Results 271 to 280 of about 52,386 (322)

Word Image Retrieval Using Bag of Visual Words

2012 10th IAPR International Workshop on Document Analysis Systems, 2012
This paper presents a Bag of Visual Words (BoVW) based approach to retrieve similar word images from a large database, efficiently and accurately. We show that a text retrieval system can be adapted to build a word image retrieval solution. This helps in achieving scalability.
Ravi Shekhar, C. V. Jawahar
openaire   +1 more source

CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally

arXiv.org
CLIP (Contrastive Language-Image Pretraining) has become a popular choice for various downstream tasks. However, recent studies have questioned its ability to represent compositional concepts effectively.
Darina Koishigarina, Arnas Uselis, S. Oh
semanticscholar   +1 more source

Understanding bag-of-words model: a statistical framework

International Journal of Machine Learning and Cybernetics, 2010
Rong Jin, Zhi-Hua Zhou, Zhou Zhi-Hua
exaly   +2 more sources

Bags of Trajectory Words for video indexing

2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), 2014
A semantic indexing system capable of detecting both spatial appearance and motion-related semantic concepts requires the use of both spatial and motion descriptors. However, extracting motion descriptors on very large video collections requires great computational resources, which has caused most approaches to limit themselves to a spatial description.
Sabin Tiberius Strat, Alexandre Benoît, Patrick Lambert   +2 more
openaire   +1 more source

Comparing Bag-of-Words, SBERT, and GPT-3 for Bias Detection

Journal of student-scientists' research
This project aims to detect bias in media by training a machine learning model to recognize biased sentences. We did this by using a dataset containing 3700 sentences each annotated by multiple experts.
Max Luo, Clayton Greenberg
semanticscholar   +1 more source

Distinguish Polarity in Bag-of-Words Visualization

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We
Yusheng Xie, Zhengzhang Chen, Ankit Agrawal 0001, Alok N. Choudhary   +3 more
openaire   +1 more source

Accelerating Bag-of-Words with SOM

2019
We propose a fast Bag-of-Words (BoW) method for image classification, inspired by the mechanism that arrangement of neurons in visual cortex can preserve the topology of mapping from inputs, and the fact that human brain can retrieve information almost instantly.
Jian-Hui Chen, Zuo-Ren Wang, Cheng-Lin Liu   +2 more
openaire   +1 more source

An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges

International Enformatika Conference, 2019
In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot ...
W. Qader, M. M. Ameen, Bilal Ahmed
semanticscholar   +1 more source

Verdict Prediction for Indian Courts Using Bag of Words and Convolutional Neural Network

2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020
All courts in India publish judgment by statistically analyzing the data of different cases and understanding the verdict from precedents judgments and the statute law.
V. Pillai, L. R. Chandran
semanticscholar   +1 more source

Spatial extensions to bag of visual words

Proceedings of the ACM International Conference on Image and Video Retrieval, 2009
The Bag of Visual Words (BoV) paradigm has successfully been applied to image content analysis tasks such as image classification and object detection. The basic BoV approach overlooks spatial descriptor distribution within images. Here we describe spatial extensions to BoV and experimentally compare them in the VOC2007 benchmark image category ...
Ville Viitaniemi, Jorma Laaksonen
openaire   +2 more sources