Search Results for author: Ming-Ming Cheng

Found 154 papers, 91 papers with code

Sora Generates Videos with Stunning Geometrical Consistency

no code implementations27 Feb 2024 XuanYi Li, Daquan Zhou, Chenxu Zhang, Shaodong Wei, Qibin Hou, Ming-Ming Cheng

We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality.

3D Reconstruction Video Generation

A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence

no code implementations20 Feb 2024 Penghai Zhao, Xin Zhang, Ming-Ming Cheng, Jian Yang, Xiang Li

In response to these concerns, this Analysis aims to provide a thorough review of reviews in the PAMI field from diverse perspectives.

Language Modelling Large Language Model

Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models

1 code implementation15 Dec 2023 Senmao Li, Taihang Hu, Fahad Shahbaz Khan, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, Jian Yang

This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder.

Knowledge Distillation

A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation

1 code implementation10 Dec 2023 Yunheng Li, Zhongyu Li, ShangHua Gao, Qilong Wang, Qibin Hou, Ming-Ming Cheng

Effectively modeling discriminative spatio-temporal information is essential for segmenting activities in long action sequences.

Action Segmentation

PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

1 code implementation7 Dec 2023 Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, Ying Shan

Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts.

Diffusion Personalization Tuning Free Text-to-Image Generation

TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes

no code implementations7 Dec 2023 Xuying Zhang, Bo-Wen Yin, Yuming Chen, Zheng Lin, Yunheng Li, Qibin Hou, Ming-Ming Cheng

Particularly, a cross-modal graph is constructed to align the object points accurately and noun phrases decoupled from the 3D mesh and textual description.

Graph Attention Object

Class Incremental Learning with Pre-trained Vision-Language Models

no code implementations31 Oct 2023 Xialei Liu, Xusheng Cao, Haori Lu, Jia-Wen Xiao, Andrew D. Bagdanov, Ming-Ming Cheng

We also propose a method for parameter retention in the adapter layers that uses a measure of parameter importance to better maintain stability and plasticity during incremental learning.

Class Incremental Learning Incremental Learning +1

Zone Evaluation: Revealing Spatial Bias in Object Detection

1 code implementation20 Oct 2023 Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ping Wang, Ming-Ming Cheng

A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders.

Object object-detection +1

Low-Resolution Self-Attention for Semantic Segmentation

no code implementations8 Oct 2023 Yu-Huan Wu, Shi-Chen Zhang, Yun Liu, Le Zhang, Xin Zhan, Daquan Zhou, Jiashi Feng, Ming-Ming Cheng, Liangli Zhen

Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.

Segmentation Semantic Segmentation

Enhancing Representations through Heterogeneous Self-Supervised Learning

no code implementations8 Oct 2023 Zhong-Yu Li, Bo-Wen Yin, ShangHua Gao, Yongxiang Liu, Li Liu, Ming-Ming Cheng

Thus, we propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model.

Image Classification Instance Segmentation +5

Masked Autoencoders are Efficient Class Incremental Learners

1 code implementation ICCV 2023 Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

Moreover, MAEs can reliably reconstruct original input images from randomly selected patches, which we use to store exemplars from past tasks more efficiently for CIL.

Class Incremental Learning Incremental Learning

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection

1 code implementation10 Aug 2023 Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, Ming-Ming Cheng

We aim at providing the object detection community with an efficient and performant object detector, termed YOLO-MS.

Object object-detection +2

Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model

1 code implementation ICCV 2023 Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Xialei Liu, Chongyi Li, Ming-Ming Cheng

However, these methods are impeded by several critical limitations: a) the explicit calibration process is both labor- and time-intensive, b) challenge exists in transferring denoisers across different camera models, and c) the disparity between synthetic and real noise is exacerbated by digital gain.

Image Denoising

Revisiting Computer-Aided Tuberculosis Diagnosis

1 code implementation6 Jul 2023 Yun Liu, Yu-Huan Wu, Shi-Chen Zhang, Li Liu, Min Wu, Ming-Ming Cheng

This dataset enables the training of sophisticated detectors for high-quality CTD.

Image Classification

CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection

1 code implementation20 Jun 2023 Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou

Such a distillation manner relieves the student's head from receiving contradictory supervision signals from the ground-truth annotations and the teacher's predictions, greatly improving the student's detection performance.

Dense Object Detection Knowledge Distillation +3

Referring Camouflaged Object Detection

1 code implementation13 Jun 2023 Xuying Zhang, Bowen Yin, Zheng Lin, Qibin Hou, Deng-Ping Fan, Ming-Ming Cheng

We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images with salient target objects.

Object object-detection +1

CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation

1 code implementation7 Jun 2023 Boyuan Sun, YuQi Yang, Le Zhang, Ming-Ming Cheng, Qibin Hou

Motivated by these, we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies.

Segmentation Semi-Supervised Semantic Segmentation

Delving Deeper into Data Scaling in Masked Image Modeling

no code implementations24 May 2023 Cheng-Ze Lu, Xiaojie Jin, Qibin Hou, Jun Hao Liew, Ming-Ming Cheng, Jiashi Feng

The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical.

Self-Supervised Learning

Multi-Space Neural Radiance Fields

no code implementations CVPR 2023 Ze-Xin Yin, Jiaxiong Qiu, Ming-Ming Cheng, Bo Ren

Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering.

Advances in Deep Concealed Scene Understanding

1 code implementation21 Apr 2023 Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, Luc van Gool

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage.

Scene Understanding Semantic Segmentation

SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything"

no code implementations12 Apr 2023 Ge-Peng Ji, Deng-Ping Fan, Peng Xu, Ming-Ming Cheng, BoWen Zhou, Luc van Gool

Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision.

MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

1 code implementation ICCV 2023 ShangHua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs' ability to contextual relation learning among object semantic parts in an image.

Image Generation

SRFormer: Permuted Self-Attention for Single Image Super-Resolution

1 code implementation ICCV 2023 Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, Qibin Hou

Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e. g., SwinIR) can significantly improve the model performance but the computation overhead is also considerable.

Image Super-Resolution

Large Selective Kernel Network for Remote Sensing Object Detection

1 code implementation ICCV 2023 YuXuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng, Jian Yang, Xiang Li

To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection.

Object object-detection +3

Co-Salient Object Detection with Co-Representation Purification

1 code implementation14 Mar 2023 Ziyue Zhu, Zhao Zhang, Zheng Lin, Xing Sun, Ming-Ming Cheng

Such irrelevant information in the co-representation interferes with its locating of co-salient objects.

Co-Salient Object Detection Object +2

Traffic Scene Parsing through the TSP6K Dataset

no code implementations6 Mar 2023 Peng-Tao Jiang, YuQi Yang, Yang Cao, Qibin Hou, Ming-Ming Cheng, Chunhua Shen

Traffic scene parsing is one of the most important tasks to achieve intelligent cities.

Scene Parsing

QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning

no code implementations2 Feb 2023 Weimin Shi, Mingchen Zhuge, Dehong Gao, Zhong Zhou, Ming-Ming Cheng, Deng-Ping Fan

Daily images may convey abstract meanings that require us to memorize and infer profound information from them.

World Knowledge

CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition

no code implementations15 Jan 2023 Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang, Qibin Hou, Ming-Ming Cheng, Jiashi Feng

Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition.

Action Recognition Temporal Action Localization

Towards Spatial Equilibrium Object Detection

1 code implementation14 Jan 2023 Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ming-Ming Cheng

In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial bias'' by measuring the detection performance over zones.

Object object-detection +1

Endpoints Weight Fusion for Class Incremental Semantic Segmentation

no code implementations CVPR 2023 Jia-Wen Xiao, Chang-Bin Zhang, Jiekang Feng, Xialei Liu, Joost Van de Weijer, Ming-Ming Cheng

In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions.

Class-Incremental Semantic Segmentation Incremental Learning +1

SLAN: Self-Locator Aided Network for Vision-Language Understanding

no code implementations ICCV 2023 Jiang-Tian Zhai, Qi Zhang, Tong Wu, Xing-Yu Chen, Jiang-Jiang Liu, Ming-Ming Cheng

By aggregating vision-language information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.

Image Retrieval Retrieval

Robust Saliency Guidance for Data-free Class Incremental Learning

no code implementations16 Dec 2022 Xialei Liu, Jiang-Tian Zhai, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

Data-Free Class Incremental Learning (DFCIL) aims to sequentially learn tasks with access only to data from the current one.

Class Incremental Learning Incremental Learning

SLAN: Self-Locator Aided Network for Cross-Modal Understanding

no code implementations28 Nov 2022 Jiang-Tian Zhai, Qi Zhang, Tong Wu, Xing-Yu Chen, Jiang-Jiang Liu, Bo Ren, Ming-Ming Cheng

By aggregating cross-modal information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.

Image Retrieval Retrieval

Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

1 code implementation22 Nov 2022 Qibin Hou, Cheng-Ze Lu, Ming-Ming Cheng, Jiashi Feng

This paper does not attempt to design a state-of-the-art method for visual recognition but investigates a more efficient way to make use of convolutions to encode spatial features.

object-detection Object Detection +1

Towards Sustainable Self-supervised Learning

1 code implementation20 Oct 2022 ShangHua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL model based on the existing pretrained SSL model, also called as "base" model, in a cost-friendly manner, ii) allowing the training of the new model to be compatible with various base models.

Object Detection Relation +3

Long-Tailed Class Incremental Learning

1 code implementation1 Oct 2022 Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world.

Class Incremental Learning Incremental Learning

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

3 code implementations18 Sep 2022 Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, ZhengNing Liu, Ming-Ming Cheng, Shi-Min Hu

Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90. 6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it.

Segmentation Semantic Segmentation

Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes

no code implementations18 Aug 2022 Yu-Huan Wu, Da Zhang, Le Zhang, Xin Zhan, Dengxin Dai, Yun Liu, Ming-Ming Cheng

Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners.

3D Object Detection Object +2

Contrastive Masked Autoencoders are Stronger Vision Learners

1 code implementation27 Jul 2022 Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, Jiashi Feng

The momentum encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart.

Contrastive Learning Image Classification +3

Designing An Illumination-Aware Network for Deep Image Relighting

1 code implementation21 Jul 2022 Zuo-Liang Zhu, Zhen Li, Rui-Xun Zhang, Chun-Le Guo, Ming-Ming Cheng

Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images.

Image Relighting Image-to-Image Translation

Class-Specific Semantic Reconstruction for Open Set Recognition

no code implementations5 Jul 2022 Hongzhi Huang, Yu Wang, QinGhua Hu, Ming-Ming Cheng

In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning.

Open Set Learning

RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

2 code implementations14 Jun 2022 ShangHua Gao, Zhong-Yu Li, Qi Han, Ming-Ming Cheng, Liang Wang

Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further.

Action Segmentation Instance Segmentation +5

SERE: Exploring Feature Self-relation for Self-supervised Transformer

1 code implementation10 Jun 2022 Zhong-Yu Li, ShangHua Gao, Ming-Ming Cheng

Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i. e., spatial/channel self-relations, for self-supervised learning.

Relation Self-Supervised Learning +1

VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

1 code implementation13 May 2022 YuChao Gu, Xintao Wang, Liangbin Xie, Chao Dong, Gen Li, Ying Shan, Ming-Ming Cheng

Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.

Blind Face Restoration Quantization

Localization Distillation for Object Detection

1 code implementation12 Apr 2022 Zhaohui Zheng, Rongguang Ye, Qibin Hou, Dongwei Ren, Ping Wang, WangMeng Zuo, Ming-Ming Cheng

Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking underperforms for years.

Knowledge Distillation Object +2

Towards An End-to-End Framework for Flow-Guided Video Inpainting

2 code implementations CVPR 2022 Zhen Li, Cheng-Ze Lu, Jianhua Qin, Chun-Le Guo, Ming-Ming Cheng

Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories.

Hallucination Optical Flow Estimation +2

Interactive Style Transfer: All is Your Palette

no code implementations25 Mar 2022 Zheng Lin, Zhao Zhang, Kang-Rui Zhang, Bo Ren, Ming-Ming Cheng

Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image.

Style Transfer

Representation Compensation Networks for Continual Semantic Segmentation

1 code implementation CVPR 2022 Chang-Bin Zhang, Jia-Wen Xiao, Xialei Liu, Ying-Cong Chen, Ming-Ming Cheng

In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.

Class Incremental Learning Continual Semantic Segmentation +16

Visual Attention Network

17 code implementations20 Feb 2022 Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu

In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.

Image Classification Instance Segmentation +5

Deeply Explain CNN via Hierarchical Decomposition

no code implementations23 Jan 2022 Ming-Ming Cheng, Peng-Tao Jiang, Ling-Hao Han, Liang Wang, Philip Torr

The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making process.

Decision Making

Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching

no code implementations17 Dec 2021 Dingwen Zhang, Wenyuan Zeng, Guangyu Guo, Chaowei Fang, Lechao Cheng, Ming-Ming Cheng, Junwei Han

Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-refinement model and the main semantic region mining model.

Knowledge Distillation Weakly supervised Semantic Segmentation +1

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

4 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.

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

3 code implementations IEEE 2021 Peng-Tao Jiang, Chang-Bin Zhang, Qibin Hou, Ming-Ming Cheng, Yunchao Wei

To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.

Object Semantic Segmentation +1

P2T: Pyramid Pooling Transformer for Scene Understanding

4 code implementations22 Jun 2021 Yu-Huan Wu, Yun Liu, Xin Zhan, Ming-Ming Cheng

A popular solution to this problem is to use a single pooling operation to reduce the sequence length.

Ranked #3 on RGB Salient Object Detection on DUTS-TE (F-measure metric)

Image Classification Instance Segmentation +5

Representative Batch Normalization With Feature Calibration

no code implementations CVPR 2021 Shang-Hua Gao, Qi Han, Duo Li, Ming-Ming Cheng, Pai Peng

We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost.

On the Connection between Local Attention and Dynamic Depth-wise Convolution

1 code implementation ICLR 2022 Qi Han, Zejia Fan, Qi Dai, Lei Sun, Ming-Ming Cheng, Jiaying Liu, Jingdong Wang

Sparse connectivity: there is no connection across channels, and each position is connected to the positions within a small local window.

object-detection Object Detection +2

Large-scale Unsupervised Semantic Segmentation

3 code implementations6 Jun 2021 ShangHua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng, Junwei Han, Philip Torr

In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress.

Representation Learning Segmentation +1

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

Salient Objects in Clutter

2 code implementations7 May 2021 Deng-Ping Fan, Jing Zhang, Gang Xu, Ming-Ming Cheng, Ling Shao

This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.

Image Augmentation Object +4

Low-Light Image and Video Enhancement Using Deep Learning: A Survey

3 code implementations21 Apr 2021 Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, Chen Change Loy

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination.

Face Detection Low-Light Image Enhancement +1

Temporal Modulation Network for Controllable Space-Time Video Super-Resolution

1 code implementation CVPR 2021 Gang Xu, Jun Xu, Zhen Li, Liang Wang, Xing Sun, Ming-Ming Cheng

To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos.

Space-time Video Super-resolution Video Super-Resolution

Localization Distillation for Dense Object Detection

2 code implementations CVPR 2022 Zhaohui Zheng, Rongguang Ye, Ping Wang, Dongwei Ren, WangMeng Zuo, Qibin Hou, Ming-Ming Cheng

Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.

Dense Object Detection Knowledge Distillation +2

Concealed Object Detection

1 code implementation20 Feb 2021 Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.

Camouflaged Object Segmentation Dichotomous Image Segmentation +2

Global2Local: Efficient Structure Search for Video Action Segmentation

2 code implementations CVPR 2021 Shang-Hua Gao, Qi Han, Zhong-Yu Li, Pai Peng, Liang Wang, Ming-Ming Cheng

Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further.

Action Segmentation Segmentation

iNAS: Integral NAS for Device-Aware Salient Object Detection

no code implementations ICCV 2021 Yu-Chao Gu, Shang-Hua Gao, Xu-Sheng Cao, Peng Du, Shao-Ping Lu, Ming-Ming Cheng

Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations.

Neural Architecture Search Object +3

EDN: Salient Object Detection via Extremely-Downsampled Network

1 code implementation24 Dec 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng, Bo Ren

In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well.

Object object-detection +3

MobileSal: Extremely Efficient RGB-D Salient Object Detection

1 code implementation24 Dec 2020 Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian, Yu-Chao Gu, Ming-Ming Cheng

Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.

Object object-detection +2

Centralized Information Interaction for Salient Object Detection

no code implementations21 Dec 2020 Jiang-Jiang Liu, Zhi-Ang Liu, Ming-Ming Cheng

Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.

Object object-detection +3

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection

3 code implementations NeurIPS 2020 Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo

Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD).

Saliency Detection

Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

3 code implementations26 Nov 2020 Qijian Zhang, Runmin Cong, Chongyi Li, Ming-Ming Cheng, Yuming Fang, Xiaochun Cao, Yao Zhao, Sam Kwong

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.

object-detection Object Detection +1

Delving Deep into Label Smoothing

2 code implementations25 Nov 2020 Chang-Bin Zhang, Peng-Tao Jiang, Qibin Hou, Yunchao Wei, Qi Han, Zhen Li, Ming-Ming Cheng

Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets.

Classification General Classification

Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow

1 code implementation1 Sep 2020 Yu-Chao Gu, Le Zhang, Yun Liu, Shao-Ping Lu, Ming-Ming Cheng

Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes.

Generalized Zero-Shot Learning

Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

1 code implementation28 Aug 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Wang Gao, Ming-Ming Cheng

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.

Instance Segmentation object-detection +3

RGB-D Salient Object Detection: A Survey

9 code implementations1 Aug 2020 Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, Ling Shao

Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well.

Attribute Object +4

Geometric Style Transfer

no code implementations10 Jul 2020 Xiao-Chang Liu, Xuan-Yi Li, Ming-Ming Cheng, Peter Hall

Our contribution is to introduce a neural architecture that supports transfer of geometric style.

Style Transfer

Semi-Supervised Learning with Meta-Gradient

1 code implementation8 Jul 2020 Xin-Yu Zhang, Taihong Xiao, HaoLin Jia, Ming-Ming Cheng, Ming-Hsuan Yang

In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.

Meta-Learning Pseudo Label

Re-thinking Co-Salient Object Detection

2 code implementations7 Jul 2020 Deng-Ping Fan, Tengpeng Li, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Ming-Ming Cheng, Huazhu Fu, Jianbing Shen

CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.

Benchmarking Co-Salient Object Detection +3

Interactive Knowledge Distillation

no code implementations3 Jul 2020 Shipeng Fu, Zhen Li, Jun Xu, Ming-Ming Cheng, Zitao Liu, Xiaomin Yang

Knowledge distillation is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained large teacher network.

Image Classification Knowledge Distillation

MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation

1 code implementation16 Jun 2020 Shijie Li, Yazan Abu Farha, Yun Liu, Ming-Ming Cheng, Juergen Gall

Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.

Action Segmentation Segmentation

Dependency Aware Filter Pruning

no code implementations6 May 2020 Kai Zhao, Xin-Yu Zhang, Qi Han, Ming-Ming Cheng

Convolutional neural networks (CNNs) are typically over-parameterized, bringing considerable computational overhead and memory footprint in inference.

Gradient-Induced Co-Saliency Detection

1 code implementation ECCV 2020 Zhao Zhang, Wenda Jin, Jun Xu, Ming-Ming Cheng

Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images.

Co-Salient Object Detection

Conditional Variational Image Deraining

1 code implementation23 Apr 2020 Ying-Jun Du, Jun Xu, Xian-Tong Zhen, Ming-Ming Cheng, Ling Shao

In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image.

Density Estimation Rain Removal

Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton

no code implementations18 Apr 2020 Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng

To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets.

Edge Detection Semantic Segmentation

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

1 code implementation15 Apr 2020 Yu-Huan Wu, Shang-Hua Gao, Jie Mei, Jun Xu, Deng-Ping Fan, Rong-Guo Zhang, Ming-Ming Cheng

The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity.

COVID-19 Diagnosis General Classification +3

Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation

1 code implementation9 Apr 2020 Lin-Zhuo Chen, Zheng Lin, Ziqin Wang, Yong-Liang Yang, Ming-Ming Cheng

S-Conv is competent to infer the sampling offset of the convolution kernel guided by the 3D spatial information, helping the convolutional layer adjust the receptive field and adapt to geometric transformations.

RGBD Semantic Segmentation 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

Highly Efficient Salient Object Detection with 100K Parameters

1 code implementation ECCV 2020 Shang-Hua Gao, Yong-Qiang Tan, Ming-Ming Cheng, Chengze Lu, Yunpeng Chen, Shuicheng Yan

Salient object detection models often demand a considerable amount of computation cost to make precise prediction for each pixel, making them hardly applicable on low-power devices.

Object object-detection +2

Deep Hough Transform for Semantic Line Detection

2 code implementations ECCV 2020 Kai Zhao, Qi Han, Chang-Bin Zhang, Jun Xu, Ming-Ming Cheng

In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task.

Line Detection object-detection

Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle

1 code implementation19 Feb 2020 Xin-Yu Zhang, Kai Zhao, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang

Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.

Network Pruning

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

AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning

no code implementations27 Nov 2019 Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng, Yun Liu, Jia-Wang Bian, Ming-Ming Cheng

The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.

Informativeness

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 Relation

Image Inpainting with Learnable Bidirectional Attention Maps

1 code implementation ICCV 2019 Chaohao Xie, Shaohui Liu, Chao Li, Ming-Ming Cheng, WangMeng Zuo, Xiao Liu, Shilei Wen, Errui Ding

Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness.

Image Inpainting valid

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.

Depth And Camera Motion Monocular Depth Estimation +1

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

no code implementations26 Aug 2019 Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid

According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.

Robust Regression via Deep Negative Correlation Learning

no code implementations24 Aug 2019 Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng

Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).

Age Estimation Crowd Counting +2

EGNet:Edge Guidance Network for Salient Object Detection

3 code implementations22 Aug 2019 Jia-Xing Zhao, Jiang-Jiang Liu, Den-Ping Fan, Yang Cao, Jufeng Yang, Ming-Ming Cheng

In the second step, we integrate the local edge information and global location information to obtain the salient edge features.

Object object-detection +2

Scoot: A Perceptual Metric for Facial Sketches

1 code implementation ICCV 2019 Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul L. Rosin, Rongrong Ji

In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.

Face Sketch Synthesis SSIM +1

Image Formation Model Guided Deep Image Super-Resolution

1 code implementation18 Aug 2019 Jinshan Pan, Yang Liu, Deqing Sun, Jimmy Ren, Ming-Ming Cheng, Jian Yang, Jinhui Tang

We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution.

Image Super-Resolution

Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

1 code implementation17 Jun 2019 Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, Ling Shao

A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images.

Image Denoising Test

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

LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer

1 code implementation14 May 2019 Lin-Zhuo Chen, Xuan-Yi Li, Deng-Ping Fan, Kai Wang, Shao-Ping Lu, Ming-Ming Cheng

We design a novel Local Spatial Aware (LSA) layer, which can learn to generate Spatial Distribution Weights (SDWs) hierarchically based on the spatial relationship in local region for spatial independent operations, to establish the relationship between these operations and spatial distribution, thus capturing the local geometric structure sensitively. We further propose the LSANet, which is based on LSA layer, aggregating the spatial information with associated features in each layer of the network better in network design. The experiments show that our LSANet can achieve on par or better performance than the state-of-the-art methods when evaluating on the challenging benchmark datasets.

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.

object-detection RGB Salient Object Detection +1

Res2Net: A New Multi-scale Backbone Architecture

29 code implementations2 Apr 2019 Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr

We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.

Image Classification Instance Segmentation +4

DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection

1 code implementation28 Mar 2019 Yun Liu, Ming-Ming Cheng, Xin-Yu Zhang, Guang-Yu Nie, Meng Wang

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).

object-detection RGB Salient Object Detection +2

Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship

no code implementations23 Jan 2019 Jie Liang, Jufeng Yang, Ming-Ming Cheng, Paul L. Rosin, Liang Wang

In this paper we propose a unified framework to simultaneously discover the number of clusters and group the data points into them using subspace clustering.

Clustering Model Selection

Salient Object Detection via High-to-Low Hierarchical Context Aggregation

no code implementations28 Dec 2018 Yun Liu, Yu Qiu, Le Zhang, Jia-Wang Bian, Guang-Yu Nie, Ming-Ming Cheng

In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.

object-detection RGB Salient Object Detection +4

Self-Erasing Network for Integral Object Attention

no code implementations NeurIPS 2018 Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng

To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models.

Object Semantic Segmentation +1

Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation

no code implementations ECCV 2018 Ruochen Fan, Qibin Hou, Ming-Ming Cheng, Gang Yu, Ralph R. Martin, Shi-Min Hu

We also combine our method with Mask R-CNN for instance segmentation, and demonstrated for the first time the ability of weakly supervised instance segmentation using only keyword annotations.

Clustering graph partitioning +6

MatchBench: An Evaluation of Feature Matchers

no code implementations7 Aug 2018 Jia-Wang Bian, Ruihan Yang, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid, WenHai Wu

This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.

Hifi: Hierarchical feature integration for skeleton detection

no code implementations1 Jul 2018 Kai Zhao, Wei Shen, ShangHua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.

Object Object Skeleton Detection

Enhanced-alignment Measure for Binary Foreground Map Evaluation

2 code implementations26 May 2018 Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, Ali Borji

The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways.

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.

Segmentation Semantic Segmentation

Optimizing the F-measure for Threshold-free Salient Object Detection

no code implementations ICCV 2019 Kai Zhao, Shang-Hua Gao, Wenguan Wang, Ming-Ming Cheng

By reformulating the standard F-measure we propose the relaxed F-measure which is differentiable w. r. t the posterior and can be easily appended to the back of CNNs as the loss function.

object-detection RGB Salient Object Detection +1

Semantic Edge Detection with Diverse Deep Supervision

1 code implementation9 Apr 2018 Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, Jiawang Bian, DaCheng Tao

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.

Edge Detection Object Proposal Generation +2

Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure

1 code implementation9 Apr 2018 Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Ming-Ming Cheng, Bo Ren, Rongrong Ji, Paul L. Rosin

However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.

Face Sketch Synthesis

Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction

no code implementations27 Mar 2018 Qibin Hou, Jiang-Jiang Liu, Ming-Ming Cheng, Ali Borji, Philip H. S. Torr

Although these tasks are inherently very different, we show that our unified approach performs very well on all of them and works far better than current single-purpose state-of-the-art methods.

Edge Detection Semantic Segmentation

WebSeg: Learning Semantic Segmentation from Web Searches

no code implementations27 Mar 2018 Qibin Hou, Ming-Ming Cheng, Jiang-Jiang Liu, Philip H. S. Torr

In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods.

Segmentation Semantic Segmentation

Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

no code implementations ECCV 2018 Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, Ali Borji

Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter.

Attribute Object +3

Review of Visual Saliency Detection with Comprehensive Information

no code implementations9 Mar 2018 Runmin Cong, Jianjun Lei, Huazhu Fu, Ming-Ming Cheng, Weisi Lin, Qingming Huang

With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection.

Co-Salient Object Detection Video Saliency Detection

Revisiting Video Saliency: A Large-scale Benchmark and a New Model

1 code implementation CVPR 2018 Wenguan Wang, Jianbing Shen, Fang Guo, Ming-Ming Cheng, Ali Borji

Existing video saliency datasets lack variety and generality of common dynamic scenes and fall short in covering challenging situations in unconstrained environments.

Video Saliency Detection

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

no code implementations5 Jan 2018 Kai Zhao, Wei Shen, Shang-Hua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.

Object Object Skeleton Detection

S4Net: Single Stage Salient-Instance Segmentation

1 code implementation CVPR 2019 Ruochen Fan, Ming-Ming Cheng, Qibin Hou, Tai-Jiang Mu, Jingdong Wang, Shi-Min Hu

Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch.

Instance Segmentation Segmentation +1

Image Matching: An Application-oriented Benchmark

no code implementations12 Sep 2017 Jia-Wang Bian, Le Zhang, Yun Liu, Wen-Yan Lin, Ming-Ming Cheng, Ian D. Reid

To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.

Attribute Benchmarking

Structure-measure: A New Way to Evaluate Foreground Maps

1 code implementation ICCV 2017 Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, Ali Borji

Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.

Object object-detection +5

Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

no code implementations CVPR 2017 Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, Shuicheng Yan

We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems.

Classification General Classification +5

FLIC: Fast Linear Iterative Clustering with Active Search

no code implementations6 Dec 2016 Jia-Xing Zhao, Ren Bo, Qibin Hou, Ming-Ming Cheng, Paul L. Rosin

It also has drawbacks on convergence rate as a result of both the fixed search region and separately doing the assignment step and the update step.

Clustering Segmentation

Deeply supervised salient object detection with short connections

4 code implementations CVPR 2017 Qibin Hou, Ming-Ming Cheng, Xiao-Wei Hu, Ali Borji, Zhuowen Tu, Philip Torr

Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).

Boundary Detection Object +5

Sequential Optimization for Efficient High-Quality Object Proposal Generation

no code implementations14 Nov 2015 Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H. S. Torr

We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.

Computational Efficiency Object +3

STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation

1 code implementation10 Sep 2015 Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao, Shuicheng Yan

Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations.

object-detection RGB Salient Object Detection +4

Salient Object Detection: A Benchmark

no code implementations5 Jan 2015 Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li

We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods.

Benchmarking Object +3

Salient Object Detection: A Survey

no code implementations18 Nov 2014 Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, Jia Li

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision.

Object object-detection +4

Salient Object Detection: A Discriminative Regional Feature Integration Approach

no code implementations CVPR 2013 Huaizu Jiang, Zejian yuan, Ming-Ming Cheng, Yihong Gong, Nanning Zheng, Jingdong Wang

Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.

Image Segmentation Object +4

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

no code implementations CVPR 2014 Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Philip Torr

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm.

Object object-detection +1

Dense Semantic Image Segmentation with Objects and Attributes

no code implementations CVPR 2014 Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr

The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e. g. "I see a shiny red chair').

Attribute Image Segmentation +2