451 citations · 1k across the 33 of their papers we have counts for
35 papers
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…
Neural Structure Fields with Application to Crystal Structure Autoencoders
Naoya Chiba, Yuta Suzuki, Tatsunori Taniai +4
Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure es…
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…
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi +1
This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used…
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…
Crowd Density Forecasting by Modeling Patch-based Dynamics
Hiroaki Minoura, Ryo Yonetani, Mai Nishimura +1
Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous dr…
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…
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
Yang Li, Yoshitaka Ushiku, Tatsuya Harada
Unsupervised Learning based monocular visual odometry (VO) has lately drawn significant attention for its potential in label-free leaning ability and robustness to camera parameter…
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation
Kohei Watanabe, Kuniaki Saito, Yoshitaka Ushiku +1
Most contemporary robots have depth sensors, and research on semantic segmentation with RGBD images has shown that depth images boost the accuracy of segmentation. Since it is time…
Conditional Video Generation Using Action-Appearance Captions
Shohei Yamamoto, Antonio Tejero-de-Pablos, Yoshitaka Ushiku +1
The field of automatic video generation has received a boost thanks to the recent Generative Adversarial Networks (GANs). However, most existing methods cannot control the contents…
Strong-Weak Distribution Alignment for Adaptive Object Detection
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada +1
We propose an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detecti…
Generating Easy-to-Understand Referring Expressions for Target Identifications
Mikihiro Tanaka, Takayuki Itamochi, Kenichi Narioka +3
This paper addresses the generation of referring expressions that not only refer to objects correctly but also let humans find them quickly. As a target becomes relatively less sal…
Label-Noise Robust Generative Adversarial Networks
Takuhiro Kaneko, Yoshitaka Ushiku, Tatsuya Harada
Generative adversarial networks (GANs) are a framework that learns a generative distribution through adversarial training. Recently, their class-conditional extensions (e.g., condi…
Class-Distinct and Class-Mutual Image Generation with GANs
Takuhiro Kaneko, Yoshitaka Ushiku, Tatsuya Harada
Class-conditional extensions of generative adversarial networks (GANs), such as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered attention owing to their…
Visual Question Generation for Class Acquisition of Unknown Objects
Kohei Uehara, Antonio Tejero-De-Pablos, Yoshitaka Ushiku +1
Traditional image recognition methods only consider objects belonging to already learned classes. However, since training a recognition model with every object class in the world i…
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering
Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada
Image description task has been invariably examined in a static manner with qualitative presumptions held to be universally applicable, regardless of the scope or target of the des…
Viewpoint-aware Video Summarization
Atsushi Kanehira, Luc Van Gool, Yoshitaka Ushiku +1
This paper introduces a novel variant of video summarization, namely building a summary that depends on the particular aspect of a video the viewer focuses on. We refer to this as…
Open Set Domain Adaptation by Backpropagation
Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku +1
Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for a closed-…
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku +1
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a sou…