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Continual Local Replacement for Few-shot Learning [article]

Canyu Le, Zhonggui Chen, Xihan Wei, Biao Wang, Lei Zhang
2020 arXiv   pre-print
Original labeled images will be locally replaced by the selected images for the next epoch training.  ...  Extensive experiments demonstrate that it can achieve state-of-the-art results on various few-shot image recognition benchmarks.  ...  local replacement method for few-shot image recognition.  ... 
arXiv:2001.08366v2 fatcat:4wz7bja2vzgnhou44vhkwfkm34

Transfer Learning in a Transductive Setting

Marcus Rohrbach, Sandra Ebert, Bernt Schiele
2013 Neural Information Processing Systems  
More specifically we adapt a graph-based learning algorithm -so far only used for semi-supervised learningto zero-shot and few-shot learning.  ...  We evaluate our approach on three challenging datasets in two different applications, namely on Animals with Attributes and ImageNet for image classification and on MPII Composites for activity recognition  ...  We evaluated this approach on three diverse datasets for image and video-activity recognition, consistently improving performance over the state-of-the-art for zero-shot and few-shot prediction.  ... 
dblp:conf/nips/RohrbachES13 fatcat:b3mufpaek5dqnfqogtaqbf3jzm

Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022 [article]

Li Gu, Zhixiang Chi, Huan Liu, Yuanhao Yu, Yang Wang
2022 arXiv   pre-print
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.  ...  Conclusion and future work In this work, we proposed several improvements for the few-shot video object recognition task.  ...  Few-shot concept is adapted and extended to various real-world settings, such as few-shot continual learning [4, 10, 17] , test-time adaptation [5, 8, 14] and leveraging domain shift [3, 21] .  ... 
arXiv:2210.00174v1 fatcat:x2p26lwgirdbzkg3ospkcdkjp4

Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds

Zhao Xu, Rui Kang, Heng Li
2022 Buildings  
Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry.  ...  The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately.  ...  SHOT SHOT (signature of histograms of orientations) was proposed as a local reference system for surface matching [41] .  ... 
doi:10.3390/buildings12070968 fatcat:2ozfamsgpvgahnod2iz4shtq4m

Cross-domain Few-shot Micro-expression Recognition incorporating Action Units

Yi Dai, Ling Feng
2021 IEEE Access  
[57] addressed cross-domain few-shot problem in generic object recognition and fine-grained image classification.  ...  Instead of sharing weights across the entire image, the region layer has local convolution components for different facial regions, thus enabling the model to capture local appearance changes.  ... 
doi:10.1109/access.2021.3120542 fatcat:o3dpaqnxabao7n4yvlczw7z5by

Transferring Textual Knowledge for Visual Recognition [article]

Wenhao Wu, Zhun Sun, Wanli Ouyang
2022 arXiv   pre-print
Conventional methods randomly initialize the linear classifier head for vision classification, but they leave the usage of the text encoder for downstream visual recognition tasks undiscovered.  ...  In this paper, we revise the role of the linear classifier and replace the classifier with the embedded language representations of the object categories.  ...  in many types of transfer learning, i.e., image/video recognition, zero-shot recognition, few-shot recognition.  ... 
arXiv:2207.01297v2 fatcat:wsrv6u3mfjdm5fndfruta5guvi

Research on Long Shot Segmentation in Basketball Video

ShengBo Liao, Jingmeng Sun, Haitao Yang
2015 International Journal of Multimedia and Ubiquitous Engineering  
In the basketball segmentation in long shots, Gauss filter is adopted to smooth noise in the image firstly.  ...  Acknowledgments This work was supported by The Fundamental Research Funds for the Central Universities. No. HEUCF151601.  ...  For cutting of basketball in long shots, we used Gauss filter to smooth image noises; then split up background areas with the use of inter-frame difference; next, we devised "basketball recognition strategy  ... 
doi:10.14257/ijmue.2015.10.12.19 fatcat:nupendtvx5cejknj7lhh6qeb6i

SGAP-Net: Semantic-Guided Attentive Prototypes Network for Few-Shot Human-Object Interaction Recognition

Zhong Ji, Xiyao Liu, Yanwei Pang, Xuelong Li
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Motivated by the success of few-shot learning that learns a robust model from a few instances, we formulate HOI as a few-shot task in a meta-learning framework to alleviate the above challenges.  ...  Finally, in order to realize the task of few-shot HOI, we reorganize two HOI benchmark datasets, i.e., HICO-FS and TUHOI-FS, to realize the task of few-shot HOI.  ...  (a) The few-shot HOI recognition results on 5-way 1-shot. (b) The few-shot HOI recognition results on 5-way 5-shot.  ... 
doi:10.1609/aaai.v34i07.6764 fatcat:52fc6e65ajfnzngqxx4jrxg26u

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Chenwei Wang, Xiaoyu Liu, Yulin Huang, Siyi Luo, Jifang Pei, Jianyu Yang, Deqing Mao
2022 Remote Sensing  
Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning.  ...  The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods.  ...  This has led to studies on few-shot learning (FSL) [6] [7] [8] , which builds new classifiers from very few labeled SAR images, and these studies also continue to drive towards efficient and robust SAR  ... 
doi:10.3390/rs14184547 fatcat:7kh4rqyrjvhhdjasvcpntfw2cu

Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples [article]

Nir Zabari, Yedid Hoshen
2021 arXiv   pre-print
Our results are particularly remarkable for images containing rare categories.  ...  Our method takes as input the image-level labels of the class categories present in the image; they can be obtained automatically or manually.  ...  33] and object detection [4, 34, 35], few-shot and zero-shot of visual concepts.  ... 
arXiv:2112.03185v1 fatcat:k7tgvamso5frzkhqmxqrjs77am

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples [article]

Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu
2018 arXiv   pre-print
In this paper, we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting, termed few-shot fine-grained recognition (FSFG).  ...  ., few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples.  ...  Compared with those tasks, we consider a novel few-shot image recognition topic, i.e., few-shot fine-grained image recognition.  ... 
arXiv:1805.04288v1 fatcat:jkcua6jv2rdelmglifgq77lj2q

Florence: A New Foundation Model for Computer Vision [article]

Lu Yuan and Dongdong Chen and Yi-Ling Chen and Noel Codella and Xiyang Dai and Jianfeng Gao and Houdong Hu and Xuedong Huang and Boxin Li and Chunyuan Li and Ce Liu and Mengchen Liu and Zicheng Liu and Yumao Lu and Yu Shi and Lijuan Wang and Jianfeng Wang and Bin Xiao and Zhen Xiao and Jianwei Yang and Michael Zeng and Luowei Zhou and Pengchuan Zhang
2021 arXiv   pre-print
Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects  ...  object detection, VQA, image caption, video retrieval and action recognition.  ...  We would also thank Qingfen Lin, Cha Zhang for their thoughtful feedback on the broader impacts of the paper. Thanks Mei Gao, Ping Jin for helping run evaluations on benchmark infrastructure.  ... 
arXiv:2111.11432v1 fatcat:qpq2twmmgrapxb6whq3f4iss6u

Few-shot Structured Radiology Report Generation Using Natural Language Prompts [article]

Matthias Keicher, Kamilia Mullakaeva, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab
2022 arXiv   pre-print
The results indicate that even when only a few image-level annotations are used for training, the method can localize pathologies in chest radiographs and generate structured reports.  ...  Then, we create textual prompts for each structured finding and optimize a classifier for predicting clinical findings and their associations within the medical image.  ...  Ashkan Khakzar was partially supported by the Munich Center for Machine Learning (MCML) with funding from the BMBF under the project 01IS18036B.  ... 
arXiv:2203.15723v1 fatcat:dad4pnshezbanl7nojmp4tnema

Generalized Coarse-to-Fine Visual Recognition with Progressive Training [article]

Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille
2019 arXiv   pre-print
We apply our framework to three vision tasks including image classification, object localization and semantic segmentation, and demonstrate consistent accuracy gain compared to the baseline training strategy  ...  which starts with feeding the ground-truth instead of the coarse output into the fine model, and gradually increases the fraction of coarse output, so that at the end of training the fine model is ready for  ...  In what follows, we show much larger improvements in few-shot experiments. Few-shot Classification Few-shot classification experiments are performed on Mini-ImageNet [42] and ImageNet [6] .  ... 
arXiv:1811.12047v2 fatcat:eurqud2ifvc5vmbvmsqvdbfuky

Dynamic Few-Shot Visual Learning without Forgetting [article]

Spyros Gidaris, Nikos Komodakis
2018 arXiv   pre-print
We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings  ...  To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the  ...  Dynamic Few-Shot Learning without Forgetting Feature Extractor Classifier Classification weight vectors Base Novel Few-shot classification weight generator Test image Training data for  ... 
arXiv:1804.09458v1 fatcat:bas4voc3nfe27lmu6n5lcyqnvi
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