li, Jianfeng Zhang, Tao Wang, Jing Huang, Michael Bi Mi, Jiashi Feng, Xinchao Wang
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #37 on 3D Human Pose Estimation on MPI-INF-3DHP
no code implementations • ACL 2022 • Chao Shang, Guangtao Wang, Peng Qi, Jing Huang
These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on Question Answering on CronQuestions
no code implementations • 20 Jan 2022 • Tiansong Zhou, Jing Huang, Tao Yu, Ruizhi Shao, Kun Li
To this end, we propose HDhuman, which uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network with geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering.
no code implementations • 18 Jan 2022 • Zhengyuan Yang, Jingen Liu, Jing Huang, Xiaodong He, Tao Mei, Chenliang Xu, Jiebo Luo
In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation.
1 code implementation • EMNLP (sustainlp) 2021 • Gengyu Wang, Xiaochen Hou, Diyi Yang, Kathleen McKeown, Jing Huang
Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion.
2 code implementations • 16 Aug 2021 • Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
1 code implementation • 2 Jul 2021 • Qing Guo, Junya Chen, Dong Wang, Yuewei Yang, Xinwei Deng, Lawrence Carin, Fan Li, Jing Huang, Chenyang Tao
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
no code implementations • AKBC 2021 • Chao Shang, Peng Qi, Guangtao Wang, Jing Huang, Youzheng Wu, BoWen Zhou
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
no code implementations • 9 Jun 2021 • Zichuan Lin, Jing Huang, BoWen Zhou, Xiaodong He, Tengyu Ma
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e. g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation.
no code implementations • NAACL 2021 • Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang
The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation.
no code implementations • 13 May 2021 • Peng Qi, Jing Huang, Youzheng Wu, Xiaodong He, BoWen Zhou
Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts.
no code implementations • CVPR 2021 • Amanpreet Singh, Guan Pang, Mandy Toh, Jing Huang, Wojciech Galuba, Tal Hassner
A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system.
Optical Character Recognition Optical Character Recognition (OCR) +2
1 code implementation • 3 May 2021 • Jing Huang, Jie Yang
In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs.
1 code implementation • 2 May 2021 • Jing Huang, Xiaolin Huang, Jie Yang
Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains.
1 code implementation • CVPR 2021 • Jing Huang, Guan Pang, Rama Kovvuri, Mandy Toh, Kevin J Liang, Praveen Krishnan, Xi Yin, Tal Hassner
Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results.
no code implementations • NAACL 2021 • Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, BoWen Zhou
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
1 code implementation • 21 Oct 2020 • Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang
In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
Ranked #6 on Relation Extraction on ReDocRED
Document-level Relation Extraction Multi-Label Classification +2
1 code implementation • 29 Sep 2020 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec
Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
1 code implementation • 19 Sep 2020 • Bin Wang, Guangtao Wang, Jing Huang, Jiaxuan You, Jure Leskovec, C. -C. Jay Kuo
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
no code implementations • 27 Aug 2020 • Kevin Huang, Guangtao Wang, Tengyu Ma, Jing Huang
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document.
Ranked #14 on Relation Extraction on DocRED
1 code implementation • ECCV 2020 • Minghui Liao, Guan Pang, Jing Huang, Tal Hassner, Xiang Bai
Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress.
Ranked #11 on Text Spotting on Total-Text
no code implementations • 17 Jul 2020 • Vinay Kothapally, Wei Xia, Shahram Ghorbani, John H. L. Hansen, Wei Xue, Jing Huang
The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications.
1 code implementation • CVPR 2021 • Liwei Wang, Jing Huang, Yin Li, Kun Xu, Zhengyuan Yang, Dong Yu
Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed.
no code implementations • ICLR 2020 • Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
no code implementations • 13 Apr 2020 • Tae Jin Park, Kyu J. Han, Jing Huang, Xiaodong He, Bo-Wen Zhou, Panayiotis Georgiou, Shrikanth Narayanan
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 4 Apr 2020 • Ming Tu, Jing Huang, Xiaodong He, Bo-Wen Zhou
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
no code implementations • 24 Nov 2019 • Jing Huang, Hengfeng Miao, Lin Li, Yuanqiao Wen, Changshi Xiao
This paper attempts to find a solution to guarantee the effectiveness of waterline detection for inland maritime applications with general digital camera sensor.
no code implementations • ACL 2020 • Yun Tang, Jing Huang, Guangtao Wang, Xiaodong He, Bo-Wen Zhou
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE.
Ranked #18 on Link Prediction on FB15k-237
no code implementations • 4 Nov 2019 • Haoqi Li, Ming Tu, Jing Huang, Shrikanth Narayanan, Panayiotis Georgiou
In this paper, we propose a machine learning framework to obtain speech emotion representations by limiting the effect of speaker variability in the speech signals.
no code implementations • CONLL 2019 • Kevin Huang, Yun Tang, Jing Huang, Xiaodong He, Bo-Wen Zhou
We test the relation module on the SQuAD 2. 0 dataset using both the BiDAF and BERT models as baseline readers.
1 code implementation • 1 Nov 2019 • Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, Bo-Wen Zhou
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
no code implementations • 25 Oct 2019 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.
no code implementations • NAACL (TextGraphs) 2021 • Xiaochen Hou, Jing Huang, Guangtao Wang, Xiaodong He, BoWen Zhou
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.
no code implementations • 23 Oct 2019 • Kevin Huang, Yun Tang, Jing Huang, Xiaodong He, Bo-Wen Zhou
In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e. g. the recently proposed SQuAD 2. 0 task).
1 code implementation • 29 Aug 2019 • Shuaichen Chang, PengFei Liu, Yun Tang, Jing Huang, Xiaodong He, Bo-Wen Zhou
Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task.
no code implementations • 5 Aug 2019 • Wei Xia, Jing Huang, John H. L. Hansen
Speaker verification systems often degrade significantly when there is a language mismatch between training and testing data.
no code implementations • 12 Jun 2019 • Ming Tu, Jing Huang, Xiaodong He, Bo-Wen Zhou
In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags.
no code implementations • ACL 2019 • Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bo-Wen Zhou
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
no code implementations • 21 Feb 2019 • Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bo-Wen Zhou
This paper aims to improve the widely used deep speaker embedding x-vector model.
no code implementations • 16 Nov 2018 • Jing Huang, Viswanath Sivakumar, Mher Mnatsakanyan, Guan Pang
In this work, we extend the scene-text extraction system at Facebook, Rosetta, to efficiently handle text in various orientations.
1 code implementation • 11 Nov 2018 • Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bo-Wen Zhou
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Ranked #28 on Link Prediction on FB15k-237
1 code implementation • 17 May 2018 • Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar
We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images.
no code implementations • 20 May 2017 • Tim Danford, Onur Filiz, Jing Huang, Brian Karrer, Manohar Paluri, Guan Pang, Vish Ponnampalam, Nicolas Stier-Moses, Birce Tezel
This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks.
1 code implementation • 11 Apr 2017 • Liwei Wang, Yin Li, Jing Huang, Svetlana Lazebnik
Image-language matching tasks have recently attracted a lot of attention in the computer vision field.