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Learning to Segment the Tail
[article]
2020
arXiv
pre-print
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We call our approach Learning to Segment the Tail (LST). ...
head to the data-poor tail. ...
We thank all the reviewers for their constructive comments. This work was supported by Alibaba-NTU JRI, and partly supported by Major Scientific Research Project of Zhejiang Lab (No. 2019DB0ZX01). ...
arXiv:2004.00900v2
fatcat:f4ucuwxnxfay3ct3igzctetady
Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer
[article]
2020
arXiv
pre-print
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We address this problem by transferring the contents of tail classes from synthetic to real domain. ...
In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. ...
Zhu et al. (2017) proposed to transfer the domain of images, and domain-transferred images can be used to learn the segmentation model. ...
arXiv:2012.12545v1
fatcat:qxdokvfw65ekto4cydmindcn2m
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
[article]
2021
arXiv
pre-print
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The goal of this paper is to propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions. ...
Trained on COCO dataset without additional annotations of the long-tail objects, our model is able to discover novel and more fine-grained objects than the common categories in COCO. ...
Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 2026498, as well as a seed grant from the Institute for Human-Centered Artificial Intelligence ...
arXiv:2104.01257v1
fatcat:fju7jtlb6rhrnftb42qdxp52ri
The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation
[article]
2020
arXiv
pre-print
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They tend to suffer performance drop on realistic datasets that are usually long-tailed. This work aims to study and address such open challenges. ...
Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause ...
This suggests the box and mask head learning are less sensitive to long-tail training data than classification. ...
arXiv:2007.11978v5
fatcat:gigp7bmq3rcfjport7b67xtc4y
Unsupervised Object Detection with LiDAR Clues
[article]
2021
arXiv
pre-print
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The labeling process is carefully designed so as to mitigate the issue of long-tailed and open-ended distribution. ...
Then, an iterative segment labeling process is conducted to assign segment labels and to train a segment labeling network, which is based on features from both 2D images and 3D point clouds. ...
Acknowledgments The work is supported by the National Key R&D Program of China (2020AAA0105200), Beijing Academy of Artificial Intelligence. ...
arXiv:2011.12953v3
fatcat:oqwvshdmongjpjqs3uwl5ig73i
A Survey on Long-Tailed Visual Recognition
2022
International Journal of Computer Vision
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2022 pre-print arXiv:2205.13775v1 fatcat:4fvlrotqnbho5ha6cjftd6w4de
Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers. ...
In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. ...
Acknowledgements This work was supported by the National Key Research and Development Program of China (Grant No. 2021YFF0500900). ...
doi:10.1007/s11263-022-01622-8
fatcat:gtlfuw6igbd6xixqhynnqxubw4
Page 20 of The Journal of Biological Psychology Vol. 5, Issue 2
[page]
1963
The Journal of Biological Psychology
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The failure to confirm the results of McConnell and other experimenters (which state that head and tail segments regenerated in water show about the same savings of the initially acquired learning) may ...
The planarians which regenerated from tail segments did not exhibit this ability. ...
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
[article]
2021
arXiv
pre-print
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However, it is not clear how to find the optimal split without sacrificing the overall network performance. ...
To amalgamate these methods and thereby maximize their distinct strengths, here we show that the Vision Transformer, a recently developed deep learning architecture with straightforward decomposable configuration ...
By minimizing the loss with respect to the tail weight, the gradients of the local tails are passed reversely to the server. ...
arXiv:2111.01338v2
fatcat:ixwyhfpfvfh5dgkwcpanvuzlma
Deep Long-Tailed Learning: A Survey
[article]
2021
arXiv
pre-print
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Considering the rapid evolution of this field, this paper aims to provide a comprehensive survey on recent advances in deep long-tailed learning. ...
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution ...
Afterward, learning to segment the tail (LST) [77] also divides the training samples into several balanced subsets, and handles each one based on class-incremental learning. ...
arXiv:2110.04596v1
fatcat:lpvt2x6cv5crxm2qxdctjrlkqq
Unlocking the Full Potential of Small Data with Diverse Supervision
[article]
2020
arXiv
pre-print
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2019 pre-print arXiv:1911.12911v1 fatcat:font6jinwbdybl2grdk4rgo3da
Following the standard few-shot learning protocol, we use the head classes for representation learning and the tail classes for evaluation. ...
In particular, we use the richly annotated scene parsing dataset ADE20K to construct our realistic Long-tail Recognition with Diverse Supervision (LRDS) benchmark by splitting the object categories into ...
novel forms of multi-task learning that are able to better combine the benefits of diverse labels is an important direction for future work. ...
arXiv:1911.12911v2
fatcat:tayre5ygxffoxjirv4vmymf3k4
The relative effectiveness of observing response vs predifferentiation pretraining on children's discrimination learning
1971
Psychonomic Science
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stimuli; (2) making a specific observing response to the critical feature of the stimuli; (3) simple familiarization with the stimui; and (4) developing a set to compare stimuli. ...
This study was designed to assess the relative effectiveness of four components of pretraining on a subsequent simultaneous discriminaton and reversal: (1) making same-different judgments about the two ...
All cat stimuli were exactly the same except for the type of tail. Tbe distinguishing feature of each stimulus (mouth or tail) fit entirely within the bottom segment of the console window. ...
doi:10.3758/bf03335559
fatcat:kw5regebojgajgrfg7ae3yesni
Deep learning method for comet segmentation and comet assay image analysis
2020
Scientific Reports
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First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). ...
Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. ...
In contrast, DL can allow raw image data as input and learn to detect and segment comet in an end-to-end process. ...
doi:10.1038/s41598-020-75592-7
pmid:33144610
pmcid:PMC7609680
fatcat:pp3fvg22infclkspsruhg5iiei
Page 154 of The Biological Bulletin Vol. 82, Issue 1
[page]
1942
The Biological Bulletin
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Whether a head or a tail forms depends to a large extent upon the segment of the host into which the implant is inserted. ...
An implant derived from the anterior end (segments 1-7) of a donor may give rise to a bud of either head or tail type (Sayles, 1940a). ...
Learning to rank audience for behavioral targeting in display ads
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
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Behavioral targeting (BT), which aims to sell advertisers those behaviorally related user segments to deliver their advertisements, is facing a bottleneck in serving the rapid growth of long tail advertisers ...
segments. ...
Each segment is labeled with keywords to indicate the interests of the users in the segment. For example, a segment labeled with "car buyer" indicates the users in this segment may want to buy cars. ...
doi:10.1145/2063576.2063666
dblp:conf/cikm/TangLYSGGYZ11
fatcat:jidbcm6cyvg7bojqastar3vmla
PartImageNet: A Large, High-Quality Dataset of Parts
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2112.00933v1 fatcat:5eeqa2rovzf5felzhycht5ywhi
It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. ...
This has the potential to improve the performance of algorithms for object recognition and segmentation but can also help for downstream tasks like activity recognition. ...
These approaches mainly share a common learning strategy: 1) unsupervised learning, 2) explicit clustering to obtain the parts, 3) modeling the spatial relation between parts. ...
arXiv:2112.00933v2
fatcat:iysdgtt4vjdg3lmttw2ps45lgu
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