Search Results for author: Chunhua Shen

Found 295 papers, 106 papers with code

Instance-Aware Embedding for Point Cloud Instance Segmentation

no code implementations ECCV 2020 Tong He, Yifan Liu, Chunhua Shen, Xinlong Wang, Changming Sun

However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects.

Instance Segmentation Semantic Segmentation

Poseur: Direct Human Pose Regression with Transformers

no code implementations19 Jan 2022 Weian Mao, Yongtao Ge, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton Van Den Hengel

We propose a direct, regression-based approach to 2D human pose estimation from single images.

Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

no code implementations18 Jan 2022 Yutong Dai, Brian Price, He Zhang, Chunhua Shen

Deep image matting methods have achieved increasingly better results on benchmarks (e. g., Composition-1k/alphamatting. com).

Data Augmentation Image Matting +1

SPTS: Single-Point Text Spotting

no code implementations15 Dec 2021 Dezhi Peng, Xinyu Wang, Yuliang Liu, Jiaxin Zhang, Mingxin Huang, Songxuan Lai, Shenggao Zhu, Jing Li, Dahua Lin, Chunhua Shen, Lianwen Jin

Most significantly, we show that the performance is not very sensitive to the positions of the point annotation, meaning that it can be much easier to be annotated and automatically generated than the bounding box that requires precise positions.

Language Modelling Text Spotting

NAS-FCOS: Efficient Search for Object Detection Architectures

1 code implementation24 Oct 2021 Ning Wang, Yang Gao, Hao Chen, Peng Wang, Zhi Tian, Chunhua Shen, Yanning Zhang

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures.

Neural Architecture Search Object Detection

Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning

no code implementations ICCV 2021 Chi Zhang, Henghui Ding, Guosheng Lin, Ruibo Li, Changhu Wang, Chunhua Shen

Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.

AutoML Few-Shot Learning

Explainable Deep Few-shot Anomaly Detection with Deviation Networks

1 code implementation1 Aug 2021 Guansong Pang, Choubo Ding, Chunhua Shen, Anton Van Den Hengel

Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models.

Few Shot Anomaly Detection Multiple Instance Learning

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

no code implementations NeurIPS 2021 BoWen Zhang, Yifan Liu, Zhi Tian, Chunhua Shen

This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient.

Semantic Segmentation

Dynamic Convolution for 3D Point Cloud Instance Segmentation

no code implementations18 Jul 2021 Tong He, Chunhua Shen, Anton Van Den Hengel

The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.

Instance Segmentation Semantic Segmentation

SOLO: A Simple Framework for Instance Segmentation

no code implementations30 Jun 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation.

Image Matting Instance Segmentation +3

Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data

no code implementations CVPR 2021 Ying Shu, Yan Yan, Si Chen, Jing-Hao Xue, Chunhua Shen, Hanzi Wang

First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data.

Self-Supervised Learning

FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions

3 code implementations CVPR 2021 Weian Mao, Zhi Tian, Xinlong Wang, Chunhua Shen

We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose.

Multi-Person Pose Estimation

Unsupervised Scale-consistent Depth Learning from Video

2 code implementations25 May 2021 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.

Monocular Depth Estimation Monocular Visual Odometry +1

ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting

1 code implementation8 May 2021 Yuliang Liu, Chunhua Shen, Lianwen Jin, Tong He, Peng Chen, Chongyu Liu, Hao Chen

Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output.

Text Spotting

PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text

1 code implementation2 May 2021 Wenhai Wang, Enze Xie, Xiang Li, Xuebo Liu, Ding Liang, Zhibo Yang, Tong Lu, Chunhua Shen

By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text.

Scene Text Detection Text Spotting

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

5 code implementations NeurIPS 2021 Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen

Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks.

Image Classification Semantic Segmentation

Kernel Agnostic Real-world Image Super-resolution

no code implementations19 Apr 2021 Hu Wang, Congbo Ma, Chunhua Shen

In the proposed framework, the degradation kernels and noises are adaptively modeled rather than explicitly specified.

Image Super-Resolution

A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

1 code implementation ICCV 2021 Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang, Hao Li

Previous works [3, 27] fail to employ strong augmentation in pseudo label learning efficiently, as the large distribution change caused by strong augmentation harms the batch normalisation statistics.

Data Augmentation Image Classification +1

Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

no code implementations CVPR 2021 Delian Ruan, Yan Yan, Shenqi Lai, Zhenhua Chai, Chunhua Shen, Hanzi Wang

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition.

Facial Expression Recognition

An Adversarial Human Pose Estimation Network Injected with Graph Structure

no code implementations29 Mar 2021 Lei Tian, Guoqiang Liang, Peng Wang, Chunhua Shen

Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods.

Pose Estimation Pose Prediction

TFPose: Direct Human Pose Estimation with Transformers

no code implementations29 Mar 2021 Weian Mao, Yongtao Ge, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang

We propose a human pose estimation framework that solves the task in the regression-based fashion.

Pose Estimation

Generic Perceptual Loss for Modeling Structured Output Dependencies

no code implementations CVPR 2021 Yifan Liu, Hao Chen, Yu Chen, Wei Yin, Chunhua Shen

We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.

Depth Estimation Image Generation +4

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

1 code implementation8 Mar 2021 Lingtong Kong, Chunhua Shen, Jie Yang

Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only 1/10 computation of comparable networks to achieve on par accuracy.

Optical Flow Estimation

Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction

1 code implementation7 Mar 2021 Wei Yin, Yifan Liu, Chunhua Shen

In this work, we show the importance of the high-order 3D geometric constraints for depth prediction.

Monocular Depth Estimation

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

1 code implementation4 Mar 2021 Yutong Xie, Jianpeng Zhang, Chunhua Shen, Yong Xia

Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.

Medical Image Segmentation

Conditional Positional Encodings for Vision Transformers

1 code implementation22 Feb 2021 Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, Xiaolin Wei, Huaxia Xia, Chunhua Shen

Benefit from the conditional positional encoding scheme, we obtain state-of-the-art results on the ImageNet classification task compared with vision Transformers to date.

AutoML General Classification +2

Instance and Panoptic Segmentation Using Conditional Convolutions

no code implementations5 Feb 2021 Zhi Tian, BoWen Zhang, Hao Chen, Chunhua Shen

In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance.

Instance Segmentation Panoptic Segmentation

Object Detection Made Simpler by Eliminating Heuristic NMS

no code implementations28 Jan 2021 Qiang Zhou, Chaohui Yu, Chunhua Shen, Zhibin Wang, Hao Li

On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors.

Object Detection

Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline

no code implementations24 Jan 2021 Hu Wang, Hao Chen, Qi Wu, Congbo Ma, Yidong Li, Chunhua Shen

To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios.

LBS: Loss-aware Bit Sharing for Automatic Model Compression

no code implementations13 Jan 2021 Jing Liu, Bohan Zhuang, Peng Chen, Yong Guo, Chunhua Shen, Jianfei Cai, Mingkui Tan

Low-bitwidth model compression is an effective method to reduce the model size and computational overhead.

Model Compression Quantization

Occluded Person Re-Identification With Single-Scale Global Representations

no code implementations ICCV 2021 Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen

However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians.

Graph Matching Person Re-Identification +1

BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification

no code implementations ICCV 2021 Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li

In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i. e., an elevated view of an object from above.

Person Re-Identification

Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

1 code implementation24 Dec 2020 Haokui Zhang, Ying Li, Hao Chen, Chengrong Gong, Zongwen Bai, Chunhua Shen

For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance.

Image Denoising Image Restoration +2

Learning to Recover 3D Scene Shape from a Single Image

1 code implementation CVPR 2021 Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, Chunhua Shen

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.

Ranked #2 on Indoor Monocular Depth Estimation on DIODE (using extra training data)

3D Scene Reconstruction Indoor Monocular Depth Estimation +2

Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

1 code implementation7 Dec 2020 Haokui Zhang, Ying Li, Yenan Jiang, Peng Wang, Qiang Shen, Chunhua Shen

In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets.

General Classification Transfer Learning

End-to-End Video Instance Segmentation with Transformers

2 code implementations CVPR 2021 Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, Huaxia Xia

Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem.

Instance Segmentation Semantic Segmentation +2

Fully Quantized Image Super-Resolution Networks

1 code implementation29 Nov 2020 Hu Wang, Peng Chen, Bohan Zhuang, Chunhua Shen

With the rising popularity of intelligent mobile devices, it is of great practical significance to develop accurate, realtime and energy-efficient image Super-Resolution (SR) inference methods.

Image Super-Resolution Quantization

Learning Affinity-Aware Upsampling for Deep Image Matting

1 code implementation CVPR 2021 Yutong Dai, Hao Lu, Chunhua Shen

By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affinity-Aware Upsampling (A2U) where upsampling kernels are generated using a light-weight lowrank bilinear model and are conditioned on second-order features.

Image Matting Image Reconstruction +1

Channel-wise Knowledge Distillation for Dense Prediction

2 code implementations ICCV 2021 Changyong Shu, Yifan Liu, Jianfei Gao, Zheng Yan, Chunhua Shen

Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks.

Knowledge Distillation Semantic Segmentation

DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution

1 code implementation CVPR 2021 Tong He, Chunhua Shen, Anton Van Den Hengel

Previous top-performing approaches for point cloud instance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions.

Instance Segmentation Semantic Segmentation

PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation

no code implementations25 Nov 2020 Yutong Xie, Jianpeng Zhang, Zehui Liao, Yong Xia, Chunhua Shen

In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.

Medical Image Segmentation Self-Supervised Learning

Graph Attention Tracking

no code implementations CVPR 2021 Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen

We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.

Graph Attention Object Tracking +1

Robust Data Hiding Using Inverse Gradient Attention

no code implementations21 Nov 2020 Honglei Zhang, Hu Wang, Yuanzhouhan Cao, Chunhua Shen, Yidong Li

The neglect of considering the sensitivity of each pixel will inevitably affect the model robustness for information hiding.

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

1 code implementation CVPR 2021 Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen

To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.

Medical Image Segmentation Tumor Segmentation

Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions

no code implementations19 Nov 2020 Hao Chen, Chunhua Shen, Zhi Tian

To our knowledge, DR1Mask is the first panoptic segmentation framework that exploits a shared feature map for both instance and semantic segmentation by considering both efficacy and efficiency.

Instance Segmentation Multi-Task Learning +1

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

3 code implementations CVPR 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin.

Contrastive Learning Image Classification +4

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

1 code implementation15 Sep 2020 Guansong Pang, Anton Van Den Hengel, Chunhua Shen, Longbing Cao

We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset.

Anomaly Detection

FATNN: Fast and Accurate Ternary Neural Networks

no code implementations ICCV 2021 Peng Chen, Bohan Zhuang, Chunhua Shen

Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts.

Image Classification Quantization

Representative Graph Neural Network

no code implementations ECCV 2020 Changqian Yu, Yifan Liu, Changxin Gao, Chunhua Shen, Nong Sang

In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.

Object Detection Semantic Segmentation

Pairwise Relation Learning for Semi-supervised Gland Segmentation

no code implementations6 Aug 2020 Yutong Xie, Jianpeng Zhang, Zhibin Liao, Chunhua Shen, Johan Verjans, Yong Xia

In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.

AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

2 code implementations ECCV 2020 Wenhai Wang, Xuebo Liu, Xiaozhong Ji, Enze Xie, Ding Liang, Zhibo Yang, Tong Lu, Chunhua Shen, Ping Luo

Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection.

Language Modelling Text Spotting

Improving Generative Adversarial Networks with Local Coordinate Coding

1 code implementation28 Jul 2020 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.

Soft Expert Reward Learning for Vision-and-Language Navigation

no code implementations ECCV 2020 Hu Wang, Qi Wu, Chunhua Shen

In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task.

Vision and Language Navigation

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

1 code implementation ECCV 2020 Liang Liu, Hao Lu, Hongwei Zou, Haipeng Xiong, Zhiguo Cao, Chunhua Shen

Inspired by scale weighing, we propose a novel 'counting scale' termed LibraNet where the count value is analogized by weight.

Crowd Counting

AQD: Towards Accurate Quantized Object Detection

no code implementations CVPR 2021 Peng Chen, Jing Liu, Bohan Zhuang, Mingkui Tan, Chunhua Shen

Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices.

Image Classification Object Detection +1

Deep Learning for Anomaly Detection: A Review

no code implementations6 Jul 2020 Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel

This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods.

Anomaly Detection Outlier Detection

FCOS: A simple and strong anchor-free object detector

1 code implementation14 Jun 2020 Zhi Tian, Chunhua Shen, Hao Chen, Tong He

In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications.

Object Detection Semantic Segmentation

A Robust Attentional Framework for License Plate Recognition in the Wild

no code implementations6 Jun 2020 Linjiang Zhang, Peng Wang, Hui Li, Zhen Li, Chunhua Shen, Yanning Zhang

On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly.

Image Generation License Plate Recognition

Auto-Rectify Network for Unsupervised Indoor Depth Estimation

1 code implementation4 Jun 2020 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, Ian Reid

However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices.

Monocular Depth Estimation Rectification +2

Scope Head for Accurate Localization in Object Detection

no code implementations11 May 2020 Geng Zhan, Dan Xu, Guo Lu, Wei Wu, Chunhua Shen, Wanli Ouyang

Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance.

Object Detection

BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

4 code implementations5 Apr 2020 Changqian Yu, Changxin Gao, Jingbo Wang, Gang Yu, Chunhua Shen, Nong Sang

We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation.

Real-Time Semantic Segmentation

Context Prior for Scene Segmentation

2 code implementations CVPR 2020 Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, Nong Sang

Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior.

Scene Segmentation Scene Understanding

Segmenting Transparent Objects in the Wild

1 code implementation ECCV 2020 Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo

To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.

Semantic Segmentation Transparent objects

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

1 code implementation27 Mar 2020 Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia

In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.

Anomaly Detection General Classification

SOLOv2: Dynamic and Fast Instance Segmentation

14 code implementations NeurIPS 2020 Xinlong Wang, Rufeng Zhang, Tao Kong, Lei LI, Chunhua Shen

Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.

Instance Segmentation Object Detection +1

Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

no code implementations CVPR 2020 Guansong Pang, Cheng Yan, Chunhua Shen, Anton Van Den Hengel, Xiao Bai

Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video.

Anomaly Detection Representation Learning

DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

3 code implementations15 Mar 2020 Chi Zhang, Yujun Cai, Guosheng Lin, Chunhua Shen

We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.

Few-Shot Image Classification General Classification

Conditional Convolutions for Instance Segmentation

7 code implementations ECCV 2020 Zhi Tian, Chunhua Shen, Hao Chen

We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation).

Instance Segmentation Semantic Segmentation

Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes

no code implementations11 Mar 2020 Genshun Dong, Yan Yan, Chunhua Shen, Hanzi Wang

Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutional layers is designed to generate high-resolution feature maps preserving the detailed spatial information.

Semantic Segmentation

Efficient Semantic Video Segmentation with Per-frame Inference

1 code implementation ECCV 2020 Yifan Liu, Chunhua Shen, Changqian Yu, Jingdong Wang

For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.

Knowledge Distillation Optical Flow Estimation +3

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

7 code implementations CVPR 2020 Yuliang Liu, Hao Chen, Chunhua Shen, Tong He, Lianwen Jin, Liangwei Wang

Our contributions are three-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve.

Scene Text Detection Text Spotting

Joint Deep Learning of Facial Expression Synthesis and Recognition

no code implementations6 Feb 2020 Yan Yan, Ying Huang, Si Chen, Chunhua Shen, Hanzi Wang

Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions.

Facial Expression Recognition

DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

4 code implementations3 Feb 2020 Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, Dou Renyin

Compared with previous learning objectives, i. e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes.

Affine Transformation Curriculum Learning +1

Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory

no code implementations13 Jan 2020 Xin-Yu Zhang, Dong Gong, Jiewei Cao, Chunhua Shen

Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.

Person Re-Identification

Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

no code implementations13 Jan 2020 Canjie Luo, Qingxiang Lin, Yuliang Liu, Lianwen Jin, Chunhua Shen

Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training.

Style Transfer

From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

3 code implementations7 Jan 2020 Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Chunhua Shen, Zhiguo Cao

Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.

Object Counting

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

9 code implementations CVPR 2020 Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, Youliang Yan

The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.

Real-time Instance Segmentation Semantic Segmentation

Ordered or Orderless: A Revisit for Video based Person Re-Identification

no code implementations24 Dec 2019 Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen

Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.

Video-Based Person Re-Identification

Unsupervised Representation Learning by Predicting Random Distances

1 code implementation22 Dec 2019 Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma

To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space.

Anomaly Detection Representation Learning

Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection

1 code implementation20 Dec 2019 Yuliang Liu, Tong He, Hao Chen, Xinyu Wang, Canjie Luo, Shuaitao Zhang, Chunhua Shen, Lianwen Jin

More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors.

Scene Text Detection

To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions

no code implementations10 Dec 2019 Jun-Jie Zhang, Lingqiao Liu, Peng Wang, Chunhua Shen

Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training.

Auxiliary Learning Self-Supervised Learning

Unified Multifaceted Feature Learning for Person Re-Identification

no code implementations20 Nov 2019 Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen

The loss structures the augmented images resulted by the two types of image erasing in a two-level hierarchy and enforces multifaceted attention to different parts.

Person Re-Identification

Deep Anomaly Detection with Deviation Networks

4 code implementations19 Nov 2019 Guansong Pang, Chunhua Shen, Anton Van Den Hengel

Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.

Anomaly Detection Cyber Attack Detection +3

DirectPose: Direct End-to-End Multi-Person Pose Estimation

7 code implementations18 Nov 2019 Zhi Tian, Hao Chen, Chunhua Shen

We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose.

Multi-Person Pose Estimation

Multi-marginal Wasserstein GAN

3 code implementations NeurIPS 2019 Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation.

Image Generation Translation

Deep Weakly-supervised Anomaly Detection

1 code implementation30 Oct 2019 Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Anomaly detection is typically posited as an unsupervised learning task in the literature due to the prohibitive cost and difficulty to obtain large-scale labeled anomaly data, but this ignores the fact that a very small number (e. g.,, a few dozens) of labeled anomalies can often be made available with small/trivial cost in many real-world anomaly detection applications.

Unsupervised Anomaly Detection

PolarMask: Single Shot Instance Segmentation with Polar Representation

2 code implementations CVPR 2020 Enze Xie, Peize Sun, Xiaoge Song, Wenhai Wang, Ding Liang, Chunhua Shen, Ping Luo

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.

Instance Segmentation Object Detection +1

Structured Binary Neural Networks for Image Recognition

no code implementations22 Sep 2019 Bohan Zhuang, Chunhua Shen, Mingkui Tan, Peng Chen, Lingqiao Liu, Ian Reid

Experiments on both classification, semantic segmentation and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature.

Object Detection Quantization +1

Task-Aware Monocular Depth Estimation for 3D Object Detection

1 code implementation17 Sep 2019 Xinlong Wang, Wei Yin, Tao Kong, Yuning Jiang, Lei LI, Chunhua Shen

In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.

3D Object Detection 3D Object Recognition +1

TextSR: Content-Aware Text Super-Resolution Guided by Recognition

1 code implementation16 Sep 2019 Wenjia Wang, Enze Xie, Peize Sun, Wenhai Wang, Lixun Tian, Chunhua Shen, Ping Luo

Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.

Scene Text Recognition Super-Resolution

Auxiliary Learning for Deep Multi-task Learning

no code implementations5 Sep 2019 Yifan Liu, Bohan Zhuang, Chunhua Shen, Hao Chen, Wei Yin

The most current methods can be categorized as either: (i) hard parameter sharing where a subset of the parameters is shared among tasks while other parameters are task-specific; or (ii) soft parameter sharing where all parameters are task-specific but they are jointly regularized.

Auxiliary Learning Depth Estimation +2

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

2 code implementations NeurIPS 2019 Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid

To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.

Ranked #29 on Monocular Depth Estimation on KITTI Eigen split (using extra training data)

Depth And Camera Motion Monocular Depth Estimation +1

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

7 code implementations ICCV 2019 Wenhai Wang, Enze Xie, Xiaoge Song, Yuhang Zang, Wenjia Wang, Tong Lu, Gang Yu, Chunhua Shen

Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.

Scene Text Detection

MobileFAN: Transferring Deep Hidden Representation for Face Alignment

no code implementations11 Aug 2019 Yang Zhao, Yifan Liu, Chunhua Shen, Yongsheng Gao, Shengwu Xiong

To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.

Face Alignment Facial Landmark Detection

Index Network

1 code implementation11 Aug 2019 Hao Lu, Yutong Dai, Chunhua Shen, Songcen Xu

By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.

Grayscale Image Denoising Image Denoising +4

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations

no code implementations10 Aug 2019 Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, Chunhua Shen

Furthermore, we propose a second progressive quantization scheme which gradually decreases the bit-width from high-precision to low-precision during training.

Knowledge Distillation Quantization

Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation

1 code implementation29 Jul 2019 Tong Shen, Dong Gong, Wei zhang, Chunhua Shen, Tao Mei

To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labels as proxy labels to supervise the training on target data.

Semantic Segmentation Unsupervised Domain Adaptation

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

no code implementations29 Jul 2019 Damien Teney, Peng Wang, Jiewei Cao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.

Visual Reasoning

Towards End-to-End Text Spotting in Natural Scenes

no code implementations14 Jun 2019 Peng Wang, Hui Li, Chunhua Shen

Text spotting in natural scene images is of great importance for many image understanding tasks.

Image Cropping Text Spotting

REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

1 code implementation CVPR 2020 Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, Anton Van Den Hengel

One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.

Vision and Language Navigation

Attention-guided Network for Ghost-free High Dynamic Range Imaging

4 code implementations CVPR 2019 Qingsen Yan, Dong Gong, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang

Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.

Optical Flow Estimation

Template-Based Automatic Search of Compact Semantic Segmentation Architectures

1 code implementation4 Apr 2019 Vladimir Nekrasov, Chunhua Shen, Ian Reid

Automatic search of neural architectures for various vision and natural language tasks is becoming a prominent tool as it allows to discover high-performing structures on any dataset of interest.

General Classification Real-Time Semantic Segmentation

FCOS: Fully Convolutional One-Stage Object Detection

72 code implementations ICCV 2019 Zhi Tian, Chunhua Shen, Hao Chen, Tong He

By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.

Object Detection Pedestrian Detection +1

Training Quantized Neural Networks with a Full-precision Auxiliary Module

no code implementations CVPR 2020 Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid

In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function.

Image Classification Object Detection +1

Knowledge Adaptation for Efficient Semantic Segmentation

1 code implementation CVPR 2019 Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan

To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.

Knowledge Distillation Semantic Segmentation

Structured Knowledge Distillation for Dense Prediction

1 code implementation CVPR 2019 Yifan Liu, Changyong Shun, Jingdong Wang, Chunhua Shen

Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.

Depth Estimation General Classification +5

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

1 code implementation8 Mar 2019 Yutong Xie, Jianpeng Zhang, Yong Xia, Chunhua Shen

Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

General Classification Lesion Classification +2

Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

1 code implementation CVPR 2019 Zhi Tian, Tong He, Chunhua Shen, Youliang Yan

In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.

Semantic Segmentation

Associatively Segmenting Instances and Semantics in Point Clouds

3 code implementations CVPR 2019 Xinlong Wang, Shu Liu, Xiaoyong Shen, Chunhua Shen, Jiaya Jia

A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed.

Ranked #9 on 3D Instance Segmentation on S3DIS (mRec metric)

3D Instance Segmentation 3D Semantic Segmentation

Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss

no code implementations21 Jan 2019 Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen

Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.

RGB Salient Object Detection Saliency Detection +1

Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

no code implementations CVPR 2019 Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton Van Den Hengel

Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches.

Graph Attention Referring Expression Comprehension

Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification

no code implementations11 Dec 2018 Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu, Chunhua Shen, Jianxin Wu

Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.

Vehicle Re-Identification

Visual Question Answering as Reading Comprehension

no code implementations CVPR 2019 Hui Li, Peng Wang, Chunhua Shen, Anton Van Den Hengel

In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem.

Common Sense Reasoning Machine Reading Comprehension +1

RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

no code implementations24 Nov 2018 Haokui Zhang, Ying Li, Peng Wang, Yu Liu, Chunhua Shen

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition.

Action Recognition

Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

no code implementations CVPR 2019 Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid

In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources.

General Classification Image Classification +2

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

5 code implementations2 Nov 2018 Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion.

Irregular Text Recognition Scene Text Recognition

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

2 code implementations CVPR 2019 Vladimir Nekrasov, Hao Chen, Chunhua Shen, Ian Reid

While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks.

Knowledge Distillation Language Modelling +4

Correlation Propagation Networks for Scene Text Detection

no code implementations30 Sep 2018 Zichuan Liu, Guosheng Lin, Wang Ling Goh, Fayao Liu, Chunhua Shen, Xiaokang Yang

In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).

Scene Text Detection

Diagnostics in Semantic Segmentation

no code implementations27 Sep 2018 Vladimir Nekrasov, Chunhua Shen, Ian Reid

Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene understanding and rapidly becoming vital in lots of real-world applications, including driverless cars and medical imaging.

Scene Understanding Semantic Segmentation

Goal-Oriented Visual Question Generation via Intermediate Rewards

no code implementations ECCV 2018 Jun-Jie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu, Anton Van Den Hengel

Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.

Informativeness Question Generation

Towards Effective Deep Embedding for Zero-Shot Learning

no code implementations30 Aug 2018 Lei Zhang, Peng Wang, Lingqiao Liu, Chunhua Shen, Wei Wei, Yannning Zhang, Anton Van Den Hengel

Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier.

Zero-Shot Learning

Training Compact Neural Networks with Binary Weights and Low Precision Activations

no code implementations8 Aug 2018 Bohan Zhuang, Chunhua Shen, Ian Reid

In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption.

Troy: Give Attention to Saliency and for Saliency

no code implementations4 Aug 2018 Pingping Zhang, Huchuan Lu, Chunhua Shen

In addition, our work has text overlap with arXiv:1804. 06242, arXiv:1705. 00938 by other authors.

Learning to predict crisp boundaries

no code implementations ECCV 2018 Ruoxi Deng, Chunhua Shen, Shengjun Liu, Huibing Wang, Xinru Liu

Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries.

Boundary Detection BSDS500 +1

Deep attention-based classification network for robust depth prediction

1 code implementation11 Jul 2018 Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Lingxiao Hang

However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?

Deep Attention Depth Estimation +3

Adversarial Learning with Local Coordinate Coding

no code implementations ICML 2018 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).

Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

no code implementations5 Jun 2018 Lei Zhang, Peng Wang, Chunhua Shen, Lingqiao Liu, Wei Wei, Yanning Zhang, Anton Van Den Hengel

In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.

Image Super-Resolution

Monocular Depth Estimation with Augmented Ordinal Depth Relationships

no code implementations2 Jun 2018 Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, Shugong Xu

In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.

Monocular Depth Estimation Stereo Matching +1

Bootstrapping the Performance of Webly Supervised Semantic Segmentation

1 code implementation CVPR 2018 Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid

In this work, we focus on weak supervision, developing a method for training a high-quality pixel-level classifier for semantic segmentation, using only image-level class labels as the provided ground-truth.

Transfer Learning Weakly-Supervised Semantic Segmentation

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

no code implementations11 May 2018 Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu

To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.

Few-Shot Learning Fine-Grained Image Recognition

Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification

no code implementations3 May 2018 Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang, Chunhua Shen

To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet).

Face Detection Facial Attribute Classification +4

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

no code implementations14 Apr 2018 Pingping Zhang, Huchuan Lu, Chunhua Shen

Salient object detection (SOD), which aims to find the most important region of interest and segment the relevant object/item in that area, is an important yet challenging vision task.

RGB Salient Object Detection Salient Object Detection

VITAL: VIsual Tracking via Adversarial Learning

no code implementations CVPR 2018 Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, WangMeng Zuo, Chunhua Shen, Rynson Lau, Ming-Hsuan Yang

To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.

General Classification Visual Tracking

Learning Deep Gradient Descent Optimization for Image Deconvolution

1 code implementation10 Apr 2018 Dong Gong, Zhen Zhang, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Yanning Zhang

Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

Blind Image Deblurring Image Deblurring +1

An end-to-end TextSpotter with Explicit Alignment and Attention

2 code implementations CVPR 2018 Tong He, Zhi Tian, Weilin Huang, Chunhua Shen, Yu Qiao, Changming Sun

This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances.

Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps

no code implementations22 Feb 2018 Pingping Zhang, Wei Liu, Dong Wang, Yinjie Lei, Hongyu Wang, Chunhua Shen, Huchuan Lu

Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.

Object Tracking Saliency Detection +1

Salient Object Detection by Lossless Feature Reflection

no code implementations19 Feb 2018 Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen

Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.

RGB Salient Object Detection Saliency Detection +1