25 citations · 43 across the 20 of their papers we have counts for
21 papers
Hand Visibility Detector: Per-Keypoint Visibility Estimation for Hands
Ryosei Hara, Masashi Hatano, Rintaro Yanagi +3
Hand Pose Estimation (HPE) is a fundamental technology for various applications such as AR/VR and robotics. In these applications, the visibility of each hand joint in the image is…
AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation
Bao Long Nguyen Huu, Atsushi Hashimoto
GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this…
GAVEL: Grounded Caption Error Verification and Localization
Zixian Gao, Atsushi Hashimoto, Kuniaki Saito
Vision-language models (VLMs) often produce hallucinated or inconsistent outputs, where text and images are not properly aligned. Addressing this issue requires not only detecting…
Towards Safer Mobile Agents: Scalable Generation and Evaluation of Diverse Scenarios for VLMs
Takara Taniguchi, Kuniaki Saito, Atsushi Hashimoto
Vision Language Models (VLMs) are increasingly deployed in autonomous vehicles and mobile systems, making it crucial to evaluate their ability to support safer decision-making in c…
Evaluating the Capability of Video Question Generation for Expert Knowledge Elicitation
Huaying Zhang, Atsushi Hashimoto, Tosho Hirasawa
Skilled human interviewers can extract valuable information from experts. This raises a fundamental question: what makes some questions more effective than others? To address this,…
SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts
Shun Inadumi, Shohei Tanaka, Tosho Hirasawa +3
As the number of scientific papers continues to grow, there is a demand for approaches that can effectively convey research findings, with posters serving as a key medium for prese…
SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters
Shohei Tanaka, Atsushi Hashimoto, Yoshitaka Ushiku
Scientific posters play a vital role in academic communication by presenting ideas through visual summaries. Analyzing reading order and parent-child relations of posters is essent…
CaptionSmiths: Flexibly Controlling Language Pattern in Image Captioning
Kuniaki Saito, Donghyun Kim, Kwanyong Park +2
An image captioning model flexibly switching its language pattern, e.g., descriptiveness and length, should be useful since it can be applied to diverse applications. However, desp…
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
Koki Maeda, Tosho Hirasawa, Atsushi Hashimoto +4
Procedural video understanding is gaining attention in the vision and language community. Deep learning-based video analysis requires extensive data. Consequently, existing works o…
AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering
Mahiro Ukai, Shuhei Kurita, Atsushi Hashimoto +2
Visual question answering aims to provide responses to natural language questions given visual input. Recently, visual programmatic models (VPMs), which generate executable program…
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos
Takehiko Ohkawa, Takuma Yagi, Taichi Nishimura +4
We propose a novel benchmark for cross-view knowledge transfer of dense video captioning, adapting models from web instructional videos with exocentric views to an egocentric view.…
WeaveNet for Approximating Two-sided Matching Problems
Shusaku Sone, Jiaxin Ma, Atsushi Hashimoto +2
Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and cons…
A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task
Shiqi Yang, Atsushi Hashimoto, Yoshitaka Ushiku
In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such…
Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition
Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku
To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no con…
Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows
Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura +4
We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn each cooking action result in a recipe text. The dataset consists of object state changes a…
Recipe Generation from Unsegmented Cooking Videos
Taichi Nishimura, Atsushi Hashimoto, Yoshitaka Ushiku +2
This paper tackles recipe generation from unsegmented cooking videos, a task that requires agents to (1) extract key events in completing the dish and (2) generate sentences for th…
3D Point Cloud Registration with Learning-based Matching Algorithm
Rintaro Yanagi, Atsushi Hashimoto, Shusaku Sone +3
We present a novel differential matching algorithm for 3D point cloud registration. Instead of only optimizing the feature extractor for a matching algorithm, we propose a learning…
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto +2
Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different environments, adapting a pre-trained segment…
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
Ukyo Honda, Yoshitaka Ushiku, Atsushi Hashimoto +2
Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from…
Divergence Optimization for Noisy Universal Domain Adaptation
Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku
Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the l…
Decentralized Learning of Generative Adversarial Networks from Non-iid Data
Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto +1
This work addresses a new problem that learns generative adversarial networks (GANs) from multiple data collections that are each i) owned separately by different clients and ii) d…