Search Results for author: Jiashi Feng

Found 244 papers, 90 papers with code

Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond

no code implementations NeurIPS 2021 Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan

Specifically, we prove that lookahead using SGD as its inner-loop optimizer can better balance the optimization error and generalization error to achieve smaller excess risk error than vanilla SGD on (strongly) convex problems and nonconvex problems with Polyak-{\L}ojasiewicz condition which has been observed/proved in neural networks.

Shunted Self-Attention via Multi-Scale Token Aggregation

1 code implementation30 Nov 2021 Sucheng Ren, Daquan Zhou, Shengfeng He, Jiashi Feng, Xinchao Wang

This novel merging scheme enables the self-attention to learn relationships between objects with different sizes and simultaneously reduces the token numbers and the computational cost.

MetaFormer is Actually What You Need for Vision

2 code implementations22 Nov 2021 Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance.

Image Classification Semantic Segmentation

Direct Multi-view Multi-person 3D Pose Estimation

1 code implementation NeurIPS 2021 Tao Wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng

Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks.

3D Pose Estimation

Deep Long-Tailed Learning: A Survey

1 code implementation9 Oct 2021 Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, Jiashi Feng

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution.

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

1 code implementation7 Oct 2021 Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, Vincent Y. F. Tan

Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization.

How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?

no code implementations NeurIPS Workshop ImageNet_PPF 2021 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Self-Supervised Learning

PnP-DETR: Towards Efficient Visual Analysis with Transformers

1 code implementation ICCV 2021 Tao Wang, Li Yuan, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result.

Object Detection Panoptic Segmentation

Voxel Transformer for 3D Object Detection

1 code implementation ICCV 2021 Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, Chunjing Xu

We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.

 Ranked #1 on 3D Object Detection on waymo vehicle (L1 mAP metric)

3D Object Detection Object Recognition

Triplet Contrastive Learning for Brain Tumor Classification

no code implementations8 Aug 2021 Tian Yu Liu, Jiashi Feng

Brain tumor is a common and fatal form of cancer which affects both adults and children.

Classification Contrastive Learning +2

Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision

1 code implementation20 Jul 2021 Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng

To handle this task, we propose a new method, called Test-time Aggregating Diverse Experts, that presents two solution strategies: (1) a new skill-diverse expert learning strategy that trains diverse experts to excel at handling different class distributions from a single long-tailed training distribution; (2) a novel test-time expert aggregation strategy that leverages self-supervision to aggregate multiple experts for handling various unknown test distributions.

Image Classification Long-tail Learning

Recovering the Unbiased Scene Graphs from the Biased Ones

1 code implementation5 Jul 2021 Meng-Jiun Chiou, Henghui Ding, Hanshu Yan, Changhu Wang, Roger Zimmermann, Jiashi Feng

Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.

Data Augmentation Graph Generation +2

VOLO: Vision Outlooker for Visual Recognition

5 code implementations24 Jun 2021 Li Yuan, Qibin Hou, Zihang Jiang, Jiashi Feng, Shuicheng Yan

Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided.

Image Classification Semantic Segmentation

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition

3 code implementations23 Jun 2021 Qibin Hou, Zihang Jiang, Li Yuan, Ming-Ming Cheng, Shuicheng Yan, Jiashi Feng

By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections.

LV-BERT: Exploiting Layer Variety for BERT

1 code implementation Findings (ACL) 2021 Weihao Yu, Zihang Jiang, Fei Chen, Qibin Hou, Jiashi Feng

In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order.

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

no code implementations NeurIPS 2021 Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, Jiashi Feng

Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model.

Classification Federated Learning

Refiner: Refining Self-attention for Vision Transformers

1 code implementation7 Jun 2021 Daquan Zhou, Yujun Shi, Bingyi Kang, Weihao Yu, Zihang Jiang, Yuan Li, Xiaojie Jin, Qibin Hou, Jiashi Feng

Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.

Image Classification

Image-to-Video Generation via 3D Facial Dynamics

no code implementations31 May 2021 Xiaoguang Tu, Yingtian Zou, Jian Zhao, Wenjie Ai, Jian Dong, Yuan YAO, Zhikang Wang, Guodong Guo, Zhifeng Li, Wei Liu, Jiashi Feng

Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.

Video Generation Video Prediction

PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal

1 code implementation26 May 2021 Si Liu, Wentao Jiang, Chen Gao, Ran He, Jiashi Feng, Bo Li, Shuicheng Yan

In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively.

Style Transfer

Joint Face Image Restoration and Frontalization for Recognition

no code implementations12 May 2021 Xiaoguang Tu, Jian Zhao, Qiankun Liu, Wenjie Ai, Guodong Guo, Zhifeng Li, Wei Liu, Jiashi Feng

First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.

Face Recognition Image Restoration

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation

1 code implementation CVPR 2021 Kehong Gong, Jianfeng Zhang, Jiashi Feng

To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator.

Data Augmentation Monocular 3D Human Pose Estimation +1

Body Meshes as Points

1 code implementation CVPR 2021 Jianfeng Zhang, Dongdong Yu, Jun Hao Liew, Xuecheng Nie, Jiashi Feng

In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.

3D Human Pose Estimation 3D Pose Estimation

How Well Self-Supervised Pre-Training Performs with Streaming Data?

no code implementations25 Apr 2021 Dapeng Hu, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Alfred Shen, Jiashi Feng

Based on our findings, we recommend using sequential self-supervised training as a \textbf{more efficient yet performance-competitive} representation learning practice for real-world applications.

Representation Learning Self-Supervised Learning

DINE: Domain Adaptation from Single and Multiple Black-box Predictors

2 code implementations4 Apr 2021 Jian Liang, Dapeng Hu, Jiashi Feng, Ran He

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target).

Unsupervised Domain Adaptation

Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation

no code implementations30 Mar 2021 Shuning Chang, Pichao Wang, Fan Wang, Hao Li, Jiashi Feng

Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection.

Action Detection Temporal Action Proposal Generation +1

DeepViT: Towards Deeper Vision Transformer

3 code implementations22 Mar 2021 Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, Jiashi Feng

In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper.

Image Classification Representation Learning

AutoSpace: Neural Architecture Search with Less Human Interference

1 code implementation ICCV 2021 Daquan Zhou, Xiaojie Jin, Xiaochen Lian, Linjie Yang, Yujing Xue, Qibin Hou, Jiashi Feng

Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction.

Neural Architecture Search

Coordinate Attention for Efficient Mobile Network Design

1 code implementation CVPR 2021 Qibin Hou, Daquan Zhou, Jiashi Feng

Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e. g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps.

Object Detection Semantic Segmentation

Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

1 code implementation NeurIPS 2021 Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng

In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the contrastive loss benefits both discriminative representation learning and model optimization during fine-tuning.

Contrastive Learning Fine-tuning +4

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

2 code implementations10 Feb 2021 Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama

By comparing \textit{non-robust} (normally trained) and \textit{robustified} (adversarially trained) models, we observe that adversarial training (AT) robustifies CNNs by aligning the channel-wise activations of adversarial data with those of their natural counterparts.

Adversarial Robustness Feature Selection

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

8 code implementations ICCV 2021 Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zihang Jiang, Francis EH Tay, Jiashi Feng, Shuicheng Yan

To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study.

Image Classification Language Modelling +1

ORDNet: Capturing Omni-Range Dependencies for Scene Parsing

no code implementations11 Jan 2021 Shaofei Huang, Si Liu, Tianrui Hui, Jizhong Han, Bo Li, Jiashi Feng, Shuicheng Yan

Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images.

Scene Parsing

AggMask: Exploring locally aggregated learning of mask representations for instance segmentation

1 code implementation1 Jan 2021 Tao Wang, Jun Hao Liew, Yu Li, Yunpeng Chen, Jiashi Feng

Recently proposed one-stage instance segmentation models (\emph{e. g.}, SOLO) learn to directly predict location-specific object mask with fully-convolutional networks.

Instance Segmentation Semantic Segmentation

Exploring Balanced Feature Spaces for Representation Learning

no code implementations ICLR 2021 Bingyi Kang, Yu Li, Sa Xie, Zehuan Yuan, Jiashi Feng

Motivated by this question, we conduct a series of studies on the performance of self-supervised contrastive learning and supervised learning methods over multiple datasets where training instance distributions vary from a balanced one to a long-tailed one.

Contrastive Learning Long-tail Learning +2

Learning Safe Policies with Cost-sensitive Advantage Estimation

no code implementations1 Jan 2021 Bingyi Kang, Shie Mannor, Jiashi Feng

Reinforcement Learning (RL) with safety guarantee is critical for agents performing tasks in risky environments.

DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning

no code implementations1 Jan 2021 Kaichen Zhou, Lanqing Hong, Fengwei Zhou, Binxin Ru, Zhenguo Li, Trigoni Niki, Jiashi Feng

Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.

Data Augmentation Neural Architecture Search

Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer

1 code implementation14 Dec 2020 Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, Jiashi Feng

Furthermore, we propose a new labeling transfer strategy, which separates the target data into two splits based on the confidence of predictions (labeling information), and then employ semi-supervised learning to improve the accuracy of less-confident predictions in the target domain.

Classification General Classification +3

Adversarial images for the primate brain

no code implementations11 Nov 2020 Li Yuan, Will Xiao, Gabriel Kreiman, Francis E. H. Tay, Jiashi Feng, Margaret S. Livingstone

Next, we modified images, such as human faces, to match their model-predicted neuronal responses to a target category, such as monkey faces.

Improving Generalization in Reinforcement Learning with Mixture Regularization

1 code implementation NeurIPS 2020 Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng

Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments.

Data Augmentation

Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Yichen Zhou, Shuning Chang, Ziyuan Huang, Yunpeng Chen, Xuecheng Nie, Tao Wang, Jiashi Feng, Shuicheng Yan

Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.

Action Recognition Action Recognition In Videos +2

A Simple Baseline for Pose Tracking in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Shuning Chang, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Xuecheng Nie, Francis E. H. Tay, Jiashi Feng, Shuicheng Yan

This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.

Multi-Object Tracking Optical Flow Estimation +1

Towards Accurate Human Pose Estimation in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Shuning Chang, Xuecheng Nie, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.

Optical Flow Estimation Pose Estimation

Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning

no code implementations NeurIPS 2020 Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Hoi, Weinan E

The result shows that (1) the escaping time of both SGD and ADAM~depends on the Radon measure of the basin positively and the heaviness of gradient noise negatively; (2) for the same basin, SGD enjoys smaller escaping time than ADAM, mainly because (a) the geometry adaptation in ADAM~via adaptively scaling each gradient coordinate well diminishes the anisotropic structure in gradient noise and results in larger Radon measure of a basin; (b) the exponential gradient average in ADAM~smooths its gradient and leads to lighter gradient noise tails than SGD.

Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations

1 code implementation10 Sep 2020 Meng-Jiun Chiou, Roger Zimmermann, Jiashi Feng

Visual relationship detection aims to reason over relationships among salient objects in images, which has drawn increasing attention over the past few years.

Object Detection Relational Reasoning +1

Dual Adversarial Auto-Encoders for Clustering

no code implementations23 Aug 2020 Pengfei Ge, Chuan-Xian Ren, Jiashi Feng, Shuicheng Yan

By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.

Variational Inference

Few-shot Classification via Adaptive Attention

1 code implementation6 Aug 2020 Zi-Hang Jiang, Bingyi Kang, Kuangqi Zhou, Jiashi Feng

To be specific, we devise a simple and efficient meta-reweighting strategy to adapt the sample representations and generate soft attention to refine the representation such that the relevant features from the query and support samples can be extracted for a better few-shot classification.

Classification Few-Shot Learning +1

ConvBERT: Improving BERT with Span-based Dynamic Convolution

7 code implementations NeurIPS 2020 Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning.

Language understanding Natural Language Understanding

The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation

1 code implementation ECCV 2020 Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Junhao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate classification of object proposals.

Classification General Classification +3

Domain Adaptation with Auxiliary Target Domain-Oriented Classifier

1 code implementation CVPR 2021 Jian Liang, Dapeng Hu, Jiashi Feng

ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels.

Domain Adaptation Transfer Learning

Rethinking Bottleneck Structure for Efficient Mobile Network Design

4 code implementations ECCV 2020 Zhou Daquan, Qibin Hou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

In this paper, we rethink the necessity of such design changes and find it may bring risks of information loss and gradient confusion.

Classification General Classification +2

Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation

no code implementations NeurIPS 2020 Jianfeng Zhang, Xuecheng Nie, Jiashi Feng

In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.

3D Human Pose Estimation Self-Supervised Learning

Local Grid Rendering Networks for 3D Object Detection in Point Clouds

no code implementations4 Jul 2020 Jianan Li, Jiashi Feng

The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns.

3D Object Detection

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

1 code implementation CVPR 2020 Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng

Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution.

Image Classification Instance Segmentation +3

Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation

no code implementations14 Jun 2020 Kuangqi Zhou, Qibin Hou, Zun Li, Jiashi Feng

In this paper, we propose a novel multi-miner framework to perform a region mining process that adapts to diverse object sizes and is thus able to mine more integral and finer object regions.

Weakly-Supervised Semantic Segmentation

Understanding and Resolving Performance Degradation in Graph Convolutional Networks

1 code implementation12 Jun 2020 Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng

In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works.

Boosting Few-Shot Learning With Adaptive Margin Loss

no code implementations CVPR 2020 Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, Li-Wei Wang

Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples.

Few-Shot Image Classification Semantic Similarity +1

Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation

1 code implementation30 Mar 2020 Dapeng Hu, Jian Liang, Qibin Hou, Hanshu Yan, Yunpeng Chen, Shuicheng Yan, Jiashi Feng

To successfully align the multi-modal data structures across domains, the following works exploit discriminative information in the adversarial training process, e. g., using multiple class-wise discriminators and introducing conditional information in input or output of the domain discriminator.

Object Recognition Semantic Segmentation +1

Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

2 code implementations CVPR 2020 Qibin Hou, Li Zhang, Ming-Ming Cheng, Jiashi Feng

Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing.

Scene Parsing Semantic Segmentation

Cross-layer Feature Pyramid Network for Salient Object Detection

no code implementations25 Feb 2020 Zun Li, Congyan Lang, Junhao Liew, Qibin Hou, Yidong Li, Jiashi Feng

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection.

RGB Salient Object Detection Salient Object Detection

ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning

1 code implementation ICLR 2020 Weihao Yu, Zi-Hang Jiang, Yanfei Dong, Jiashi Feng

Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set.

Logical Reasoning Question Answering Logical Reasoning Reading Comprehension +1

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

No single submission was best at predicting all three outcomes.

alzheimer's disease detection Disease Prediction

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

no code implementations22 Jan 2020 Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.

Meta-Learning Model Selection +1

RC-DARTS: Resource Constrained Differentiable Architecture Search

no code implementations30 Dec 2019 Xiaojie Jin, Jiang Wang, Joshua Slocum, Ming-Hsuan Yang, Shengyang Dai, Shuicheng Yan, Jiashi Feng

In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy.

Image Classification One-Shot Learning

Zoom in to where it matters: a hierarchical graph based model for mammogram analysis

no code implementations16 Dec 2019 Hao Du, Jiashi Feng, Mengling Feng

In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.

Classification General Classification +3

Efficient Meta Learning via Minibatch Proximal Update

no code implementations NeurIPS 2019 Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng

We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks.

Few-Shot Learning Few-shot Regression

Classification Calibration for Long-tail Instance Segmentation

1 code implementation29 Oct 2019 Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Jun Hao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.

Classification General Classification +2

On Robustness of Neural Ordinary Differential Equations

2 code implementations ICLR 2020 Hanshu Yan, Jiawei Du, Vincent Y. F. Tan, Jiashi Feng

We show that the TisODE method outperforms vanilla neural ODEs and also can work in conjunction with other state-of-the-art architectural methods to build more robust deep networks.

Adversarial Attack

Compressed Video Action Recognition with Refined Motion Vector

no code implementations6 Oct 2019 Haoyuan Cao, Shining Yu, Jiashi Feng

Although CNN has reached satisfactory performance in image-related tasks, using CNN to process videos is much more challenging due to the enormous size of raw video streams.

Action Recognition Optical Flow Estimation +1

Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning

1 code implementation27 Sep 2019 Mingjie Sun, Jimin Xiao, Eng Gee Lim, Yanchu Xie, Jiashi Feng

In this paper, we aim to tackle the task of semi-supervised video object segmentation across a sequence of frames where only the ground-truth segmentation of the first frame is provided.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

Hierarchical Neural Architecture Search via Operator Clustering

1 code implementation26 Sep 2019 Guilin Li, Xing Zhang, Zitong Wang, Matthias Tan, Jiashi Feng, Zhenguo Li, Tong Zhang

Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.

Neural Architecture Search

Prototype Recalls for Continual Learning

no code implementations25 Sep 2019 Mengmi Zhang, Tao Wang, Joo Hwee Lim, Jiashi Feng

Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.

Continual Learning Metric Learning


no code implementations25 Sep 2019 Dapeng Hu, Jian Liang*, Qibin Hou, Hanshu Yan, Jiashi Feng

Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribution embedded in probabilistic predictions, hence bringing insufficient alleviation to the latent mismatch problem.

Object Recognition Semantic Segmentation +1

Revisiting Knowledge Distillation via Label Smoothing Regularization

2 code implementations CVPR 2020 Li Yuan, Francis E. H. Tay, Guilin Li, Tao Wang, Jiashi Feng

Without any extra computation cost, Tf-KD achieves up to 0. 65\% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization.

Self-Knowledge Distillation

Towards Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations25 Sep 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.

Adversarial Robustness

PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer

1 code implementation CVPR 2020 Wentao Jiang, Si Liu, Chen Gao, Jie Cao, Ran He, Jiashi Feng, Shuicheng Yan

In this paper, we address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image.

Hierarchic Neighbors Embedding

no code implementations16 Sep 2019 Shenglan Liu, Yang Yu, Yang Liu, Hong Qiao, Lin Feng, Jiashi Feng

Manifold learning now plays a very important role in machine learning and many relevant applications.

Single-Stage Multi-Person Pose Machines

1 code implementation ICCV 2019 Xuecheng Nie, Jianfeng Zhang, Shuicheng Yan, Jiashi Feng

Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods.

3D Pose Estimation Keypoint Detection +1

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

no code implementations ICCV 2019 Xuecheng Nie, Yuncheng Li, Linjie Luo, Ning Zhang, Jiashi Feng

Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications.

Pose Estimation

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

4 code implementations ICCV 2019 Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, Jiashi Feng

In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.

Few-Shot Semantic Segmentation Metric Learning +1

Central Similarity Quantization for Efficient Image and Video Retrieval

1 code implementation CVPR 2020 Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng

In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy.

Quantization Video Retrieval

Neural Epitome Search for Architecture-Agnostic Network Compression

no code implementations ICLR 2020 Daquan Zhou, Xiaojie Jin, Qibin Hou, Kaixin Wang, Jianchao Yang, Jiashi Feng

The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs).

Model Compression Neural Architecture Search

Delving into 3D Action Anticipation from Streaming Videos

no code implementations15 Jun 2019 Hongsong Wang, Jiashi Feng

Action anticipation, which aims to recognize the action with a partial observation, becomes increasingly popular due to a wide range of applications.

Action Anticipation Action Classification +1

PVRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction

1 code implementation15 Jun 2019 Hongsong Wang, Jian Dong, Bin Cheng, Jiashi Feng

We therefore propose a novel Position-Velocity Recurrent Encoder-Decoder (PVRED) for human motion prediction, which makes full use of pose velocities and temporal positional information.

Human motion prediction motion prediction

Distilling Object Detectors with Fine-grained Feature Imitation

3 code implementations CVPR 2019 Tao Wang, Li Yuan, Xiaopeng Zhang, Jiashi Feng

To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response.

Knowledge Distillation Object Detection

Query-efficient Meta Attack to Deep Neural Networks

1 code implementation ICLR 2020 Jiawei Du, Hu Zhang, Joey Tianyi Zhou, Yi Yang, Jiashi Feng

Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries.

Adversarial Attack Meta-Learning

Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations6 Jun 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.

Adversarial Robustness

Panoptic Edge Detection

no code implementations3 Jun 2019 Yuan Hu, Yingtian Zou, Jiashi Feng

In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.

Edge Detection Object Detection +1

Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation

2 code implementations26 May 2019 Hanyang Kong, Jian Zhao, Xiaoguang Tu, Junliang Xing, ShengMei Shen, Jiashi Feng

Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.

Face Hallucination Face Recognition +2

Variational Prototype Replays for Continual Learning

1 code implementation23 May 2019 Mengmi Zhang, Tao Wang, Joo Hwee Lim, Gabriel Kreiman, Jiashi Feng

In each classification task, our method learns a set of variational prototypes with their means and variances, where embedding of the samples from the same class can be represented in a prototypical distribution and class-representative prototypes are separated apart.

Continual Learning General Classification +1

A Simple Pooling-Based Design for Real-Time Salient Object Detection

5 code implementations CVPR 2019 Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, Jianmin Jiang

We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway.

RGB Salient Object Detection Salient Object Detection

Hierarchical Meta Learning

no code implementations19 Apr 2019 Yingtian Zou, Jiashi Feng

Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.

Few-Shot Learning Fine-tuning

Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization

no code implementations17 Apr 2019 Li Yuan, Francis EH Tay, Ping Li, Li Zhou, Jiashi Feng

The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.

Unsupervised Video Summarization

Few-shot Adaptive Faster R-CNN

no code implementations CVPR 2019 Tao Wang, Xiaopeng Zhang, Li Yuan, Jiashi Feng

To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples.

Object Detection Unsupervised Domain Adaptation

Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search

2 code implementations CVPR 2019 Xin Li, Yiming Zhou, Zheng Pan, Jiashi Feng

It prunes the architecture search space with a partial order assumption to automatically search for the architectures with the best speed and accuracy trade-off.

Neural Architecture Search

Dynamic Feature Fusion for Semantic Edge Detection

1 code implementation25 Feb 2019 Yuan Hu, Yunpeng Chen, Xiang Li, Jiashi Feng

In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively.

Edge Detection

Multi-Prototype Networks for Unconstrained Set-based Face Recognition

no code implementations13 Feb 2019 Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng

In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.

Face Recognition

Deep Reasoning with Multi-Scale Context for Salient Object Detection

no code implementations24 Jan 2019 Zun Li, Congyan Lang, Yunpeng Chen, Junhao Liew, Jiashi Feng

However, the saliency inference module that performs saliency prediction from the fused features receives much less attention on its architecture design and typically adopts only a few fully convolutional layers.

RGB Salient Object Detection Saliency Prediction +1

Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

1 code implementation17 Jan 2019 Xiaoguang Tu, Jian Zhao, Mei Xie, Guodong Du, Hengsheng Zhang, Jianshu Li, Zheng Ma, Jiashi Feng

Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.

Domain Adaptation Face Anti-Spoofing +1

Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

no code implementations7 Jan 2019 Guohao Ying, Yingtian Zou, Lin Wan, Yiming Hu, Jiashi Feng

In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.

Video Prediction

Similarity R-C3D for Few-shot Temporal Activity Detection

no code implementations25 Dec 2018 Huijuan Xu, Bingyi Kang, Ximeng Sun, Jiashi Feng, Kate Saenko, Trevor Darrell

In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video.

Action Detection Activity Detection

Few-shot Object Detection via Feature Reweighting

4 code implementations ICCV 2019 Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell

The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.

Few-Shot Learning Few-Shot Object Detection +1

New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity

no code implementations NeurIPS 2018 Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

In this paper, we affirmatively answer this open question by showing that under WoRS and for both convex and non-convex problems, it is still possible for HSGD (with constant step-size) to match full gradient descent in rate of convergence, while maintaining comparable sample-size-independent incremental first-order oracle complexity to stochastic gradient descent.

Efficient Stochastic Gradient Hard Thresholding

no code implementations NeurIPS 2018 Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

To address these deficiencies, we propose an efficient hybrid stochastic gradient hard thresholding (HSG-HT) method that can be provably shown to have sample-size-independent gradient evaluation and hard thresholding complexity bounds.

Graph-Based Global Reasoning Networks

4 code implementations CVPR 2019 Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis

In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed.

Action Classification Action Recognition +3

Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space

no code implementations27 Sep 2018 Bin Zhou, Jiashi Feng

Current deep neuroevolution models are usually trained in a large parameter search space for complex learning tasks, e. g. playing video games, which needs billions of samples and thousands of search steps to obtain significant performance.

Atari Games

Dynamic Conditional Networks for Few-Shot Learning

no code implementations ECCV 2018 Fang Zhao, Jian Zhao, Shuicheng Yan, Jiashi Feng

This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i. e, only a few training samples are available for each condition.

Face Generation Few-Shot Learning +3

Pose Partition Networks for Multi-Person Pose Estimation

no code implementations ECCV 2018 Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan

This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.

Human Detection Multi-Person Pose Estimation

Egocentric Spatial Memory

1 code implementation31 Jul 2018 Mengmi Zhang, Keng Teck Ma, Shih-Cheng Yen, Joo Hwee Lim, Qi Zhao, Jiashi Feng

Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective.

Feature Engineering

Multi-Fiber Networks for Video Recognition

no code implementations ECCV 2018 Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng

In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks.

Ranked #30 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition +1

Object Relation Detection Based on One-shot Learning

no code implementations16 Jul 2018 Li Zhou, Jian Zhao, Jianshu Li, Li Yuan, Jiashi Feng

Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.

One-Shot Learning

TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

no code implementations ECCV 2018 Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, JinJun Xiong, Jiashi Feng, Thomas Huang

This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).

Multiple Instance Learning Weakly Supervised Object Detection +1

Policy Optimization with Demonstrations

no code implementations ICML 2018 Bingyi Kang, Zequn Jie, Jiashi Feng

Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse.

Policy Gradient Methods

Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements

1 code implementation7 Jun 2018 Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan

Specifically, we show that by solving a TNN minimization problem, the underlying tensor of size $n_1\times n_2\times n_3$ with tubal rank $r$ can be exactly recovered when the given number of Gaussian measurements is $O(r(n_1+n_2-r)n_3)$.

MoNet: Deep Motion Exploitation for Video Object Segmentation

no code implementations CVPR 2018 Huaxin Xiao, Jiashi Feng, Guosheng Lin, Yu Liu, Maojun Zhang

In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i. e., frame representation learning and segmentation refinement.

Optical Flow Estimation Representation Learning +3

Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network

no code implementations CVPR 2018 Fang Zhao, Jianshu Li, Jian Zhao, Jiashi Feng

In this paper, we propose a novel weakly supervised model, Multi-scale Anchored Transformer Network (MATN), to accurately localize free-form textual phrases with only image-level supervision.

Region Proposal

Deep Adversarial Subspace Clustering

no code implementations CVPR 2018 Pan Zhou, Yunqing Hou, Jiashi Feng

To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering.

Image Clustering Representation Learning

Human Pose Estimation With Parsing Induced Learner

no code implementations CVPR 2018 Xuecheng Nie, Jiashi Feng, Yiming Zuo, Shuicheng Yan

Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art.

Human Parsing Multi-Person Pose Estimation

Understanding Generalization and Optimization Performance of Deep CNNs

no code implementations ICML 2018 Pan Zhou, Jiashi Feng

Besides, we prove that for an arbitrary gradient descent algorithm, the computed approximate stationary point by minimizing empirical risk is also an approximate stationary point to the population risk.

Subspace Clustering by Block Diagonal Representation

no code implementations23 May 2018 Canyi Lu, Jiashi Feng, Zhouchen Lin, Tao Mei, Shuicheng Yan

Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e. g., sparsity and low-rankness, which are indirect.

Learning Markov Clustering Networks for Scene Text Detection

no code implementations CVPR 2018 Zichuan Liu, Guosheng Lin, Sheng Yang, Jiashi Feng, Weisi Lin, Wang Ling Goh

MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.

Scene Text Scene Text Detection

Learning Pixel-wise Labeling from the Internet without Human Interaction

no code implementations19 May 2018 Yun Liu, Yujun Shi, Jia-Wang Bian, Le Zhang, Ming-Ming Cheng, Jiashi Feng

Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.

Fine-tuning Semantic Segmentation

Zigzag Learning for Weakly Supervised Object Detection

no code implementations CVPR 2018 Xiaopeng Zhang, Jiashi Feng, Hongkai Xiong, Qi Tian

Unlike them, we propose a zigzag learning strategy to simultaneously discover reliable object instances and prevent the model from overfitting initial seeds.

Weakly Supervised Object Detection

Adversarial Complementary Learning for Weakly Supervised Object Localization

2 code implementations CVPR 2018 Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, Thomas Huang

With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.

General Classification Weakly-Supervised Object Localization

Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm

1 code implementation10 Apr 2018 Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan

Equipped with the new tensor nuclear norm, we then solve the TRPCA problem by solving a convex program and provide the theoretical guarantee for the exact recovery.

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

2 code implementations10 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.

Autonomous Driving Instance Segmentation +4

Left-Right Comparative Recurrent Model for Stereo Matching

no code implementations CVPR 2018 Zequn Jie, Pengfei Wang, Yonggen Ling, Bo Zhao, Yunchao Wei, Jiashi Feng, Wei Liu

Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.

Disparity Estimation Stereo Disparity Estimation +2

Cross-domain Human Parsing via Adversarial Feature and Label Adaptation

no code implementations4 Jan 2018 Si Liu, Yao Sun, Defa Zhu, Guanghui Ren, Yu Chen, Jiashi Feng, Jizhong Han

Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences.

Human Parsing

Interpreting Deep Classification Models With Bayesian Inference

no code implementations ICLR 2018 Hanshu Yan, Jiashi Feng

The results demonstrate that the proposed interpreter successfully finds the core hidden units most responsible for prediction making.

Bayesian Inference Classification +1

Egocentric Spatial Memory Network

no code implementations ICLR 2018 Mengmi Zhang, Keng Teck Ma, Joo Hwee Lim, Shih-Cheng Yen, Qi Zhao, Jiashi Feng

During the exploration, our proposed ESM network model updates belief of the global map based on local observations using a recurrent neural network.

Simultaneous Localization and Mapping

Empirical Risk Landscape Analysis for Understanding Deep Neural Networks

no code implementations ICLR 2018 Pan Zhou, Jiashi Feng

This work aims to provide comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance.

Generalization Bounds