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A reinforcement learning based adaptive ROI generation for video object segmentation

Usman Ahmad Usmani, Junzo Watada, Jafreezal Jaafar, Izzatdin Abdul Aziz, Arunava Roy
2021 IEEE Access  
This work aims at solving the zero-shot video object segmentation issue in a holistic fashion.  ...  We take advantage of the inherent correlations between the video frames by incorporating a global co-attention mechanism to overcome the limitations.  ...  Zero Shot Video Object Segmentation (ZVOS) is very helpful for both application and research since it does not need to interact manually during the assumption phase.  ... 
doi:10.1109/access.2021.3132453 fatcat:kjuksmp33nbqlmsaxgk2sneys4

CRNet: Cross-Reference Networks for Few-Shot Segmentation [article]

Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu
2020 arXiv   pre-print
With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images, thus helping the few-shot segmentation task.  ...  Recently, few-shot segmentation is proposed to solve this problem. Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images.  ...  This research is also partly supported by the Delta-NTU Corporate Lab with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore.  ... 
arXiv:2003.10658v1 fatcat:vuyhq3h57rakteroednjwpd2jy

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation [article]

Xiaolin Zhang, Yunchao Wei, Yi Yang, Thomas Huang
2020 arXiv   pre-print
One-shot image semantic segmentation poses a challenging task of recognizing the object regions from unseen categories with only one annotated example as supervision.  ...  In this paper, we propose a simple yet effective Similarity Guidance network to tackle the One-shot (SG-One) segmentation problem.  ...  Relationship with Video Object Segmentation One-shot video segmentation is to segment specified objects in video clips with only the first frame densely annotated [36] .  ... 
arXiv:1810.09091v4 fatcat:qhgvsybmqzhhxnu4pm5jgkoldy

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks [article]

Wenguan Wang, Xiankai Lu, Jianbing Shen, David Crandall, Ling Shao
2020 arXiv   pre-print
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS).  ...  To further demonstrate the generalizability of our framework, we extend AGNN to an additional task: image object co-segmentation (IOCS).  ...  In this work, an attentive graph neural network (AGNN) is proposed to addresses zero-shot video object segmentation (ZVOS), which recasts ZVOS as an end-to-end, message passing based graph information  ... 
arXiv:2001.06807v1 fatcat:il4hh2aes5h4nh2fayb3ysvz6m

A Survey on Deep Learning Technique for Video Segmentation [article]

Wenguan Wang, Tianfei Zhou, Fatih Porikli, David Crandall, Luc Van Gool
2021 arXiv   pre-print
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  ...  In this survey, we comprehensively review two basic lines of research - generic object segmentation (of unknown categories) in videos and video semantic segmentation - by introducing their respective task  ...  AVOS, or unsupervised video segmentation or zero-shot video segmentation, performs VOS in an automatic manner, without any manual initialization ( Fig. 1(a-b) ).  ... 
arXiv:2107.01153v3 fatcat:nry4yjhq7zhtzbfh53wf7ie3um

Multimodal One-Shot Learning of Speech and Images [article]

Ryan Eloff, Herman A. Engelbrecht, Herman Kamper
2019 arXiv   pre-print
We use a dataset of paired spoken and visual digits to specifically investigate recent advances in Siamese convolutional neural networks.  ...  Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter".  ...  Speech is parametrised as Mel-frequency cepstral coefficients with first and second order derivatives. We centre zero-pad or crop speech segments to 120 frames.  ... 
arXiv:1811.03875v2 fatcat:tf453xjpzrbpnkzmdzp3wtfvje

Front Matter: Volume 12084

Wolfgang Osten, Dmitry Nikolaev, Jianhong Zhou
2022 Fourteenth International Conference on Machine Vision (ICMV 2021)  
generation method for Siamese neural networks training [12084-30] 1B Wavelet network-based deep learning system for image classification [12084-35] 1C Zero-shot learning and classification of steel surface  ...  for optimising the accuracy of Siamese neural networks in re- identification 0Y Few-shot object detection via metric learning [12084-25] 0Z A novel machine learning approach based on fast multi-scale  ...  Afterwards five technical sessions with thirty-four presentations completed the program that ended with an award ceremony for the best papers in all sessions that were selected by the respective chairs  ... 
doi:10.1117/12.2625908 fatcat:zrgauhqj7ng65flfcmbhqx2ony

Multimodal One-shot Learning of Speech and Images

Ryan Eloff, Herman A. Engelbrecht, Herman Kamper
2019 ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Specifically, we consider spoken word learning with co-occurring visual context in a one-shot setting, where an agent must learn novel concepts (words and object categories from a single joint audio-visual  ...  This model outperforms our other approaches on our most difficult benchmark with a cross-modal matching accuracy of 40.3% for 10-way 5-shot learning.  ...  For our neural network models which require fixed length inputs, we centre zero-pad or crop speech segments to 120 frames.  ... 
doi:10.1109/icassp.2019.8683587 dblp:conf/icassp/EloffEK19 fatcat:47yfbmhsg5bbbdeiivcglj3vtu

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Martinikorena, I., +, TIP 2020 2328-2343 MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation.  ...  ., +, TIP 2020 9445-9457 Siamese Local and Global Networks for Robust Face Tracking. Qi, Y., +, Text Co-Detection in Multi-View Scene.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook [article]

Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas
2021 arXiv   pre-print
Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress.  ...  Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking  ...  Matas, know more: Unsupervised video object segmentation with co-attentionObject tracking by reconstruction with view-specific discriminative siamese networks,” in  ... 
arXiv:2112.02838v1 fatcat:nsre4b5uafeopjb37go6c3obwu

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Ma, D., +, TIP 2021 1825-1839 CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence.  ...  Liu, Y., +, TIP 2021 5573-5588 Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Table of contents

2020 IEEE Transactions on Image Processing  
Sun 1465 DP-Siam: Dynamic Policy Siamese Network for Robust Object Tracking ..... M. H. Abdelpakey and M. S.  ...  Urey 4505 One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation ..................... ..............................................................................  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Learning Video Object Segmentation from Unlabeled Videos [article]

Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David Crandall,, Steven C. H. Hoi
2020 arXiv   pre-print
With a carefully-designed architecture and strong representation learning ability, our learned model can be applied to diverse VOS settings, including object-level zero-shot VOS, instance-level zero-shot  ...  We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data.  ...  Introduction Video object segmentation (VOS) has two common settings, zero-shot and one-shot.  ... 
arXiv:2003.05020v1 fatcat:qood45mzjfchleuq4p6gilq6ce

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Su, Li; Huang, Qingming 2615 Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation DAY 3 -Jan 14, 2021 Weng, Zichun; Xiang, Youjun; Li, Xianfeng; Liang, Juntao; Huo, Wanliang  ...  2120 Temporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video DAY 3 -Jan 14, 2021 Yorimoto, Kohei; Han, Xian-Hua 2340 Deep Residual Attention Network  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks [article]

Arulkumar Subramaniam, Jayesh Vaidya, Muhammed Abdul Majeed Ameen, Athira Nambiar, Anurag Mittal
2021 arXiv   pre-print
In this regard, we propose a generic module called "Co-Segmentation Activation Module" (COSAM) that can be plugged into any CNN to promote the notion of co-segmentation based attention among a sequence  ...  regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.  ...  Co-segmentation activation module (COSAM) Concept of object co-segmentation Object co-segmentation is the task of identifying and segmenting common objects from two or more images according to "some"  ... 
arXiv:2111.07370v2 fatcat:2z5axk3svzf3jmoer3nspkyi7a
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