Daiki Ikami

1.1k total citations · 1 hit paper
12 papers, 528 citations indexed

About

Daiki Ikami is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Daiki Ikami has authored 12 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 2 papers in Computational Mechanics. Recurrent topics in Daiki Ikami's work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Face and Expression Recognition (3 papers). Daiki Ikami is often cited by papers focused on Domain Adaptation and Few-Shot Learning (5 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Face and Expression Recognition (3 papers). Daiki Ikami collaborates with scholars based in Japan and United States. Daiki Ikami's co-authors include Kiyoharu Aizawa, Toshihiko Yamasaki, Daiki Tanaka, Shota Horiguchi, Go Irie, Takashi Shibata, Akari Asai and Qing Yu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and Computer Vision and Pattern Recognition.

In The Last Decade

Daiki Ikami

12 papers receiving 514 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Joint Optimization Framework for Learning with Noisy Labels

2018 400 citations Daiki Tanaka, Daiki Ikami et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daiki Ikami Japan	8	386	243	48	44	27	12	528
Himanshu Kumar India	6	432 1.1×	252 1.0×	59 1.2×	35 0.8×	39 1.4×	16	623
Eric Arazo Ireland	6	380 1.0×	264 1.1×	21 0.4×	36 0.8×	27 1.0×	10	568
Diego Ortego Spain	8	400 1.0×	333 1.4×	21 0.4×	33 0.8×	27 1.0×	14	635
Masanori Suganuma Japan	8	379 1.0×	400 1.6×	33 0.7×	27 0.6×	20 0.7×	25	711
Michał Koziarski Poland	10	304 0.8×	104 0.4×	15 0.3×	59 1.3×	16 0.6×	20	546
Haifeng Xia United States	14	350 0.9×	286 1.2×	48 1.0×	10 0.2×	13 0.5×	30	531
Arūnas Lipnickas Lithuania	9	141 0.4×	119 0.5×	36 0.8×	66 1.5×	28 1.0×	43	375
Yuanyi Zhong United States	8	185 0.5×	268 1.1×	22 0.5×	15 0.3×	38 1.4×	10	392
Jiongcheng Li China	7	166 0.4×	117 0.5×	22 0.5×	16 0.4×	12 0.4×	12	344

Countries citing papers authored by Daiki Ikami

Since Specialization

Citations

This map shows the geographic impact of Daiki Ikami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daiki Ikami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daiki Ikami more than expected).

Fields of papers citing papers by Daiki Ikami

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daiki Ikami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daiki Ikami. The network helps show where Daiki Ikami may publish in the future.

Co-authorship network of co-authors of Daiki Ikami

This figure shows the co-authorship network connecting the top 25 collaborators of Daiki Ikami. A scholar is included among the top collaborators of Daiki Ikami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daiki Ikami. Daiki Ikami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Yu, Qing, et al.. (2023). Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2808–2817. 3 indexed citations

2.

Ikami, Daiki, et al.. (2022). Self-Labeling Framework for Novel Category Discovery over Domains. Proceedings of the AAAI Conference on Artificial Intelligence. 36(3). 3161–3169. 15 indexed citations

3.

Asai, Akari, et al.. (2021). The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. 1438–1445. 5 indexed citations

4.

Irie, Go, et al.. (2021). Generalized Domain Adaptation. 1084–1093. 13 indexed citations

5.

Shibata, Takashi, et al.. (2021). Learning with Selective Forgetting. 989–996. 20 indexed citations

6.

Asai, Akari, Daiki Ikami, & Kiyoharu Aizawa. (2019). Multi-Task Learning based on Separable Formulation of Depth Estimation and its Uncertainty. Computer Vision and Pattern Recognition. 21–24. 3 indexed citations

7.

Ikami, Daiki, et al.. (2019). Synthesis of Screentone Patterns of Manga Characters. 212–215. 8 indexed citations

8.

Horiguchi, Shota, Daiki Ikami, & Kiyoharu Aizawa. (2019). Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(5). 1–1. 44 indexed citations

9.

Tanaka, Daiki, Daiki Ikami, Toshihiko Yamasaki, & Kiyoharu Aizawa. (2018). Joint Optimization Framework for Learning with Noisy Labels. 5552–5560. 400 indexed citations breakdown →

10.

Ikami, Daiki, Toshihiko Yamasaki, & Kiyoharu Aizawa. (2018). Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems. 8147–8155. 7 indexed citations

11.

Ikami, Daiki, Toshihiko Yamasaki, & Kiyoharu Aizawa. (2018). Local and Global Optimization Techniques in Graph-Based Clustering. 3456–3464. 3 indexed citations

12.

Horiguchi, Shota, Daiki Ikami, & Kiyoharu Aizawa. (2017). Significance of Softmax-Based Features over Metric Learning-Based Features. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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