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- Hiromichi FujisawaSuggested from co-authorship
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Works530 from public data
- 2,390
- Dense Trajectories and Motion Boundary Descriptors for Action Recognition1,800
The MBH descriptor shows to consistently outperform other state-of-the-art descriptors, in particular on real-world videos that contain a significant amount of camera motion.
- 622
- Handwritten digit recognition: benchmarking of state-of-the-art techniques586
The results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques are competitive to the best ones previously reported on the same databases.
- 550
- 538
- 535
- Robust Classification with Convolutional Prototype Learning479
This paper argues that the lack of robustness for CNN is caused by the softmax layer, which is a totally discriminative model and based on the assumption of closed world (i.e., with a fixed number of categories), and proposes a novel learning framework called convolutional prototype learning (CPL).
- 455
- Deep Direct Regression for Multi-oriented Scene Text Detection432
A deep direct regression based method for multi-oriented scene text detection that achieves the F-measure of 81%, which is a new state-of-the-art and significantly outperforms previous approaches.
- Drawing and Recognizing Chinese Characters with Recurrent Neural Network363
Under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters and can be recognized by the discriminative RNN model with high accuracy.
- 353
- Character Recognition Systems: A Guide for Students and Practitioners351
This chapter discusses the development of Character Recognition, Evolution and Development, and some of the techniques used to achieve this goal, including Bayes Decision Theory, as well as some new methods based onributed graph matching.
- 339
- Handwritten digit recognition: investigation of normalization and feature extraction techniques332
A comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential and the comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts.
- 329
- From System 1 to System 2: A Survey of Reasoning Large Language Models314
This survey provides an overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs and explores promising directions for advancing reasoning LLMs.
- Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification304
A unified framework to expand short texts based on word embedding clustering and convolutional neural network and semantic cliques via fast clustering is proposed, which validates the effectiveness of the proposed method on two open benchmarks.
- Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark302
Although directMap+convNet can achieve the best results and surpass human-level performance, it is shown that writer adaptation in this case is still effective and a new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer.
- ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019301
The dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge are presented, which has 4 tasks covering various aspects of multi-lingual scene text.
- 238
- 226
- Data-Distortion Guided Self-Distillation for Deep Neural Networks212
A more elegant self-distillation mechanism to transfer knowledge between different distorted versions of same training data without the reliance on accompanying models, which can effectively improve the generalization performance of various network architectures.
- Arbitrary Shape Scene Text Detection With Adaptive Text Region Representation209
Recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found, and text regions of arbitrary shapes are detected and represented with adaptive number of boundary Points.
- 199
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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