11 papers
S3OD: Towards Generalizable Salient Object Detection with Synthetic Data
Orest Kupyn, Hirokatsu Kataoka, Christian Rupprecht
Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present…
FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation
Yukinori Yamamoto, Kazuya Nishimura, Tsukasa Fukusato +3
Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privacy constraints and the high cost…
3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds
Ryousuke Yamada, Kohsuke Ide, Yoshihiro Fukuhara +4
Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D represe…
Constant Rate Scheduling: A General Framework for Optimizing Diffusion Noise Schedule via Distributional Change
Shuntaro Okada, Kenji Doi, Ryota Yoshihashi +2
We propose a general framework for optimizing noise schedules in diffusion models, applicable to both training and sampling. Our method enforces a constant rate of change in the pr…
AgroBench: Vision-Language Model Benchmark in Agriculture
Risa Shinoda, Nakamasa Inoue, Hirokatsu Kataoka +2
Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs)…
AnimalClue: Recognizing Animals by their Traces
Risa Shinoda, Nakamasa Inoue, Iro Laina +2
Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Rec…