no code implementations • ECCV 2020 • Yuan Liu, Ruoteng Li, Yu Cheng, Robby T. Tan, Xiubao Sui
To facilitate the future prediction ability, we follow three key observations: 1) object motion trajectory is affected significantly by camera motion; 2) the past trajectory of an object can act as a salient cue to estimate the object motion in the spatial domain; 3) previous frames contain the surroundings and appearance of the target object, which is useful for predicting the target object’s future locations.
no code implementations • 20 May 2024 • Jinxin Xu, Haixin Wu, Yu Cheng, Liyang Wang, Xin Yang, Xintong Fu, Yuelong Su
This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm.
no code implementations • 17 May 2024 • Yu Cheng, Qin Yang, Liyang Wang, Ao Xiang, Jingyu Zhang
In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability.
no code implementations • 25 Apr 2024 • Ao Xiang, Jingyu Zhang, Qin Yang, Liyang Wang, Yu Cheng
With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues.
no code implementations • 10 Apr 2024 • Jingyu Zhang, Ao Xiang, Yu Cheng, Qin Yang, Liyang Wang
With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring.
no code implementations • 26 Mar 2024 • Wei Tao, Yucheng Zhou, Wenqiang Zhang, Yu Cheng
Motivated by the empirical findings, we propose a novel LLM-based Multi-Agent framework for GitHub Issue reSolution, MAGIS, consisting of four kinds of agents customized for the software evolution: Manager, Repository Custodian, Developer, and Quality Assurance Engineer agents.
1 code implementation • 18 Mar 2024 • Wendi Li, Wei Wei, Kaihe Xu, Wenfeng Xie, Dangyang Chen, Yu Cheng
To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs).
no code implementations • NeurIPS 2023 • Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright
We introduce a general framework for efficiently finding an approximate SOSP with \emph{dimension-independent} accuracy guarantees, using $\widetilde{O}({D^2}/{\epsilon})$ samples where $D$ is the ambient dimension and $\epsilon$ is the fraction of corrupted datapoints.
no code implementations • 24 Feb 2024 • Ying Shen, Zhiyang Xu, Qifan Wang, Yu Cheng, Wenpeng Yin, Lifu Huang
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks.
no code implementations • 19 Feb 2024 • Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, Bryan Hooi
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
no code implementations • 18 Feb 2024 • Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
no code implementations • 27 Jan 2024 • Linmi Tao, Ruiyang Liu, Donglai Tao, Wu Xia, Feilong Ma, Yu Cheng, Jingmao Cui
Tao general difference (TGD) is a novel theory and approach to difference computation for discrete sequences and arrays in multidimensional space.
1 code implementation • 26 Dec 2023 • Chenghao Fan, Wei Wei, Xiaoye Qu, Zhenyi Lu, Wenfeng Xie, Yu Cheng, Dangyang Chen
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks.
1 code implementation • 17 Dec 2023 • Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Wu Yuan, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, Zhenguo Li
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
no code implementations • 3 Nov 2023 • Xing Di, Yiyu Zheng, Xiaoming Liu, Yu Cheng
This paper presents a novel approach, called Prototype-based Self-Distillation (ProS), for unsupervised face representation learning.
1 code implementation • 2 Oct 2023 • Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen
Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to duplication of the network layers into multiple copies as experts; and (b) Redundancy in Experts, as common learning-based routing policies suffer from representational collapse.
1 code implementation • 27 Sep 2023 • Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, Fajie Yuan
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.
no code implementations • 21 Sep 2023 • Yu Cheng, Bo wang, Robby T. Tan
In 3D human shape and pose estimation from a monocular video, models trained with limited labeled data cannot generalize well to videos with occlusion, which is common in the wild videos.
2 code implementations • 14 Sep 2023 • JiaQi Zhang, Yu Cheng, Yongxin Ni, Yunzhu Pan, Zheng Yuan, Junchen Fu, Youhua Li, Jie Wang, Fajie Yuan
The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning recommendation dataset and benchmark suites is one of the biggest obstacles.
1 code implementation • 13 Sep 2023 • Yu Cheng, Yunzhu Pan, JiaQi Zhang, Yongxin Ni, Aixin Sun, Fajie Yuan
Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels.
Ranked #1 on Recommendation Systems on PixelRec
1 code implementation • 26 Aug 2023 • Jianqiang Xia, Dianxi Shi, Ke Song, Linna Song, Xiaolei Wang, Songchang Jin, Li Zhou, Yu Cheng, Lei Jin, Zheng Zhu, Jianan Li, Gang Wang, Junliang Xing, Jian Zhao
With this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
Ranked #1 on Rgb-T Tracking on GTOT
no code implementations • NeurIPS 2023 • Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
no code implementations • 15 Jun 2023 • Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang
We show that, our algorithm obtains an $\varepsilon$-optimal policy with only $\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^3})$ samples, where $\varepsilon$ is the suboptimality gap and $d$ is a complexity measure of the function class approximating the policy.
no code implementations • 24 May 2023 • Woojeong Jin, Subhabrata Mukherjee, Yu Cheng, Yelong Shen, Weizhu Chen, Ahmed Hassan Awadallah, Damien Jose, Xiang Ren
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks.
no code implementations • 19 May 2023 • Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, JiaQi Zhang, Fajie Yuan
Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm.
1 code implementation • CVPR 2023 • Heyuan Li, Bo wang, Yu Cheng, Mohan Kankanhalli, Robby T. Tan
Thanks to the proposed fusion module, our method is robust not only to occlusion and large pitch and roll view angles, which is the benefit of our image space approach, but also to noise and large yaw angles, which is the benefit of our model space method.
Ranked #1 on 3D Face Reconstruction on AFLW2000-3D (Mean NME metric)
no code implementations • 14 May 2023 • Linmi Tao, Ruiyang Liu, Donglai Tao, Wu Xia, Feilong Ma, Yu Cheng, Jingmao Cui
This stems from a key disconnect between the infinitesimal quantities in continuous differentiation and the finite intervals in its discrete counterpart.
no code implementations • 6 May 2023 • Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Zichuan Xu, Haozhao Wang, Xing Di, Weining Lu, Yu Cheng
This paper addresses the temporal sentence grounding (TSG).
2 code implementations • 18 Mar 2023 • Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao
Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e. g., low-rank increments.
1 code implementation • 24 Feb 2023 • Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
Furthermore, drawing from the growing body of work on self-supervised learning, we propose a novel masking objective for which recovering the ground-truth dictionary is in fact optimal as the signal increases for a large class of data-generating processes.
no code implementations • 5 Jan 2023 • Daizong Liu, Xiang Fang, Pan Zhou, Xing Di, Weining Lu, Yu Cheng
Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query.
no code implementations • 2 Jan 2023 • Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning.
no code implementations • CVPR 2023 • Zenghui Yuan, Pan Zhou, Kai Zou, Yu Cheng
Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs).
1 code implementation • 26 Oct 2022 • Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang
However, when deploying MTL onto those real-world systems that are often resource-constrained or latency-sensitive, two prominent challenges arise: (i) during training, simultaneously optimizing all tasks is often difficult due to gradient conflicts across tasks; (ii) at inference, current MTL regimes have to activate nearly the entire model even to just execute a single task.
1 code implementation • 29 Aug 2022 • Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank Wang
Diffusion models (DMs) have shown great potential for high-quality image synthesis.
no code implementations • 25 Aug 2022 • Yu Cheng, Yihao Ai, Bo wang, Xinchao Wang, Robby T. Tan
In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection.
1 code implementation • 26 Jul 2022 • Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan
Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a shared embedding space.
1 code implementation • 12 Jul 2022 • Yuhua Sun, Tailai Zhang, Xingjun Ma, Pan Zhou, Jian Lou, Zichuan Xu, Xing Di, Yu Cheng, Lichao
In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations.
1 code implementation • 23 May 2022 • Makesh Narsimhan Sreedhar, Xiangpeng Wan, Yu Cheng, Junjie Hu
Subword tokenization schemes are the dominant technique used in current NLP models.
no code implementations • 16 May 2022 • Hanrui Zhang, Yu Cheng, Vincent Conitzer
Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and $\log(1/\varepsilon)$, and returns a policy that is optimal up to an additive error of $\varepsilon$.
1 code implementation • 14 May 2022 • Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario.
Ranked #1 on Dialogue State Tracking on CoSQL
1 code implementation • Findings (NAACL) 2022 • Boxin Wang, Chejian Xu, Xiangyu Liu, Yu Cheng, Bo Li
In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e. g., WordNet), contextualized semantic space (e. g., the embedding space of BERT clusterings), or the combination of these spaces.
1 code implementation • 2 May 2022 • Yu Cheng, Bo wang, Robby T. Tan
Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i. e., the coordinates based on the center of the target person.
Ranked #1 on 3D Human Pose Estimation on JTA
3D Multi-Person Pose Estimation (absolute) 3D Multi-Person Pose Estimation (root-relative) +4
1 code implementation • 20 Mar 2022 • Yuezihan Jiang, Yu Cheng, Hanyu Zhao, Wentao Zhang, Xupeng Miao, Yu He, Liang Wang, Zhi Yang, Bin Cui
We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs.
1 code implementation • CVPR 2022 • Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang
However, a "head-to-toe assessment" regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field.
no code implementations • 14 Jan 2022 • Daizong Liu, Xiaoye Qu, Yinzhen Wang, Xing Di, Kai Zou, Yu Cheng, Zichuan Xu, Pan Zhou
Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query.
no code implementations • 3 Jan 2022 • Daizong Liu, Xiaoye Qu, Xing Di, Yu Cheng, Zichuan Xu, Pan Zhou
To tackle this issue, we propose a memory-augmented network, called Memory-Guided Semantic Learning Network (MGSL-Net), that learns and memorizes the rarely appeared content in TSG tasks.
1 code implementation • 4 Nov 2021 • Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, Bo Li
In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.
Ranked #1 on Adversarial Robustness on AdvGLUE
1 code implementation • 30 Oct 2021 • Xuxi Chen, Tianlong Chen, Weizhu Chen, Ahmed Hassan Awadallah, Zhangyang Wang, Yu Cheng
To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights.
no code implementations • 16 Oct 2021 • Mengnan Du, Subhabrata Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu, Ahmed Hassan Awadallah
Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks.
1 code implementation • ACL 2022 • Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, Xiang Ren
Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning.
Ranked #4 on Image Captioning on Flickr30k Captions test (SPICE metric)
no code implementations • 29 Sep 2021 • Haoxuan You, Luowei Zhou, Bin Xiao, Noel C Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan
Large-scale multimodal contrastive pretraining has demonstrated great utility to support high performance in a range of downstream tasks by mapping multiple modalities into a shared embedding space.
1 code implementation • 23 Sep 2021 • Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel M. Kane, Mahdi Soltanolkotabi
We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental tasks of robust sparse mean estimation and robust sparse PCA.
no code implementations • CVPR 2021 • Yiting Li, Haiyue Zhu, Yu Cheng, Wenxin Wang, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee
The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a novel aspect of hard example mining.
1 code implementation • NeurIPS 2021 • Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
For example, our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0. 28% top-1 accuracy, and meanwhile enjoys 49. 32% FLOPs and 4. 40% running time savings.
Ranked #20 on Efficient ViTs on ImageNet-1K (with DeiT-T)
1 code implementation • 8 Jun 2021 • Linjie Li, Jie Lei, Zhe Gan, Licheng Yu, Yen-Chun Chen, Rohit Pillai, Yu Cheng, Luowei Zhou, Xin Eric Wang, William Yang Wang, Tamara Lee Berg, Mohit Bansal, Jingjing Liu, Lijuan Wang, Zicheng Liu
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task.
1 code implementation • ICLR 2021 • Yu Cheng, Honghao Lin
We achieve this by establishing a direct connection between robust learning of Bayesian networks and robust mean estimation.
no code implementations • 23 Apr 2021 • Zhe Gan, Yen-Chun Chen, Linjie Li, Tianlong Chen, Yu Cheng, Shuohang Wang, Jingjing Liu, Lijuan Wang, Zicheng Liu
However, we can find "relaxed" winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy.
no code implementations • 12 Apr 2021 • Hanrui Zhang, Yu Cheng, Vincent Conitzer
We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal's limited verification power.
1 code implementation • CVPR 2021 • Yu Cheng, Bo wang, Bo Yang, Robby T. Tan
Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions.
3D Multi-Person Pose Estimation (absolute) 3D Multi-Person Pose Estimation (root-relative) +2
no code implementations • 1 Apr 2021 • Luowei Zhou, Jingjing Liu, Yu Cheng, Zhe Gan, Lei Zhang
This work concerns video-language pre-training and representation learning.
no code implementations • CVPR 2021 • Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu, Jingjing Liu
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language.
1 code implementation • NeurIPS 2021 • Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, Zhangyang Wang
Based on these results, we articulate the Elastic Lottery Ticket Hypothesis (E-LTH): by mindfully replicating (or dropping) and re-ordering layers for one network, its corresponding winning ticket could be stretched (or squeezed) into a subnetwork for another deeper (or shallower) network from the same family, whose performance is nearly the same competitive as the latter's winning ticket directly found by IMP.
1 code implementation • CVPR 2021 • Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Yu Cheng, Wei Wei, Zichuan Xu, Yulai Xie
This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query.
1 code implementation • 22 Mar 2021 • Tianlong Chen, Yu Cheng, Zhe Gan, JianFeng Wang, Lijuan Wang, Zhangyang Wang, Jingjing Liu
Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models.
1 code implementation • NAACL 2021 • Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, Shiqi Xu
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
1 code implementation • NeurIPS 2021 • Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang
Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models.
1 code implementation • 15 Feb 2021 • Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He
For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.
no code implementations • 15 Jan 2021 • Jin Sun, Yu Cheng, Xiao-Gang He
Or it may be the Majoron in models from lepton number violation in producing seesaw Majorana neutrino masses if the symmetry breaking scale is much higher than the electroweak scale.
High Energy Physics - Phenomenology
no code implementations • Findings (ACL) 2021 • Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Transformer has become ubiquitous in the deep learning field.
no code implementations • 1 Jan 2021 • Tianlong Chen, Yu Cheng, Zhe Gan, Yu Hu, Zhangyang Wang, Jingjing Liu
Adversarial training is an effective method to combat adversarial attacks in order to create robust neural networks.
no code implementations • 1 Jan 2021 • Minhao Cheng, Zhe Gan, Yu Cheng, Shuohang Wang, Cho-Jui Hsieh, Jingjing Liu
By incorporating different feature maps after the masking, we can distill better features to help model generalization.
1 code implementation • ACL 2021 • Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Zhangyang Wang, Jingjing Liu
Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks.
1 code implementation • 22 Dec 2020 • Yu Cheng, Bo wang, Bo Yang, Robby T. Tan
To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters.
Ranked #1 on Root Joint Localization on Human3.6M
3D Absolute Human Pose Estimation 3D Multi-Person Pose Estimation (absolute) +5
no code implementations • 18 Dec 2020 • Anilesh K. Krishnaswamy, Zhihao Jiang, Kangning Wang, Yu Cheng, Kamesh Munagala
Instead, we propose a fairness notion whose guarantee, on each group $g$ in a class $\mathcal{G}$, is relative to the performance of the best classifier on $g$.
no code implementations • 3 Dec 2020 • Yu Cheng, Wei Liao
We find that the mass of the dark sector singlet fermion can be GeV scale or MeV scale and the interaction of the dark sector singlet fermion is very weak.
High Energy Physics - Phenomenology
no code implementations • 12 Nov 2020 • Jiangtao Kong, Yu Cheng, Benjia Zhou, Kai Li, Junliang Xing
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
no code implementations • 15 Oct 2020 • YuAn Liu, Ruoteng Li, Robby T. Tan, Yu Cheng, Xiubao Sui
Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory.
1 code implementation • EMNLP 2020 • Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jing Jiang, Jingjing Liu
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering.
no code implementations • EMNLP 2020 • Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, Jingjing Liu
Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.
2 code implementations • ICLR 2021 • Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu
Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks.
Ranked #3 on Natural Language Inference on ANLI test (using extra training data)
1 code implementation • 3 Oct 2020 • Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu
In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem.
1 code implementation • EMNLP 2020 • Siqi Sun, Zhe Gan, Yu Cheng, Yuwei Fang, Shuohang Wang, Jingjing Liu
Existing language model compression methods mostly use a simple L2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one.
no code implementations • 13 Sep 2020 • Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Transformer has become ubiquitous in the deep learning field.
Ranked #1 on Open-Domain Question Answering on SearchQA
no code implementations • 6 Aug 2020 • Xiaoye Qu, Pengwei Tang, Zhikang Zhou, Yu Cheng, Jianfeng Dong, Pan Zhou
In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction.
1 code implementation • ICML 2020 • Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu
In GOT, cross-domain alignment is formulated as a graph matching problem, by representing entities into a dynamically-constructed graph.
1 code implementation • 21 Jun 2020 • Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein
Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in face of noisy objectives.
2 code implementations • NeurIPS 2020 • Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning.
Ranked #7 on Visual Entailment on SNLI-VE val (using extra training data)
no code implementations • ECCV 2020 • Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, Jingjing Liu
To reveal the secrets behind the scene of these powerful models, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e. g., Visual Coreference Resolution, Visual Relation Detection, Linguistic Probing Tasks) generalizable to standard pre-trained V+L models, aiming to decipher the inner workings of multimodal pre-training (e. g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings).
no code implementations • ICML 2020 • Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
We study the problem of high-dimensional robust mean estimation in the presence of a constant fraction of adversarial outliers.
3 code implementations • EMNLP 2020 • Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, Jingjing Liu
We present HERO, a novel framework for large-scale video+language omni-representation learning.
Ranked #1 on Video Retrieval on TVR
no code implementations • NAACL 2021 • Shuyang Dai, Zhe Gan, Yu Cheng, Chenyang Tao, Lawrence Carin, Jingjing Liu
In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar, Dianqi Li, Jingjing Liu
To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.
no code implementations • AAAI Conference on Artificial Intelligence, AAAI 2020 2020 • Yu Cheng, Bo Yang, Bo wang, Robby T. Tan
Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in recent years.
Ranked #3 on 3D Human Pose Estimation on HumanEva-I
1 code implementation • CVPR 2020 • Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, Zhangyang Wang
We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins (eg, 3. 83% on robust accuracy and 1. 3% on standard accuracy, on the CIFAR-10 dataset), compared with the conventional end-to-end adversarial training baseline.
1 code implementation • CVPR 2020 • Yandong Li, Yu Cheng, Zhe Gan, Licheng Yu, Liqiang Wang, Jingjing Liu
We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.
1 code implementation • CVPR 2020 • Jingzhou Liu, Wenhu Chen, Yu Cheng, Zhe Gan, Licheng Yu, Yiming Yang, Jingjing Liu
We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text.
no code implementations • 31 Jan 2020 • Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers.
no code implementations • NeurIPS 2019 • Hanrui Zhang, Yu Cheng, Vincent Conitzer
In other settings, the principal may not even be able to observe samples directly; instead, she must rely on signals that the agent is able to send based on the samples that he obtains, and he will choose these signals strategically.
no code implementations • 25 Nov 2019 • Xiaojiang Yang, Wendong Bi, Yitong Sun, Yu Cheng, Junchi Yan
Most existing works on disentangled representation learning are solely built upon an marginal independence assumption: all factors in disentangled representations should be statistically independent.
1 code implementation • ACL 2020 • Yichen Huang, Yizhe Zhang, Oussama Elachqar, Yu Cheng
Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion.
1 code implementation • ACL 2020 • Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, Jingjing Liu
Experiments show that the proposed approach significantly outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization.
1 code implementation • ACL 2020 • Jiacheng Xu, Zhe Gan, Yu Cheng, Jingjing Liu
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models.
no code implementations • 14 Oct 2019 • Shuangjie Xu, Feng Xu, Yu Cheng, Pan Zhou
In this paper, we investigate a novel problem of telling the difference between image pairs in natural language.
1 code implementation • 8 Oct 2019 • Wenhu Chen, Zhe Gan, Linjie Li, Yu Cheng, William Wang, Jingjing Liu
To design a more powerful NMN architecture for practical use, we propose Meta Module Network (MMN) centered on a novel meta module, which can take in function recipes and morph into diverse instance modules dynamically.
no code implementations • ICCV 2019 • Yu Cheng, Bo Yang, Bo Wang, Wending Yan, Robby T. Tan
In addition, we use this model to create a pose regularization constraint, preferring the 2D estimations of unreliable keypoints to be occluded.
Ranked #4 on 3D Human Pose Estimation on HumanEva-I
2 code implementations • ICLR 2020 • Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu
Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models.
7 code implementations • ECCV 2020 • Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu
Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).
Ranked #3 on Visual Question Answering (VQA) on VCR (Q-A) test
no code implementations • 25 Sep 2019 • Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding.
1 code implementation • 11 Sep 2019 • Junjie Hu, Yu Cheng, Zhe Gan, Jingjing Liu, Jianfeng Gao, Graham Neubig
Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr.
Ranked #10 on Visual Storytelling on VIST
no code implementations • 11 Sep 2019 • Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu, Lawrence Carin
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
3 code implementations • IJCNLP 2019 • Siqi Sun, Yu Cheng, Zhe Gan, Jingjing Liu
Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks.
1 code implementation • IJCNLP 2019 • Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Ming-Ting Sun, Bill Dolan
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
no code implementations • 7 Aug 2019 • Zhikang Zou, Yu Cheng, Xiaoye Qu, Shouling Ji, Xiaoxiao Guo, Pan Zhou
ACM-CNN consists of three types of modules: a coarse network, a fine network, and a smooth network.
no code implementations • 24 Jul 2019 • Duo Wang, Ming Li, Nir Ben-Shlomo, C. Eduardo Corrales, Yu Cheng, Tao Zhang, Jagadeesan Jayender
The model is trained jointly in a multi-task learning setting.
8 code implementations • 17 Jun 2019 • Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
no code implementations • 11 Jun 2019 • Yu Cheng, Ilias Diakonikolas, Rong Ge, David Woodruff
We study the problem of estimating the covariance matrix of a high-dimensional distribution when a small constant fraction of the samples can be arbitrarily corrupted.
no code implementations • NAACL 2019 • Xiaoye Qu, Zhikang Zou, Yu Cheng, Yang Yang, Pan Zhou
Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain.
no code implementations • 4 Apr 2019 • Duo Wang, Yu Cheng, Mo Yu, Xiaoxiao Guo, Tao Zhang
The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes.
1 code implementation • ICCV 2019 • Linjie Li, Zhe Gan, Yu Cheng, Jingjing Liu
In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects.
no code implementations • 19 Mar 2019 • Danli Wu, Yu Cheng, Dehan Luo, Kin-Yeung Wong, Kevin Hung, Zhijing Yang
Predicting odor's pleasantness simplifies the evaluation of odors and has the potential to be applied in perfumes and environmental monitoring industry.
1 code implementation • 9 Feb 2019 • Zihao Zhu, Changchang Yin, Buyue Qian, Yu Cheng, Jishang Wei, Fei Wang
One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse.
no code implementations • ACL 2019 • Zhe Gan, Yu Cheng, Ahmed El Kholy, Linjie Li, Jingjing Liu, Jianfeng Gao
This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image.
no code implementations • 26 Jan 2019 • Yu Cheng, Mo Yu, Xiaoxiao Guo, Bo-Wen Zhou
Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners.
1 code implementation • 25 Jan 2019 • Lecheng Zheng, Yu Cheng, Jingrui He
However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.).
no code implementations • 20 Dec 2018 • Yu Cheng, Zhe Gan, Yitong Li, Jingjing Liu, Jianfeng Gao
The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the provided textual description; 2) step-by-step region-level modification to maintain visual consistency across the generated image sequence in each session.
1 code implementation • CVPR 2019 • Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, Jianfeng Gao
We therefore propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework.
no code implementations • 23 Nov 2018 • Yu Cheng, Ilias Diakonikolas, Rong Ge
We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted.
1 code implementation • 19 Nov 2018 • Haoran You, Yu Cheng, Tianheng Cheng, Chunliang Li, Pan Zhou
We evaluate the proposed Bayesian CycleGAN on multiple benchmark datasets, including Cityscapes, Maps, and Monet2photo.
1 code implementation • 2 Sep 2018 • Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, ShengMei Shen, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on Age-Invariant Face Recognition on MORPH Album2
no code implementations • 16 Jul 2018 • Xinxing Su, Yingtian Zou, Yu Cheng, Shuangjie Xu, Mo Yu, Pan Zhou
We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem.
no code implementations • CVPR 2018 • Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, Fang Zhao, Karlekar Jayashree, Sugiri Pranata, ShengMei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
2 code implementations • NAACL 2018 • Mo Yu, Xiaoxiao Guo, Jin-Feng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bo-Wen Zhou
We study few-shot learning in natural language domains.
1 code implementation • NeurIPS 2018 • Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Schmidt Feris
Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.
2 code implementations • 10 Apr 2018 • Jian Zhao, Jianshu Li, Yu Cheng, Li Zhou, Terence Sim, Shuicheng Yan, Jiashi Feng
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on Multi-Human Parsing on PASCAL-Part
2 code implementations • 5 Apr 2018 • Xi Ouyang, Yu Cheng, Yifan Jiang, Chun-Liang Li, Pan Zhou
The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.
Ranked #2 on Scene Text Recognition on MSDA
no code implementations • 28 Mar 2018 • Yu Cheng, Rong Ge
Matrix completion is a well-studied problem with many machine learning applications.
no code implementations • 8 Jan 2018 • Yu Cheng, Angus Wong, Kevin Hung, Zhizhong Li, Weitong Li, Jun Zhang
That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time.
no code implementations • 21 Nov 2017 • Yu Cheng, Shaddin Dughmi, David Kempe
Our main result is a clean and tight characterization of positional voting rules that have constant expected distortion (independent of the number of candidates and the metric space).
2 code implementations • ICLR 2018 • Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng
We show that the Sobolev IPM compares two distributions in high dimensions based on weighted conditional Cumulative Distribution Functions (CDF) of each coordinate on a leave one out basis.
no code implementations • 23 Oct 2017 • Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang
Methods of parameter pruning and quantization are described first, after that the other techniques are introduced.
no code implementations • 26 Sep 2017 • Devu Manikantan Shilay, Kin Gwn Lorey, Tianshu Weiz, Teems Lovetty, Yu Cheng
A significant challenge in energy system cyber security is the current inability to detect cyber-physical attacks targeting and originating from distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible loads, and electric vehicles.
no code implementations • 6 Sep 2017 • Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, Yan Liu
We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance.
1 code implementation • ICCV 2017 • Shuangjie Xu, Yu Cheng, Kang Gu, Yang Yang, Shiyu Chang, Pan Zhou
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction.
2 code implementations • NeurIPS 2017 • Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos
In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN.
no code implementations • 4 May 2017 • Yu Cheng, Shaddin Dughmi, David Kempe
However, we show that independence alone is not enough to achieve the upper bound: even when candidates are drawn independently, if the population of candidates can be different from the voters, then an upper bound of $2$ on the approximation is tight.
no code implementations • 25 Jan 2017 • Zhengping Che, Yu Cheng, Zhaonan Sun, Yan Liu
To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts.
no code implementations • NeurIPS 2016 • Xi Chen, Yu Cheng, Bo Tang
This is the first upper bound for $RTD(C)$ that depends only on $VCD(C)$, independent of the size of the concept class $|C|$ and its~domain size $n$.
4 code implementations • CVPR 2017 • Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogerio Feris
We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.
1 code implementation • CVPR 2017 • Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogerio Feris
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them.
1 code implementation • 6 Nov 2016 • Shuangfei Zhai, Yu Cheng, Rogerio Feris, Zhongfei Zhang
We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\mathbf{x})$ is approximated by a variational distribution $q(\mathbf{x})$ that is easy to sample from.
no code implementations • NeurIPS 2016 • Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs).
1 code implementation • NeurIPS 2018 • Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
We investigate the problem of learning Bayesian networks in a robust model where an $\epsilon$-fraction of the samples are adversarially corrupted.
2 code implementations • 25 May 2016 • Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures.
no code implementations • CVPR 2016 • Jing Wang, Yu Cheng, Rogerio Schmidt Feris
These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features.
no code implementations • 12 Feb 2015 • Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng
Our work is particularly motivated by the algorithmic problems for speeding up the classic Newton's method in applications such as computing the inverse square-root of the precision matrix of a Gaussian random field, as well as computing the $q$th-root transition (for $q\geq1$) in a time-reversible Markov model.
no code implementations • ICCV 2015 • Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, Shih-Fu Chang
We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
no code implementations • 20 Oct 2014 • Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng
random samples for $n$-dimensional Gaussian random fields with SDDM precision matrices.
no code implementations • CVPR 2014 • Yu Cheng, Quanfu Fan, Sharath Pankanti, Alok Choudhary
Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence.