2018/10/20コンピュータビジョン勉強会@関東「ECCV読み会2018」発表資料
Yew, Z. J., & Lee, G. H. (2018). 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. European Conference on Computer Vision.
2018/10/20コンピュータビジョン勉強会@関東「ECCV読み会2018」発表資料
Yew, Z. J., & Lee, G. H. (2018). 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. European Conference on Computer Vision.
2020/10/10に開催された第4回全日本コンピュータビジョン勉強会「人に関する認識・理解論文読み会」発表資料です。
以下の2本を読みました
Harmonious Attention Network for Person Re-identification. (CVPR2018)
Weekly Supervised Person Re-Identification (CVPR2019)
From Large Scale Image Categorization to Entry-Level CategoriesTakuya Minagawa
This document summarizes a research paper presented at ICCV2013 titled "From Large Scale Image Categorization to Entry-Level Categories". The paper proposes addressing the problem of providing more natural, entry-level category labels for images rather than the more technical labels output by existing image recognition systems. It explores using text corpora and WordNet hierarchies to map between specific categories and more general entry-level labels, as well as using supervised learning on ImageNet data to directly predict entry-level categories. Examples are given of different approaches for category translation and content naming images at the entry-level.
2020/10/10に開催された第4回全日本コンピュータビジョン勉強会「人に関する認識・理解論文読み会」発表資料です。
以下の2本を読みました
Harmonious Attention Network for Person Re-identification. (CVPR2018)
Weekly Supervised Person Re-Identification (CVPR2019)
From Large Scale Image Categorization to Entry-Level CategoriesTakuya Minagawa
This document summarizes a research paper presented at ICCV2013 titled "From Large Scale Image Categorization to Entry-Level Categories". The paper proposes addressing the problem of providing more natural, entry-level category labels for images rather than the more technical labels output by existing image recognition systems. It explores using text corpora and WordNet hierarchies to map between specific categories and more general entry-level labels, as well as using supervised learning on ImageNet data to directly predict entry-level categories. Examples are given of different approaches for category translation and content naming images at the entry-level.
- The document discusses making a presentation at the UIST conference on HCI topics. It provides background on HCI conferences like CHI, UbiComp, and UIST.
- It lists upcoming HCI conferences in Asia and encourages developing a research question, story, and discussing best papers for a potential UIST presentation.
- It also provides context on HCI research in Japan and different laboratories and companies doing HCI work in areas like IoT, VR, and more.
This document summarizes the organization of UIST 2016, including the chairs and committees. It notes there were 632 attendees, with registration capped at 500. It lists the chairs for various committees like local arrangements, program, doctoral symposium, and student volunteers. The local arrangement chair was Masa Ogata. It discusses the roles and members of the local arrangement, student volunteer, and other committees in organizing the conference.
【ECCV 2016 BNMW】Human Action Recognition without HumanHirokatsu Kataoka
Project page:
http://www.hirokatsukataoka.net/research/withouthuman/withouthuman.html
The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.
To the best of our knowledge, this is the first study of human action recognition without human. However, we should not have done that kind of thing. The motion representation from a background sequence is effective to classify videos in a human action database. We demonstrated human action recognition in with and without a human settings on the UCF101 dataset. The results show the setting without a human (47.42%; without human setting) was close to the setting with a human (56.91%; with human setting). We must accept this reality to realize better motion representation.
Slides by Míriam Bellver at the UPC Reading group for the paper:
Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed. "SSD: Single Shot MultiBox Detector." ECCV 2016.
Full listing of papers at:
https://github.com/imatge-upc/readcv/blob/master/README.md
Intelligence Domain Group, Rakuten Institute of Technology is working on developing various kinds of solutions utilizing Rakuten Data in order to assist Rakuten services.
In this presentation, we cover:
- Item category classification
- Item Attribute extraction using machine learning
- Item Recommender System with distributed representation.
- Item classification with LDA
- Search Assist Systems
- Item Review Analysis
- Time Series Data Analysis
2. 論文
Title :
Joint Inverted Indexing
Author:
Yan Xia, Fang Wen
(University of Science and Technology of China),
Kaiming He, Jian Sun
(Microsoft Research Asia)
2
3. 論文
Title :
Joint Inverted Indexing
学生.多分MSRAでイン
ターンで行った研究
Author:
Yan Xia, Fang Wen
(University of Science and Technology of China),
Kaiming He, Jian Sun
(Microsoft Research Asia)
3
4. 論文
Title :
Joint Inverted Indexing
学生.多分MSRAでイン
ターンで行った研究
Author:
Yan Xia, Fang Wen
(University of Science and Technology of China),
Kaiming He, Jian Sun
Optimized Product
(Microsoft Research Asia)
Quantization の著者の方.
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56. 参考文献(1/2)
MIH [CVPR 12]
M. Norouzi, et al. “Fast Search in Hamming Space with Multi-Index Hashing” CVPR 2012
実はhamming系の手法はこれを使うとめちょ早くなる.ただ速度がクエリに依存する.PAMIるよ
うで,ジャーナルバージョンは M. Norouzi, et al. “Fast Exact Search in Hamming Space with Multi-Index
Hashing” PAMI 2014
SH [NIPS 08]
Y. Weiss, et al. “Spectral Hashing” NIPS 2008
Hamming系の走り.配布コードがシンプルで,みんなこれに合わせている
ITQ [CVPR 11]
Y. Gong, et al. “Iterative Quantization: A Procrustean Approach to Learning Binary Codes” CVPR 2011
Hamming系は一時期みんなこれを比較対象にしていた.
K-means Hashing [CVPR 13]
K. He, et al. “K-means Hashing: an Affinity-Preserving Quantization Method for Learning Binary Compact
Codes” CVPR 2013
イントロでの分野の外観説明が分かり易く,HammingベースとLookupベースの分類はこの論文よ
り.ITQの考え方を推し進めて,Hamming系とLookup系をつなぐ手法だと自分で言っている
PQ [PAMI 11]
H. Jegou, et al. “Product Quantization for Nearest Neighbor Search” PAMI 2011
今回の手法の大元はこのIVFADCなので,取り合えず読む必要がある.実験が恐ろしくしっかりし
ていて辛い気持ちになる
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57. 参考文献(2/2)
IMI [CVPR 12]
A. Babenko and V. Lempitsky “The Inverted Multi-Index” CVPR 2012
IVFADCの粗い量子化をPQに置き換えたもの.この論文でも既存手法としてこいつを相手にしてい
る
What Is [ICCV 13]
M. Iwamura, et al. “What Is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast
Approximate Nearest Neighbor Search?” ICCV 2013
大阪府立大のグループ.IMIの粗いPQ計算の高速化
Optimized PQ [CVPR 13]
T. Ge, et al. “Optimized Product Quantization for Approximate Nearest Neighbor Search” CVPR 2013
PQするときの次元分割をエラー最少でやる最適化.最終的には回転行列を求めるだけになる.
PAMIバージョン(T. Ge, et al. “Optimized Product Quantization” PAMI 2014)ではIMIに対しこれを
行っていて,現状のstate-of-the-art感がある
Cartesian K-means [CVPR 13]
M. Norouzi and D. J. Fleet, “Cartesian k-means” CVPR 2013
Optimized PQと全く同じ会議で全く同じ内容だったという事例.立式が洒落ている.Optimized PQ
のほうが先にPAMIっているので戦略負けしたのか感がある
Random Projection Tree[Freund, STOC 07]
S. Dasgupta and Y. Freund. “Random Projection Trees and Low Dimensional Manifolds” STOC 2007
CVPR13 best paper [Dean 13]
T. Dean, et al. “Fast, Accurate Detection of 100,000 Object Classes on a Single Machine” CVPR 2013
CVPR13のベストペーパーで,機械学習の学習段階でhamming系のANNを利用することで高速に学
習する.なのでよりたくさんの特徴量つっこめるので精度上がるという企業パワー全開な論文.
今後ANNが使われる文脈の一つなのか?
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