Showing papers by "Matthew Turk published in 2020"

Recognizing Families In the Wild (RFIW): The 4th Edition

Abstract: Recognizing Families In the Wild (RFIW)– an annual large-scale, multi-track automatic kinship recognition evaluation– supports various visual kin-based problems on scales much higher than ever before. Organized in conjunction with the as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.

Recognizing Families In the Wild (RFIW): The 4th Edition

Abstract: Recognizing Families In the Wild (RFIW)– an annual large-scale, multi-track automatic kinship recognition evaluation– supports various visual kin-based problems on scales much higher than ever before Organized in conjunction with the as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps This paper summarizes the supported tasks (ie, kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results Furthermore, top submissions (ie, leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions

BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images

Abstract: Real visual-world datasets tend to have few classes with large numbers of samples (i.e., head classes) and many others with smaller numbers of samples (i.e., tail classes). Unfortunately, this imbalance enables a visual recognition system to perform well on head classes but poorly on tail classes. To alleviate this imbalance, we present BLT, a novel data augmentation technique that generates extra training samples for tail classes to improve the generalization performance of a classifier. Unlike prior long-tail approaches that rely on generative models (e.g., GANs or VQ-VAEs) to augment a dataset, BLT uses a gradient-ascent-based image generation algorithm that requires significantly less training time and computational resources. BLT avoids the use of dedicated generative networks, which adds significant computational overhead and require elaborate training procedures. Our experiments on natural and synthetic long-tailed datasets and across different network architectures demonstrate that BLT consistently improves the average classification performance of tail classes by \(11\%\) w.r.t. the common approach that balances the dataset by oversampling tail-class images. BLT maintains the accuracy on head classes while improving the performance on tail classes.

Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge.

Abstract: Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before. Organized in conjunction with the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG) as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.