1 code implementation • 8 Jul 2022 • Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
no code implementations • 5 Jan 2020 • Michele Merler, Cicero Nogueira dos santos, Mauro Martino, Alfio M. Gliozzo, John R. Smith
We introduce a multi-modal discriminative and generative frame-work capable of assisting humans in producing visual content re-lated to a given theme, starting from a collection of documents(textual, visual, or both).
2 code implementations • ECCV 2020 • Yunhui Guo, Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, Rogerio Feris
Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.
cross-domain few-shot learning Few-Shot Image Classification +1
no code implementations • 29 Jan 2019 • Michele Merler, Nalini Ratha, Rogerio S. Feris, John R. Smith
We expect face recognition to work equally accurately for every face.
1 code implementation • 30 May 2018 • Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogerio Feris, John R. Smith
Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.
no code implementations • 22 Jul 2017 • Michele Merler, Dhiraj Joshi, Quoc-Bao Nguyen, Stephen Hammer, John Kent, John R. Smith, Rogerio S. Feris
The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media.
no code implementations • 14 Oct 2016 • Noel Codella, Quoc-Bao Nguyen, Sharath Pankanti, David Gutman, Brian Helba, Allan Halpern, John R. Smith
Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70. 5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).
no code implementations • ICCV 2015 • Dongjin Song, Wei Liu, Rongrong Ji, David A. Meyer, John R. Smith
In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information.
1 code implementation • 14 Nov 2015 • Li Yao, Nicolas Ballas, Kyunghyun Cho, John R. Smith, Yoshua Bengio
The task of associating images and videos with a natural language description has attracted a great amount of attention recently.
no code implementations • CVPR 2014 • Wei Liu, Gang Hua, John R. Smith
Outliers are pervasive in many computer vision and pattern recognition problems.
no code implementations • CVPR 2013 • Zhen Li, Shiyu Chang, Feng Liang, Thomas S. Huang, Liangliang Cao, John R. Smith
This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule.
no code implementations • CVPR 2013 • Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, Shih-Fu Chang
In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix.