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Revisiting Few-shot Activity Detection with Class Similarity Control
[article]
2020
arXiv
pre-print
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In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities ...
We experiment on three large scale benchmarks for temporal activity detection (ActivityNet1.2, ActivityNet1.3 and THUMOS14 datasets) in a few-shot setting. ...
Additionally, we also show the effect of controlling the novel class similarity with the pre-training classes on few-shot detection performance. (1) a 3D ConvNet feature extractor applied to both the untrimmed ...
arXiv:2004.00137v1
fatcat:fi4cbvma35bblhy6dzhaab3py4
Revisiting Few-Shot Learning for Facial Expression Recognition
[article]
2019
arXiv
pre-print
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In this paper, we revisit and compare existing few-shot learning methods for the low-shot facial expression recognition in terms of their generalisation ability via episode-training. ...
We embrace these challenges and formulate the problem as a low-shot learning, where once the base classifier is deployed, it must rapidly adapt to recognise novel classes using a few samples. ...
In Table I we saw that few-shot learning algorithms are able to generalise better when the novel classes are extracted from a dataset created in a controlled environment, thus with less intra-class variation ...
arXiv:1912.02751v2
fatcat:nfaifcpoxjcozhzrgxzd24mbmi
Incremental Few-Shot Object Detection
[article]
2020
arXiv
pre-print
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We present a study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting ...
base classes) and with few examples. ...
For few-shot object detection in particular, a key merit of CentreNet is that each individual class maintains its own prediction heatmap and makes independent detection by activation thresholding. ...
arXiv:2003.04668v2
fatcat:kzmyl25s4vdxzbwmgh4cvcwufe
Incremental Few-Shot Object Detection
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We present a study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting ...
base classes) and with few examples. ...
For few-shot object detection in particular, a key merit of CentreNet is that each individual class maintains its own prediction heatmap and makes independent detection by activation thresholding. ...
doi:10.1109/cvpr42600.2020.01386
dblp:conf/cvpr/Perez-RuaZHX20
fatcat:76xg2hmjkrg67kro3e6qfd6t3y
Wandering Within a World: Online Contextualized Few-Shot Learning
[article]
2021
arXiv
pre-print
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We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. ...
Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual experience of an agent wandering within a world. ...
To break the rigid, artificial structure of continual and few-shot learning, we propose a new continual few-shot learning setting where environments are revisited and the total number of novel object classes ...
arXiv:2007.04546v3
fatcat:5giqdg6gbbdqvj4ekzizlo7rkm
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
[article]
2021
arXiv
pre-print
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Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community ...
Extensive experiments on multiple benchmarks show our framework is remarkably superior to other existing approaches and establishes a new state-of-the-art in few-shot literature. ...
Generalized Few-Shot Object Detection A. 1
. ...
arXiv:2108.09017v1
fatcat:w3ntxyh3wbdq7bwrtovgy5x47u
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.12991v3 fatcat:dmup7jm4aresjhpc46o74pffyiOther Versions
As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each ...
The test and val sets were balanced and reported on four splits: Many-shot containing classes with > 100 images, Medium-shot including classes with ≥ 20 & ≤ 100 images, Few-shot covering classes with < ...
In train set, the number of images per class is ranged from 1,280 to 5, which
Many-shot Classes house finch brown bear meerkat alligator lizard
Medium-shot Classes Few-shot Classes warthog bighorn ...
arXiv:2009.12991v4
fatcat:iirc3ar72bhelm7kjv2thcssxa
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
[article]
2021
arXiv
pre-print
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To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification. ...
Moreover, the performance of Tip-Adapter can be further boosted by fine-tuning such properly initialized adapter for only a few epochs with super-fast convergence speed. ...
In Eq. ( 5 ), β in the activation function ϕ controls the sharpness of the affinities. ...
arXiv:2111.03930v2
fatcat:ntojz5cn65eghfqvbmcgij4s2i
Putting a bug in ML: The moth olfactory network learns to read MNIST
[article]
2019
arXiv
pre-print
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We show that MothNet successfully learns to read given very few training samples (1 to 10 samples per class). ...
In this few-samples regime, it outperforms standard machine learning methods such as nearest-neighbors, support-vector machines, and neural networks (NNs), and matches specialized one-shot transfer-learning ...
N refers to the number of training samples per class. ...
arXiv:1802.05405v3
fatcat:iuiwuadzjbbs5e7puwkijicagq
Associative Alignment for Few-shot Image Classification
[article]
2020
arXiv
pre-print
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Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes. ...
Experiments on four standard datasets and three backbones demonstrate that combining our centroid-based alignment loss results in absolute accuracy improvements of 4.4%, 1.2%, and 6.2% in 5-shot learning ...
.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: The Conference on Computer Vision and Pattern Recognition (2019) 26. ...
arXiv:1912.05094v3
fatcat:o7lxo6kvijbw3kjb6ekf6qytoa
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
[article]
2020
arXiv
pre-print
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We find existing detection methods are unable to model few-shot classes when the dataset is extremely skewed, which can result in classifier imbalance in terms of parameter magnitude. ...
Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored.In this work, we provide the first systematic analysis ...
are severely imbalanced, since low-shot categories get few chances to be activated. ...
arXiv:2006.10408v1
fatcat:6kvftja62zg7bkaqdkgrb4hai4
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
[article]
2021
arXiv
pre-print
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The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. ...
to novel data, i.e. learning to transfer in few-shot scenario.) or meta-learning. ...
Considering an N -way K-shot few-shot task, where the support set on novel class has N classes with K labeled images and the query set contains the same N classes with Q unlabeled images in each class, ...
arXiv:2102.03983v1
fatcat:u37noahnpze4jm6ba4pcqihdyu
Single-shot carrier–envelope phase measurement of few-cycle laser pulses
2009
Nature Physics
Preserved Fulltext
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Here, we demonstrate the first single-shot CEP measurement of intense few-cycle laser pulses. ...
We focus a laser pulse on a gas target and detect photoelectrons emitted in opposing directions ('left-right') parallel to the polarization of the laser. By comparing the left-right asymmetries ...
Typically, the rate of change of the CEP is detected with a so-called f-to-2f interferometer 2,3,17 , and the CEP drift is then readjusted with a control loop. ...
doi:10.1038/nphys1250
fatcat:35gpcetuw5ajteym6ahti2ad7q
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
[article]
2022
arXiv
pre-print
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For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. ...
Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. ...
Few-shot Object Detection Few-Shot Object Detection (FSOD) is the task of detecting rare objects from several samples. ...
arXiv:2205.06743v1
fatcat:ouqpe2swrbch5n2l5jvjs2y6dm
Few-Shot Object Detection: A Survey
[article]
2021
arXiv
pre-print
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To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. ...
As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of few-shot object detection. ...
In the following, we will briefly
discuss differences and similarities with few-shot object de- Zero-shot object detection can be defined similar to few-shot
tection. ...
arXiv:2112.11699v1
fatcat:d6iubz4ui5abvdccjlkm667vay
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