Search Results for author: Tianfei Zhou

Found 24 papers, 19 papers with code

Rethinking Semantic Segmentation: A Prototype View

1 code implementation28 Mar 2022 Tianfei Zhou, Wenguan Wang, Ender Konukoglu, Luc van Gool

Prevalent semantic segmentation solutions, despite their different network designs (FCN based or attention based) and mask decoding strategies (parametric softmax based or pixel-query based), can be placed in one category, by considering the softmax weights or query vectors as learnable class prototypes.

Semantic Segmentation

Deep Hierarchical Semantic Segmentation

2 code implementations27 Mar 2022 Liulei Li, Tianfei Zhou, Wenguan Wang, Jianwu Li, Yi Yang

In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy.

Multi-Label Classification Semantic Segmentation

Visual Abductive Reasoning

1 code implementation26 Mar 2022 Chen Liang, Wenguan Wang, Tianfei Zhou, Yi Yang

In this paper, we propose a new task and dataset, Visual Abductive Reasoning (VAR), for examining abductive reasoning ability of machine intelligence in everyday visual situations.

Local-Global Context Aware Transformer for Language-Guided Video Segmentation

1 code implementation18 Mar 2022 Chen Liang, Wenguan Wang, Tianfei Zhou, Jiaxu Miao, Yawei Luo, Yi Yang

In light of this, we present Locater (local-global context aware Transformer), which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner.

Frame Semantic Segmentation +4

Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation

1 code implementation17 Mar 2022 Tianfei Zhou, Meijie Zhang, Fang Zhao, Jianwu Li

Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic representations.

Weakly-Supervised Semantic Segmentation

Detail-Preserving Transformer for Light Field Image Super-Resolution

1 code implementation2 Jan 2022 Shunzhou Wang, Tianfei Zhou, Yao Lu, Huijun Di

DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence.

Image Super-Resolution

Group-Wise Learning for Weakly Supervised Semantic Segmentation

1 code implementation journal 2021 Tianfei Zhou, Liulei Li, Xueyi Li, Chun-Mei Feng, Jianwu Li, Ling Shao

The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models.

Structured Prediction Weakly-Supervised Object Localization +2

Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality

2 code implementations15 Oct 2021 Chun-Mei Feng, Huazhu Fu, Tianfei Zhou, Yong Xu, Ling Shao, David Zhang

Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.

Image Reconstruction

A Survey on Deep Learning Technique for Video Segmentation

1 code implementation2 Jul 2021 Wenguan Wang, Tianfei Zhou, Fatih Porikli, David Crandall, Luc van Gool

Video segmentation, i. e., partitioning video frames into multiple segments or objects, plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to virtual background creation in video conferencing, just to name a few.

Autonomous Driving Scene Understanding +3

Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

1 code implementation20 Jun 2021 Tianfei Zhou, Liulei Li, Gustav Bredell, Jianwu Li, Ender Konukoglu

The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement.

Active Learning Interactive Segmentation +2

Face Forensics in the Wild

1 code implementation CVPR 2021 Tianfei Zhou, Wenguan Wang, Zhiyuan Liang, Jianbing Shen

On existing public benchmarks, face forgery detection techniques have achieved great success.

Frame Multiple Instance Learning

Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

1 code implementation CVPR 2021 Tianfei Zhou, Wenguan Wang, Si Liu, Yi Yang, Luc van Gool

To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner.

Human Parsing Multi-Person Pose Estimation +3

Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification

no code implementations26 Feb 2021 Yi Zhou, Lei Huang, Tianfei Zhou, Ling Shao

For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

5 code implementations ICCV 2021 Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, Luc van Gool

Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.

Metric Learning Optical Character Recognition +2

Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

1 code implementation9 Dec 2020 Xueyi Li, Tianfei Zhou, Jianwu Li, Yi Zhou, Zhaoxiang Zhang

We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models.

Ranked #20 on Weakly-Supervised Semantic Segmentation on COCO 2014 val (using extra training data)

Structured Prediction Weakly-Supervised Semantic Segmentation

Weakly Supervised 3D Object Detection from Lidar Point Cloud

1 code implementation ECCV 2020 Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Luc van Gool, Dengxin Dai

This work proposes a weakly supervised approach for 3D object detection, only requiring a small set of weakly annotated scenes, associated with a few precisely labeled object instances.

3D Object Detection

Video Object Segmentation with Episodic Graph Memory Networks

1 code implementation ECCV 2020 Xiankai Lu, Wenguan Wang, Martin Danelljan, Tianfei Zhou, Jianbing Shen, Luc van Gool

How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation.

Frame Semantic Segmentation +3

Cascaded Human-Object Interaction Recognition

1 code implementation CVPR 2020 Tianfei Zhou, Wenguan Wang, Siyuan Qi, Haibin Ling, Jianbing Shen

The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction.

Human-Object Interaction Detection

Motion-Attentive Transition for Zero-Shot Video Object Segmentation

1 code implementation9 Mar 2020 Tianfei Zhou, Shunzhou Wang, Yi Zhou, Yazhou Yao, Jianwu Li, Ling Shao

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.

Ranked #6 on Unsupervised Video Object Segmentation on DAVIS 2016 (using extra training data)

Semantic Segmentation Unsupervised Video Object Segmentation +2

Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking

no code implementations19 Jan 2015 Jinwu Liu, Yao Lu, Tianfei Zhou

Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking.

Multiple Instance Learning Visual Tracking

Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling

no code implementations28 Oct 2014 Tianfei Zhou, Yao Lu, Feng Lv, Huijun Di, Qingjie Zhao, Jian Zhang

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years.

Motion Detection

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