activity
20192026
most citedRethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

When Does High-CFG Diffusion Inversion Fail? A Controlled Study of Prompt--Latent Interactions

Yan Zeng, Yusuke Hosoya, Huyen T. T. Tran +1

Text-guided diffusion inversion is central to image editing, where an image is mapped to an initial latent and then edited by replaying the denoising process under a modified promp…

cs.CV2024

Rethinking Annotation for Object Detection: Is Annotating Small-size Instances Worth Its Cost?

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

Detecting objects occupying only small areas in an image is difficult, even for humans. Therefore, annotating small-size object instances is hard and thus costly. This study questi…

cs.CV2024

Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text Describability

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

Open-vocabulary object detection (OVD), detecting specific classes of objects using only their linguistic descriptions (e.g., class names) without any image samples, has garnered s…

cs.CV2022★ 3 cited

Rethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

Open-set object detection (OSOD), a task involving the detection of unknown objects while accurately detecting known objects, has recently gained attention. However, we identify a…

cs.CV2019★ 1 cited

Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

The employment of convolutional neural networks has led to significant performance improvement on the task of object detection. However, when applying existing detectors to continu…