2 citations · 3 across the 10 of their papers we have counts for
13 papers
DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction
Fan Zhu, Zhisong Xu, Ziyu Chen +2
RGB-D SLAM requires accurate online tracking, high-quality rendering, and geometrically consistent reconstruction. Existing Gaussian SLAM methods mainly optimize Gaussian primitive…
Medical-Checklist: Assessing the Comprehension of Medical Images by Multimodal Models
Bannapol Limanond, Masanori Suganuma, Takayuki Okatani
This paper introduces a new benchmark test, Medical-Checklist, for assessing medical multimodal models. The recent advancements in multimodal models have demonstrated significant p…
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…
Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method
Yan Zeng, Masanori Suganuma, Takayuki Okatani
This paper studies the problem of inverting the DDIM image generation process to recover latent variables, particularly the initial noise map, from a generated image. Existing meth…
TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/Videos
Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma +4
The application of Multi-modal Large Language Models (MLLMs) in Autonomous Driving (AD) faces significant challenges due to their limited training on traffic-specific data and the…
RP-SLAM: Real-time Photorealistic SLAM with Efficient 3D Gaussian Splatting
Lizhi Bai, Chunqi Tian, Jun Yang +3
3D Gaussian Splatting has emerged as a promising technique for high-quality 3D rendering, leading to increasing interest in integrating 3DGS into realism SLAM systems. However, exi…
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…
Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images
Xiangyong Lu, Masanori Suganuma, Takayuki Okatani
In real-world applications of image recognition tasks, such as human pose estimation, cameras often capture objects, like human bodies, at low resolutions. This scenario poses a ch…
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…
An Improved Method for Personalizing Diffusion Models
Yan Zeng, Masanori Suganuma, Takayuki Okatani
Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization usin…
SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers
Xiangyong Lu, Masanori Suganuma, Takayuki Okatani
Computer vision has become increasingly prevalent in solving real-world problems across diverse domains, including smart agriculture, fishery, and livestock management. These appli…
Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction
Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma +3
This paper addresses the problem of predicting hazards that drivers may encounter while driving a car. We formulate it as a task of anticipating impending accidents using a single…
Symmetry-aware Neural Architecture for Embodied Visual Navigation
Shuang Liu, Takayuki Okatani
Visual exploration is a task that seeks to visit all the navigable areas of an environment as quickly as possible. The existing methods employ deep reinforcement learning (RL) as t…