Search Results for author: Qibin Hou

Found 34 papers, 22 papers with code

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

Second, we introduce the concept of valuable localization region that can aid to selectively distill the classification and localization knowledge for a certain region.

Knowledge Distillation Object Detection

L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

1 code implementation7 Apr 2022 Peng-Tao Jiang, YuQi Yang, Qibin Hou, Yunchao Wei

Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks.

Transfer Learning Weakly-Supervised Semantic Segmentation

VOLO: Vision Outlooker for Visual Recognition

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

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

Image Classification Semantic Segmentation

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

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

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

LV-BERT: Exploiting Layer Variety for BERT

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

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

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

2 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.

Semantic Segmentation Weakly-Supervised Object Localization

Refiner: Refining Self-attention for Vision Transformers

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

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

Image Classification

FakeMix Augmentation Improves Transparent Object Detection

1 code implementation24 Mar 2021 Yang Cao, Zhengqiang Zhang, Enze Xie, Qibin Hou, Kai Zhao, Xiangui Luo, Jian Tuo

However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability.

Data Augmentation Object Detection +1

AutoSpace: Neural Architecture Search with Less Human Interference

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

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

Neural Architecture Search

DeepViT: Towards Deeper Vision Transformer

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

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

Image Classification Representation Learning

Coordinate Attention for Efficient Mobile Network Design

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

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

Object Detection Semantic Segmentation

Localization Distillation for Dense Object Detection

2 code implementations24 Feb 2021 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

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

Rotate to Attend: Convolutional Triplet Attention Module

4 code implementations6 Oct 2020 Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, Qibin Hou

In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing cross-dimension interaction using a three-branch structure.

Image Classification Instance Segmentation +2

Rethinking Bottleneck Structure for Efficient Mobile Network Design

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

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

General Classification Neural Architecture Search +1

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

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

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

Weakly-Supervised Semantic Segmentation

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

Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation

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

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

Object Recognition Semantic Segmentation +1

Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

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

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

Scene Parsing Semantic Segmentation

Cross-layer Feature Pyramid Network for Salient Object Detection

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

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

RGB Salient Object Detection Salient Object Detection

PROTOTYPE-ASSISTED ADVERSARIAL LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION

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

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

Object Recognition Semantic Segmentation +1

Neural Epitome Search for Architecture-Agnostic Network Compression

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

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

Model Compression Neural Architecture Search

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

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

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

RGB Salient Object Detection Salient Object Detection

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.

Semantic Segmentation

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.

graph partitioning Image-level Supervised Instance Segmentation +3

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.

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.

RGB Salient Object Detection Salient Object 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 Semantic Segmentation

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.

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 Proposal Generation RGB Salient Object Detection +2

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