Search Results for author: John R. Smith

Found 12 papers, 4 papers with code

Covering the News with (AI) Style

no code implementations5 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).

A Broader Study of Cross-Domain Few-Shot Learning

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

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

1 code implementation30 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.

General Classification

Automatic Curation of Golf Highlights using Multimodal Excitement Features

no code implementations22 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.

Action Recognition Retrieval +2

Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images

no code implementations14 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%).

Specificity Test

Top Rank Supervised Binary Coding for Visual Search

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.

Image Retrieval

Oracle performance for visual captioning

1 code implementation14 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.

Image Captioning Language Modelling +1

Learning Locally-Adaptive Decision Functions for Person Verification

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.

Face Verification Metric Learning +2

Designing Category-Level Attributes for Discriminative Visual Recognition

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.

Attribute Transfer Learning +1

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