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20162022
most citedDifferentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects

27 citations · 70 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.CV2022★ 1 cited

Informative Sample-Aware Proxy for Deep Metric Learning

Aoyu Li, Ikuro Sato, Kohta Ishikawa +2

Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding…

cs.CV2021★ 2 cited

Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach

Ryota Yoshihashi, Rei Kawakami, Shaodi You +3

Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution o…

cs.CV2018★ 18 cited

Hybrid Loss for Learning Single-Image-based HDR Reconstruction

Kenta Moriwaki, Ryota Yoshihashi, Rei Kawakami +2

This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean…

cs.CV2018★ 8 cited

Classification-Reconstruction Learning for Open-Set Recognition

Ryota Yoshihashi, Wen Shao, Rei Kawakami +3

Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes ap…

cs.CV2018★ 4 cited

Cross-connected Networks for Multi-task Learning of Detection and Segmentation

Seiichiro Fukuda, Ryota Yoshihashi, Rei Kawakami +3

Multi-task learning improves generalization performance by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset, while the…

cs.CV2017★ 27 cited

Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects

Ryota Yoshihashi, Tu Tuan Trinh, Rei Kawakami +3

While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging. Moti…

cs.CV2016★ 9 cited

Waterdrop Stereo

Shaodi You, Robby T. Tan, Rei Kawakami +2

This paper introduces depth estimation from water drops. The key idea is that a single water drop adhered to window glass is totally transparent and convex, and thus optically acts…