Arnaud Dapogny

563 total citations
22 papers, 242 citations indexed

About

Arnaud Dapogny is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Artificial Intelligence. According to data from OpenAlex, Arnaud Dapogny has authored 22 papers receiving a total of 242 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 8 papers in Experimental and Cognitive Psychology and 7 papers in Artificial Intelligence. Recurrent topics in Arnaud Dapogny's work include Face recognition and analysis (8 papers), Emotion and Mood Recognition (7 papers) and Face and Expression Recognition (7 papers). Arnaud Dapogny is often cited by papers focused on Face recognition and analysis (8 papers), Emotion and Mood Recognition (7 papers) and Face and Expression Recognition (7 papers). Arnaud Dapogny collaborates with scholars based in France, United States and Hungary. Arnaud Dapogny's co-authors include Kévin Bailly, Séverine Dubuisson, Matthieu Cord, Yifu Chen, David Cohen, Stéphanie Hun, Sylvie Serret, Charline Grossard, Hugues Pellerin and Laurence Chaby and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Frontiers in Psychology.

In The Last Decade

Arnaud Dapogny

22 papers receiving 237 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arnaud Dapogny France 9 131 97 69 30 17 22 242
Enrique Sánchez United Kingdom 8 134 1.0× 133 1.4× 36 0.5× 39 1.3× 19 1.1× 17 262
Samuel Murray United States 9 117 0.9× 73 0.8× 167 2.4× 34 1.1× 16 0.9× 29 338
Salma Kammoun Jarraya Saudi Arabia 11 123 0.9× 51 0.5× 109 1.6× 54 1.8× 7 0.4× 33 309
Eliane Pozzebon Brazil 7 89 0.7× 119 1.2× 44 0.6× 58 1.9× 4 0.2× 36 283
Hrishikesh Rao United States 6 75 0.6× 33 0.3× 103 1.5× 23 0.8× 14 0.8× 13 235
Myriam Desainte‐Catherine France 6 58 0.4× 96 1.0× 70 1.0× 17 0.6× 18 1.1× 24 194
Mohammad Mavadati United States 4 92 0.7× 144 1.5× 92 1.3× 55 1.8× 16 0.9× 8 279
Behnaz Nojavanasghari United States 5 108 0.8× 105 1.1× 45 0.7× 134 4.5× 11 0.6× 5 285
Maurizio Garbarino Italy 3 34 0.3× 76 0.8× 66 1.0× 46 1.5× 25 1.5× 3 218

Countries citing papers authored by Arnaud Dapogny

Since Specialization
Citations

This map shows the geographic impact of Arnaud Dapogny'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 Arnaud Dapogny with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arnaud Dapogny more than expected).

Fields of papers citing papers by Arnaud Dapogny

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arnaud Dapogny. 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 Arnaud Dapogny. The network helps show where Arnaud Dapogny may publish in the future.

Co-authorship network of co-authors of Arnaud Dapogny

This figure shows the co-authorship network connecting the top 25 collaborators of Arnaud Dapogny. A scholar is included among the top collaborators of Arnaud Dapogny 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 Arnaud Dapogny. Arnaud Dapogny is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dapogny, Arnaud, et al.. (2024). PIPE: Parallelized inference through ensembling of residual quantization expansions. Pattern Recognition. 154. 110571–110571. 1 indexed citations
2.
Dapogny, Arnaud, et al.. (2023). SPIQ: Data-Free Per-Channel Static Input Quantization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3858–3867. 5 indexed citations
3.
Dapogny, Arnaud, et al.. (2023). Fighting Over-Fitting with Quantization for Learning Deep Neural Networks on Noisy Labels. 575–579. 3 indexed citations
4.
Dapogny, Arnaud, et al.. (2022). RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(3). 3664–3676. 9 indexed citations
5.
Dapogny, Arnaud, et al.. (2022). Multi-Order Networks for Action Unit Detection. IEEE Transactions on Affective Computing. 14(4). 2876–2888. 8 indexed citations
6.
Dapogny, Arnaud, et al.. (2022). THIN: THrowable Information Networks and Application for Facial Expression Recognition in the Wild. IEEE Transactions on Affective Computing. 14(3). 2336–2348. 23 indexed citations
7.
Dapogny, Arnaud, et al.. (2022). Privileged Attribution Constrained Deep Networks for Facial Expression Recognition. 2022 26th International Conference on Pattern Recognition (ICPR). 1055–1061. 5 indexed citations
8.
Douillard, Arthur, et al.. (2021). PLOP: Learning without Forgetting for Continual Semantic Segmentation. arXiv (Cornell University). 4039–4049. 4 indexed citations
9.
Grossard, Charline, Arnaud Dapogny, David Cohen, et al.. (2020). Children with autism spectrum disorder produce more ambiguous and less socially meaningful facial expressions: an experimental study using random forest classifiers. Molecular Autism. 11(1). 5–5. 25 indexed citations
10.
Dapogny, Arnaud, Matthieu Cord, & Patrick Pérez. (2020). The Missing Data Encoder: Cross-Channel Image Completion with Hide-and-Seek Adversarial Network. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 10688–10695. 4 indexed citations
11.
Chen, Yifu, Arnaud Dapogny, & Matthieu Cord. (2020). SEMEDA: Enhancing segmentation precision with semantic edge aware loss. Pattern Recognition. 108. 107557–107557. 24 indexed citations
12.
Grossard, Charline, Stéphanie Hun, Arnaud Dapogny, et al.. (2019). Teaching Facial Expression Production in Autism: The Serious Game JEMImE. Creative Education. 10(11). 2347–2366. 15 indexed citations
13.
Chen, Yifu, et al.. (2019). Delving Deep into Interpreting Neural Nets with Piece-Wise Affine Representation. HAL (Le Centre pour la Communication Scientifique Directe). 609–613. 1 indexed citations
14.
Dapogny, Arnaud, Charline Grossard, Stéphanie Hun, et al.. (2019). On Automatically Assessing Children's Facial Expressions Quality: A Study, Database, and Protocol. Frontiers in Computer Science. 1. 8 indexed citations
15.
Grossard, Charline, Laurence Chaby, Stéphanie Hun, et al.. (2018). Children Facial Expression Production: Influence of Age, Gender, Emotion Subtype, Elicitation Condition and Culture. Frontiers in Psychology. 9. 446–446. 28 indexed citations
16.
Dapogny, Arnaud & Kévin Bailly. (2018). Investigating Deep Neural Forests for Facial Expression Recognition. 629–633. 9 indexed citations
17.
Grossard, Charline, Stéphanie Hun, Sylvie Serret, et al.. (2017). Rééducation de l’expression émotionnelle chez l’enfant avec trouble du spectre autistique grâce aux supports numériques : le projet JEMImE. Neuropsychiatrie de l Enfance et de l Adolescence. 65(1). 21–32. 7 indexed citations
18.
Dapogny, Arnaud, et al.. (2017). Sequential recognition of in-hand object shape using a collection of neural forests. 3081–3086. 2 indexed citations
19.
Dapogny, Arnaud, Kévin Bailly, & Séverine Dubuisson. (2017). Multi-Output Random Forests for Facial Action Unit Detection. 45. 135–140. 3 indexed citations
20.
Dapogny, Arnaud, Kévin Bailly, & Séverine Dubuisson. (2016). Confidence-Weighted Local Expression Predictions for Occlusion Handling\n in Expression Recognition and Action Unit detection. arXiv (Cornell University). 53 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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