Search Results for author: Yiran Zhong

Found 31 papers, 13 papers with code

Vicinity Vision Transformer

1 code implementation21 Jun 2022 Weixuan Sun, Zhen Qin, Hui Deng, Jianyuan Wang, Yi Zhang, Kaihao Zhang, Nick Barnes, Stan Birchfield, Lingpeng Kong, Yiran Zhong

Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.

Image Classification Natural Language Processing

Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective

no code implementations10 Apr 2022 Hui Deng, Tong Zhang, Yuchao Dai, Jiawei Shi, Yiran Zhong, Hongdong Li

In this paper, we propose to model deep NRSfM from a sequence-to-sequence translation perspective, where the input 2D frame sequence is taken as a whole to reconstruct the deforming 3D non-rigid shape sequence.

3D Reconstruction Translation

Implicit Motion Handling for Video Camouflaged Object Detection

no code implementations CVPR 2022 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.

Motion Estimation object-detection +2

cosFormer: Rethinking Softmax in Attention

2 code implementations ICLR 2022 Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, Yiran Zhong

As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length.

Language Modelling Natural Language Processing

Transcribing Natural Languages for The Deaf via Neural Editing Programs

no code implementations17 Dec 2021 Dongxu Li, Chenchen Xu, Liu Liu, Yiran Zhong, Rong Wang, Lars Petersson, Hongdong Li

This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses.

MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation

no code implementations29 Nov 2021 Jiadai Sun, Yuxin Mao, Yuchao Dai, Yiran Zhong, Jianyuan Wang

The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model

no code implementations22 Nov 2021 Jing Zhang, Yuchao Dai, Mehrtash Harandi, Yiran Zhong, Nick Barnes, Richard Hartley

Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty.

object-detection Object Detection

IDENTIFYING CONCEALED OBJECTS FROM VIDEOS

no code implementations29 Sep 2021 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.

object-detection Object Detection

RGB-D Saliency Detection via Cascaded Mutual Information Minimization

1 code implementation ICCV 2021 Jing Zhang, Deng-Ping Fan, Yuchao Dai, Xin Yu, Yiran Zhong, Nick Barnes, Ling Shao

In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.

Saliency Detection

Memory-Free Generative Replay For Class-Incremental Learning

1 code implementation1 Sep 2021 Xiaomeng Xin, Yiran Zhong, Yunzhong Hou, Jinjun Wang, Liang Zheng

With the absence of old task images, they often assume that old knowledge is well preserved if the classifier produces similar output on new images.

class-incremental learning Incremental Learning

Exploring Depth Contribution for Camouflaged Object Detection

no code implementations24 Jun 2021 Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, Yuchao Dai

In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods.

Monocular Depth Estimation object-detection +3

Invertible Attention

1 code implementation16 Jun 2021 Jiajun Zha, Yiran Zhong, Jing Zhang, Richard Hartley, Liang Zheng

Attention has been proved to be an efficient mechanism to capture long-range dependencies.

Image Reconstruction

Positive Sample Propagation along the Audio-Visual Event Line

2 code implementations CVPR 2021 Jinxing Zhou, Liang Zheng, Yiran Zhong, Shijie Hao, Meng Wang

To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.

audio-visual event localization

Depth Completion using Piecewise Planar Model

no code implementations6 Dec 2020 Yiran Zhong, Yuchao Dai, Hongdong Li

More specifically, we represent the desired depth map as a collection of 3D planar and the reconstruction problem is formulated as the optimization of planar parameters.

Depth Completion Visual Odometry

Efficient Depth Completion Using Learned Bases

no code implementations2 Dec 2020 Yiran Zhong, Yuchao Dai, Hongdong Li

The given sparse depth points are served as a data term to constrain the weighting process.

Depth Completion

Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation

2 code implementations NeurIPS 2020 Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong Li

Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volume to learn a 3D cost volume.

Optical Flow Estimation Stereo Matching

Hierarchical Neural Architecture Search for Deep Stereo Matching

1 code implementation NeurIPS 2020 Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Tom Drummond, Hongdong Li, ZongYuan Ge

To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.

Neural Architecture Search Semantic Segmentation +2

Deblurring by Realistic Blurring

1 code implementation CVPR 2020 Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, Hongdong Li

To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.

Deblurring Image Deblurring

Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

no code implementations CVPR 2019 Yiran Zhong, Pan Ji, Jianyuan Wang, Yuchao Dai, Hongdong Li

In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning.

Optical Flow Estimation

Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

3 code implementations CVPR 2019 Xuelian Cheng, Yiran Zhong, Yuchao Dao, Pan Ji, Hongdong Li

In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps.

Depth Completion Stereo Matching +1

Stereo Computation for a Single Mixture Image

no code implementations ECCV 2018 Yiran Zhong, Yuchao Dai, Hongdong Li

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before.

Stereo Matching Stereo Matching Hand

3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

no code implementations13 Aug 2018 Yiran Zhong, Yuchao Dai, Hongdong Li

This paper is concerned with the problem of how to better exploit 3D geometric information for dense semantic image labeling.

Open-World Stereo Video Matching with Deep RNN

no code implementations ECCV 2018 Yiran Zhong, Hongdong Li, Yuchao Dai

Deep Learning based stereo matching methods have shown great successes and achieved top scores across different benchmarks.

Stereo Matching Stereo Matching Hand

Adversarial Spatio-Temporal Learning for Video Deblurring

1 code implementation28 Mar 2018 Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Wei Liu, Hongdong Li

To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training.

Deblurring

Self-Supervised Learning for Stereo Matching with Self-Improving Ability

no code implementations4 Sep 2017 Yiran Zhong, Yuchao Dai, Hongdong Li

Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations.

Self-Supervised Learning Stereo Matching +1

Robust Multi-body Feature Tracker: A Segmentation-free Approach

no code implementations CVPR 2016 Pan Ji, Hongdong Li, Mathieu Salzmann, Yiran Zhong

Feature tracking is a fundamental problem in computer vision, with applications in many computer vision tasks, such as visual SLAM and action recognition.

Action Recognition Motion Segmentation

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