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| Author |
Akhil Gurram; Antonio Lopez |
| Title |
On the Metrics for Evaluating Monocular Depth Estimation |
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Miscellaneous |
| Year |
2023 |
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Arxiv |
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Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose. |
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ADAS |
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no |
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Admin @ si @ GuL2023 |
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3867 |
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| Author |
David Guillamet; B. Moghaddam |
| Title |
Joint Distribution of Local Image Features for Appearance Moldeling. |
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2002 |
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Proceedings of the IAPR Workshop on Machine Vision Applications MVA 2002. |
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no |
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Admin @ si @ GuM2002 |
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293 |
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| Author |
R. Herault; Franck Davoine; Fadi Dornaika; Y. Grandvalet |
| Title |
Simultaneous and robust face and facial action tracking |
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2006 |
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15eme Congres Francophone AFRIF–AFIA de Reconnaissance des Formes et Intelligence Artificielle (RFIA´06) |
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Tours (France) |
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Admin @ si @ HDD2006 |
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735 |
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| Author |
Luis Herranz; Weiqing Min; Shuqiang Jiang |
| Title |
Food recognition and recipe analysis: integrating visual content, context and external knowledge |
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Miscellaneous |
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2018 |
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Arxiv |
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The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions. |
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LAMP; 600.120 |
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no |
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Admin @ si @ HMJ2018 |
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3250 |
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| Author |
Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane |
| Title |
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
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Miscellaneous |
| Year |
2024 |
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Arxiv |
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We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. |
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HUPBA |
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no |
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Admin @ si @ HPF2024 |
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4021 |
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| Author |
Laura Igual; Xavier Baro |
| Title |
Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación |
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Miscellaneous |
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2013 |
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Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación, de las XIX Jornadas sobre la Enseñanza Universitaria de la Informática |
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JENUI |
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OR;HuPBA;MV |
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no |
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Admin @ si @ IgB2013 |
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2257 |
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| Author |
Zhong Jin; Franck Davoine; Zhen Lou |
| Title |
Facial expression analysis by using KPCA |
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Miscellaneous |
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2003 |
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IEEE International Conference on Robotics, Intelligent Systems and Signal Processing (IEEE RISSP 2003), pp736–741 |
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Changsha, Hunan, China |
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Admin @ si @ JDL2003 |
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431 |
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| Author |
Zhong Jin; Franck Davoine; Zhen Lou |
| Title |
An Effective EM Algorithm for PCA Mixture Model |
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Miscellaneous |
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2004 |
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Structural and Statistical Pattern Recognition, Lecture Notes in Computer Science, 3138:626–634 |
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Lisbon, Portugal |
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no |
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Admin @ si @ JDL2004 |
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482 |
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| Author |
Zhong Jin; Franck Davoine |
| Title |
Orthogonal ICA Representation Of Images |
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Miscellaneous |
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2004 |
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8th International Conference on Control, Automation, Robotics and Vision, 369–374 |
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Kunming (China) |
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no |
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Admin @ si @ JiD2004 |
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499 |
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| Author |
Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier |
| Title |
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos |
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Miscellaneous |
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2018 |
Publication |
Arxiv |
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Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. |
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HUPBA |
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no |
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Admin @ si @ JKS2018 |
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3095 |
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| Author |
Zhong Jin; Zhen Lou; Jing-Yu Yang; Quan-sen Sun |
| Title |
Face detection using template matching and skin color information |
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Miscellaneous |
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2005 |
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International Conference on Intelligent Computing, 636–645 |
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Hefei (China) |
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no |
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Admin @ si @ JLY2005 |
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627 |
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| Author |
Carme Julia |
| Title |
Missig Data Matrix Factorization Addressing the Structure from Motion Problem |
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Miscellaneous |
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2008 |
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CVC–UAB |
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Bellaterra |
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978–84–935251–6–3 |
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no |
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Admin @ si @ Jul2008 |
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1104 |
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| Author |
Zhong Jin; Jing-Yu Yang; Zhen Lou |
| Title |
A luminance-conditional distribution model of skin color information |
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Miscellaneous |
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2005 |
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2005 Beijing International Conference on Imaging: Technology and Applications for the 21th Century, 280–281 |
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Beijing (China) |
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no |
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Admin @ si @ JYL2005 |
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628 |
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Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig |
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CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. |
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Miscellaneous |
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2018 |
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Arxiv |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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MILAB; no proj |
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no |
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Admin @ si @ KJR2018 |
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3235 |
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Mert Kilickaya; Joost van de Weijer; Yuki M. Asano |
| Title |
Towards Label-Efficient Incremental Learning: A Survey |
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Miscellaneous |
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2023 |
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Arxiv |
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The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: this https URL. |
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LAMP |
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Admin @ si @ KWA2023 |
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3994 |
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