VLDB 2026 Research / reviewers in the wild / expert
Yusuke Iwasawa
dblp:117/7377
· DBLP profile ↗
43ranked-venue papers
5as first author
35since 2021 · last 2026
0000-0002-1321-2622ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 37 · 4 first-author · 31 since 2021Graphics, computer vision, multimedia, augmented reality and games · 9 · 2 first-author · 6 since 2021Systems, architecture and hardware · 3 · 3 since 2021Databases, data management, data science and information retrieval · 3 · 1 since 2021Human-computer interaction and ubiquitous computing · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Understanding Emergent Misalignment via Feature Superposition GeometryabstractEmergent misalignment, where fine-tuning on narrow, non-harmful tasks induces harmful behaviors, poses a key challenge for AI safety in LLMs.Despite growing empirical evidence, its underlying mechanism remains unclear.To uncover the reason behind this phenomenon, we propose a geometric account based on the geometry of feature superposition.Because features are encoded in overlapping representations, fine-tuning that amplifies a target feature also unintentionally strengthens nearby harmful features in accordance with their similarity.We give a simple gradient-level derivation of this effect and empirically test it in multiple LLMs (Gemma-2 2B/9B/27B, LLaMA-3.1 8B, gpt-oss 20B).Using sparse autoencoders (SAEs), we identify features tied to misalignment-inducing data and to harmful behaviors, and show that they are geometrically closer to each other than features derived from non-inducing data.This trend generalizes across domains (e.g., health, career, legal advice).Finally, we show that a geometry-aware approach-filtering training samples closest to toxic features-reduces misalignment by 34.5%, substantially outperforming random removal and achieving comparable or slightly lower misalignment than LLM-as-ajudge-based filtering.Our study links emergent misalignment to feature superposition, providing a basis for understanding and mitigating this phenomenon.This paper analyzes datasets and model outputs that include offensive language. Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo |
ACL (1) | 4 |
| 2026 | Automated Refinement of Essay Scoring Rubrics for Language Models via Reflect-and-ReviseabstractLarge Language Models (LLMs) are increasingly used for Automated Essay Scoring (AES), yet the scoring rubrics they rely on are typically designed for human raters and may not be optimal for LLMs.Inspired by the calibration process that human raters undergo before formal scoring, we propose Reflect-and-Revise, an iterative framework that refines scoring rubrics by prompting models to reflect on their own chain-of-thought rationales and score discrepancies with human labels.At each iteration, the model identifies scoring-error patterns from sampled mismatches and revises the rubric accordingly.Experiments on three essay scoring benchmarks (ASAP, ASAP 2.0, and TOEFL11) with three LLMs (GPT-5 mini, Gemini 3 Flash, and Qwen3-Next-80B-A3B-Instruct) demonstrate that our method yields improvements in Quadratic Weighted Kappa (QWK), achieving gains of up to +0.403 over human-authored rubrics.Starting from a minimal seed rubric that specifies only the score scale, our method matches or exceeds expert rubric performance in most dataset-model combinations, indicating that iterative refinement can reduce the manual effort of rubric authoring.Analysis of the refined rubrics reveals that the refinement process introduces explicit procedural structures, such as conditional gating rules and quantitative thresholds, that are absent from humanauthored rubrics, highlighting a gap between rubrics designed for human raters and those effective for LLMs. 1 Keno Harada, Lui Yoshida, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo |
CoNLL | 4 |
| 2026 | One-shot Portrait Stylization via Geometric AlignmentabstractPortrait stylization casts vivid artistic style drawn from style examples to portrait photos. Although recently extensively studied with machine learning algorithms, existing methods still face challenges in stylizing portraits from a single style reference, severely limiting their potential for real-world applications. In this paper, we propose a portrait stylization method that learns style reference from a single artistic portrait image. Unlike previous StyleGAN based methods that heavily rely on the quality of GAN inversion or diffusion based methods that introduce computational expensive operations and fall short of precise control, our method achieves high-quality stylization with small computation and parameter budget. Specifically, we employ geometric alignment to build spatial correlation between content images and style reference. A geometry LoRA and a style LoRA are then jointly optimized based on a pre-trained diffusion backbone respectively, with orthogonal adaptation used to disentangle the geometry and style information. During inference, the style LoRA is integrated into the diffusion backbone and ControlNet is further combined to facilitate better spatial and identity control. We illustrate abundant stylized portraits with multiple styles. Qualitative comparison, quantitative validation and user study prove that our method outperforms existing methods, and ablation study demonstrates the effectiveness of each components. Zilin Guo, Zhuoru Li, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo |
WACV | 6 |
| 2026 | LLM-Guided Adaptive Compensator: Bringing Adaptivity to Robotic Feedback Control With Large Language ModelabstractRecent advances in code generation and reasoning have enabled the growing use of large language models (LLMs) in robotics. The design of traditional robotic feedback controllers often requires extensive expert knowledge and iterative computational effort, motivating the exploration of LLM-assisted controller development. However, existing efforts are largely restricted to overly simplified systems, provide limited comparison with human-designed controllers, and lack validation on real-world robotic platforms. To address these gaps, this work investigates the use of LLMs in robotic feedback control by focusing on adaptive control and proposing an LLM-guided adaptive compensator. Instead of generating a complete controller from scratch, the LLM is guided to design a compensator that modulates the response of an unknown system to match that of a predefined reference system, thereby achieving adaptivity. The proposed approach is evaluated using five adaptive controllers on a two-degree-of-freedom (DoF) soft robot and a one-DoF shoulder joint of a humanoid robot. Both simulation and real-world experiments demonstrate that the LLM-guided adaptive compensator achieves performance comparable to four conventional adaptive controllers. Lyapunov-based analysis further establishes the stability and generalization capability of the proposed compensator, while analysis of the reasoning process suggests a more structured design approach. This study introduces a novel framework for integrating LLMs into robotic feedback control, with promising practical applicability. Zhongchao Zhou, Yuxi Lu 0002, Yaonan Zhu, Yifei Zhao 0003, Qian Niu, Bin He 0003, Liang He 0007, Wenwei Yu, Yusuke Iwasawa |
IEEE Trans Autom. Sci. Eng. | 9 |
| 2025 | AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM ChatbotsabstractAGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natural language. It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. The flexible design of AGENTiGraph, including intent classification, task planning, and automatic knowledge integration, ensures seamless reasoning between diverse tasks. Evaluated on a 3,500-query benchmark within an educational scenario, the system outperforms strong zero-shot baselines (achieving 95.12% classification accuracy, 90.45% execution success), indicating potential scalability to compliance-critical or multi-step queries in legal and medical domains, e.g., incorporating new statutes or research on the fly. Our open-source demo offers a powerful new paradigm for multi-turn enterprise knowledge management that bridges LLMs and structured graphs. Xinjie Zhao 0004, Moritz Blum, Yingjian Chen, Boming Yang, Luis Marquez-Carpintero, Monica Pina-Navarro, Yanran Fu, So Morikawa, Yusuke Iwasawa, Yutaka Matsuo, Chanjun Park, Irene Li |
CIKM | 10 |
| 2025 | Slender-Mamba: Fully Quantized Mamba in 1.58 Bits From Head to ToeabstractLarge language models (LLMs) have achieved significant performance improvements in natural language processing (NLP) domain. However, these models often require large computational resources for training and inference. Recently, Mamba, a language model architecture based on State-Space Models (SSMs), has achieved comparable performance to Transformer models while significantly reducing costs by compressing context windows during inference. We focused on the potential of the lightweight Mamba architecture by applying BitNet quantization method to the model architecture. In addition, while prior BitNet methods generally quantized only linear layers in the main body, we extensively quantized the embedding and projection layers considering their significant proportion of model parameters. In our experiments, we applied ternary quantization to the Mamba-2 (170M) architecture and pre-trained the model with 150 B tokens from scratch. Our method achieves approximately 90.0% reduction in the bits used by all parameters, achieving a significant improvement compared with a 48.4% reduction by the conventional BitNet quantization method. In addition, our method experienced minimal performance degradation in both the pre-training perplexity and downstream tasks. These findings demonstrate the potential of incorporating lightweight language models into edge devices, which will become more demanding in the future. Zhenxuan Yu, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa |
COLING | 4 |
| 2025 | Image Referenced Sketch Colorization Based on Animation Creation WorkflowabstractSketch colorization plays an important role in animation and digital illustration production tasks. However, existing methods still meet problems in that text-guided methods fail to provide accurate color and style reference, hint-guided methods still involve manual operation, and image-referenced methods are prone to cause artifacts. To address these limitations, we propose a diffusion-based framework inspired by real-world animation production work-flows. Our approach leverages the sketch as the spatial guidance and an RGB image as the color reference, and separately extracts foreground and background from the reference image with spatial masks. Particularly, we introduce a split cross-attention mechanism with LoRA (Low-Rank Adaptation) modules. They are trained separately with foreground and background regions to control the corresponding embeddings for keys and values in cross-attention. This design allows the diffusion model to integrate information from foreground and background independently, preventing interference and eliminating the spatial artifacts. During inference, we design switchable inference modes for diverse use scenarios by changing modules activated in the framework. Extensive qualitative and quantitative experiments, along with user studies, demonstrate our advantages over existing methods in generating high-qualigy artifact-free results with geometric mismatched references. Ablation studies further confirm the effectiveness of each component. Codes are available at https://github.com/tellurion-kanata/colorizeDiffusion. Dingkun Yan, Zhuoru Li, Suguru Saito, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo |
CVPR | 5 |
| 2025 | MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model EvaluationabstractWeihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Weihao Xuan, Rui Yang 0016, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing 0001, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li 0079, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen 0001, Douglas Teodoro, Nan Liu 0003, Randy Goebel, Lei Ma 0003, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li |
EMNLP | 30 |
| 2025 | ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QAabstractZhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Xinjie Zhao 0004, Xingyu Song, Yingjian Chen, Rui Yang 0016, Yanran Fu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li |
EMNLP | 8 |
| 2025 | Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous WordsabstractSparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition of *polysemantic* neurons into *monosemantic* features and composing a sparse dictionary of words.
However, traditional performance metrics like Mean Squared Error and $\mathrm{L}_{0}$ sparsity ignore the evaluation of the semantic representational power of SAEs - whether they can acquire interpretable monosemantic features while preserving the semantic relationship of words.For instance, it is not obvious whether a learned sparse feature could distinguish different meanings in one word.
In this paper, we propose a suite of evaluations for SAEs to analyze the quality of monosemantic features by focusing on polysemous words.
Our findings reveal that SAEs developed to improve the MSE-$\mathrm{L}_0$ Pareto frontier may confuse interpretability, which does not necessarily enhance the extraction of monosemantic features.
The analysis of SAEs with polysemous words can also figure out the internal mechanism of LLMs; deeper layers and the Attention module contribute to distinguishing polysemy in a word.
Our semantics-focused evaluation offers new insights into the polysemy and the existing SAE objective and contributes to the development of more practical SAEs. Gouki Minegishi, Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo |
ICLR | 3 |
| 2025 | Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit EmergenceabstractTransformer-based language models exhibit In-Context Learning (ICL), where predictions are made adaptively based on context. While prior work links induction heads to ICL through a sudden jump in accuracy, this can only account for ICL when the answer is included within the context. However, an important property of practical ICL in large language models is the ability to meta-learn how to solve tasks from context, rather than just copying answers from context; how such an ability is obtained during training is largely unexplored. In this paper, we experimentally clarify how such meta-learning ability is acquired by analyzing the dynamics of the model’s circuit during training. Specifically, we extend the copy task from previous research into an In-Context Meta Learning setting, where models must infer a task from examples to answer queries. Interestingly, in this setting, we find that there are multiple phases in the process of acquiring such abilities, and that a unique circuit emerges in each phase, contrasting with the single-phases change in induction heads. The emergence of such circuits can be related to several phenomena known in large language models, and our analysis lead to a deeper understanding of the source of the transformer’s ICL ability. Gouki Minegishi, Hiroki Furuta, Shohei Taniguchi, Yusuke Iwasawa, Yutaka Matsuo |
ICML | 4 |
| 2025 | A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled RoboticsabstractRecent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey comprehensively summarizes robot control approaches to mitigate physical risks by covering all the lifespan of FMRs ranging from pre-deployment to post-accident stage. Specifically, we broadly divide the timeline into the following three phases: (1) pre-deployment phase, (2) pre-incident phase, and (3) post-incident phase. Throughout this survey, we find that there is much room to study (i) pre-incident risk mitigation strategies, (ii) research that assumes physical interaction with humans, and (iii) essential issues of foundation models themselves. We hope that this survey will be a milestone in providing a high-resolution analysis of the physical risks of FMRs and their control, contributing to the realization of a good human-robot relationship. Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa, Toshinori Kitamura, Gang Yan 0003, Shu Morikuni, Ryosuke Takanami, Alfredo Solano, Tatsuya Matsushima, Akiko Murakami, Yutaka Matsuo |
IJCAI | 3 |
| 2025 | Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph PropertiesabstractRecent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain poorly understood. In this work, we introduce the notion of a reasoning graph, extracted by clustering hidden‐state representations at each reasoning step, and systematically analyze three key graph-theoretic properties: cyclicity, diameter, and small-world index, across multiple tasks (GSM8K, MATH500, AIME~2024). Our findings reveal that distilled reasoning models (e.g., DeepSeek-R1-Distill-Qwen-32B) exhibit significantly more recurrent cycles (about 5 per sample), substantially larger graph diameters, and pronounced small-world characteristics (about 6x) compared to their base counterparts.
Notably, these structural advantages grow with task difficulty and model capacity, with cycle detection peaking at the 14B scale and exploration diameter maximized in the 32B variant, correlating positively with accuracy.
Furthermore, we show that supervised fine-tuning on an improved dataset systematically expands reasoning graph diameters in tandem with performance gains, offering concrete guidelines for dataset design aimed at boosting reasoning capabilities. By bridging theoretical insights into reasoning graph structures with practical recommendations for data construction, our work advances both the interpretability and the efficacy of large reasoning models. Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo |
NeurIPS | 4 |
| 2025 | Real-time data-efficient portrait stylization via geometric alignment
Zhuoru Li, Xuanyu Yin, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo |
Neural Networks | 5 |
| 2025 | Continual Pre-training on Character-level Noisy Texts Makes Decoder-based Language Models Robust Few-shot LearnersabstractAbstract Recent decoder-based pre-trained language models (PLMs) generally use subword tokenizers. However, adding character-level perturbations drastically changes the delimitation of texts by the tokenizers, leading to the vulnerability of PLMs. This study proposes a method of continual pre-training to convert decoder-based PLMs with subword tokenizers into perturbation-robust few-shot in-context learners. Our method continually trains decoder-based PLMs to predict the next tokens conditioning on artificially created character-level noisy texts. Since decoder-based language models are auto-regressive, we skip noised words from the target optimization. In addition, to maintain the same word prediction performance under noisy text as clean text, our method employs word distribution matching between the original PLMs and training models. We conducted experiments on various subword-based PLMs, including GPT2, Pythia, Mistral, Gemma2, and Llama3, ranging from 1B to 8B parameters. The results demonstrate that our method consistently improves the performance of few-shot in-context learning on downstream tasks which contain actual typos or misspellings as well as artificial noise.1 Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa |
Trans. Assoc. Comput. Linguistics | 3 |
| 2024 | Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?abstractRecent large language models (LLMs) have demonstrated remarkable generalization abilities in mathematics and logical reasoning tasks.Prior research indicates that LLMs pre-trained with programming language data exhibit high mathematical and reasoning abilities; however, this causal relationship has not been rigorously tested.Our research aims to verify which programming languages and features during pre-training affect logical inference performance.Specifically, we pre-trained decoderbased language models from scratch using datasets from ten programming languages (e.g., Python, C, Java) and three natural language datasets (Wikipedia, Fineweb, C4) under identical conditions.Thereafter, we evaluated the trained models in a few-shot in-context learning setting on logical reasoning tasks: FLD and bAbi, which do not require commonsense or world knowledge.The results demonstrate that nearly all models trained with programming languages consistently outperform those trained with natural languages, indicating that programming languages contain factors that elicit logic inference performance.In addition, we found that models trained with programming languages exhibit a better ability to follow instructions compared to those trained with natural languages.Further analysis reveals that the depth of Abstract Syntax Trees representing parsed results of programs also affects logical reasoning performance.These findings will offer insights into the essential elements of pretraining for acquiring the foundational abilities of LLMs. 1 Fumiya Uchiyama, Takeshi Kojima, Andrew Gambardella, Yusuke Iwasawa, Yutaka Matsuo |
EMNLP | 5 |
| 2024 | Paste and Harmonize via Denoising: Subject-Driven Image Editing with Frozen Pre-Trained Diffusion ModelabstractText-to-Image generative models have shown a remarkable ability to produce high-quality images. However, existing methods still face difficulties in exemplar-guided image editing without destroying the given objects’ identity in the exemplar image. To address this problem, we propose a new framework called Paste and Harmonize via Denoising, which leverages pre-trained diffusion models to facilitate the text-driven transfer of objects from an exemplar image to the edited image while preserving their appearance and characteristics. The framework consists of two main steps: paste and harmonize via denoising. In the paste step, an off-the-shelf text-driven model is utilized to localize the objects in the exemplar image. The editing task is naturally transformed into an image harmonization task by pasting the object patches into the edited image. In the harmonize via denoising step, we introduce an image harmonization module based on pre-trained diffusion models to blend the inserted object with the target image, producing a coherent and realistic image without compromising synthesis quality and preserving the text-driven style transfer editing ability. In the experiments, the qualitative comparisons with baselines demonstrate that our method achieves impressive performance in exemplar-based image editing on both training and in-the-wild images with high fidelity. More qualitative and quantitative results can be found at our website. Jiaxian Guo, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa |
ICASSP | 5 |
| 2024 | GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware PolicyabstractDue to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world. To address this issue, we introduce GenDOM, a framework that allows the manipulation policy to handle different deformable objects with only a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable object parameters and training it with a diverse range of simulated deformable objects so that the policy can adjust actions based on different object parameters. At the time of inference, given a new object, GenDOM can estimate the deformable object parameters with only a single real-world demonstration by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations in a differentiable physics simulator. Empirical validations on both simulated and real-world object manipulation setups clearly show that our method can manipulate different objects with a single demonstration and significantly outperforms the baseline in both environments (a 62% improvement for in-domain ropes and a 15% improvement for out-of-distribution ropes in simulation, as well as a 26% improvement for ropes and a 50% improvement for cloths in the real world), demonstrating the effectiveness of our approach in one-shot deformable object manipulation. https://sites.google.com/view/gendom/home. So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi 0001, Yuya Ikeda, Ryosuke Takanami, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa |
ICRA | 10 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 273 |
| 2024 | Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-RecoveryabstractA general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environments. In this paper, we first developed a top-level GPSR system for worldwide competition (RoboCup@Home2023) based on multiple foundation models. This system is both generalizable to variations and adaptive by prompting each model. Then, by analyzing the performance of the developed system, we found three types of failure in more realistic GPSR application settings: insufficient information, incorrect plan generation, and plan execution failure. We then propose the self-recovery prompting pipeline, which explores the necessary information and modifies its prompts to recover from failure. We experimentally confirm that the system with the self-recovery mechanism can accomplish tasks by resolving various failure cases. https://sites.google.com/view/srgpsr Mimo Shirasaka, Tatsuya Matsushima, Soshi Tsunashima, Yuya Ikeda, Aoi Horo, So Ikoma, Chikaha Tsuji, Hikaru Wada, Tsunekazu Omija, Dai Komukai, Yutaka Matsuo, Yusuke Iwasawa |
ICRA | 12 |
| 2024 | On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific NeuronsabstractTakeshi Kojima, Itsuki Okimura, Yusuke Iwasawa, Hitomi Yanaka, Yutaka Matsuo. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. Takeshi Kojima, Itsuki Okimura, Yusuke Iwasawa, Hitomi Yanaka, Yutaka Matsuo |
NAACL-HLT | 3 |
| 2024 | ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi, Yuta Oshima, Seong Cheol Jeong, Go Nagahara, Tomoshi Iiyama, Masahiro Suzuki 0001, Yusuke Iwasawa, Yutaka Matsuo |
NeurIPS | 9 |
| 2023 | Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled TextabstractWhile Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear.In this study, we present novel experimental insights into the resilience of LLMs, particularly GPT-4, when subjected to extensive character-level permutations.To investigate this, we first propose the Scrambled Bench, a suite designed to measure the capacity of LLMs to handle scrambled input, in terms of both recovering scrambled sentences and answering questions given scrambled context.The experimental results indicate that multiple advanced LLMs demonstrate the capability akin to typoglycemia 1 , a phenomenon where humans can understand the meaning of words even when the letters within those words are scrambled, as long as the first and last letters remain in place.More surprisingly, we found that only GPT-4 nearly flawlessly processes inputs with unnatural errors, a task that poses significant challenges for other LLMs and often even for humans.Specifically, GPT-4 can almost perfectly reconstruct the original sentences from scrambled ones, decreasing the edit distance by 95%, even when all letters within each word are entirely scrambled.It is counter-intuitive that LLMs can exhibit such resilience despite severe disruption to input tokenization caused by scrambled text. 2 Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa |
EMNLP | 4 |
| 2023 | A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Gu |
ICLR | 2 |
| 2023 | End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode InitializationabstractWe address the problem of biased gradient estimation in deep Boltzmann machines (DBMs). The existing method to obtain an unbiased estimator uses a maximal coupling based on a Gibbs sampler, but when the state is high-dimensional, it takes a long time to converge. In this study, we propose to use a coupling based on the Metropolis-Hastings (MH) and to initialize the state around a local mode of the target distribution. Because of the propensity of MH to reject proposals, the coupling tends to converge in only one step with a high probability, leading to high efficiency. We find that our method allows DBMs to be trained in an end-to-end fashion without greedy pretraining. We also propose some practical techniques to further improve the performance of DBMs. We empirically demonstrate that our training algorithm enables DBMs to show comparable generative performance to other deep generative models, achieving the FID score of 10.33 for MNIST. Shohei Taniguchi, Masahiro Suzuki 0001, Yusuke Iwasawa, Yutaka Matsuo |
ICML | 3 |
| 2023 | Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following
Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki 0001, Yusuke Iwasawa, Yutaka Matsuo |
MMM (2) | 4 |
| 2023 | Deep Learning Pipeline for Spotting Macro- and Micro-expressions in Long Video Sequences Based on Action Units and Optical Flow
Bo Yang 0057, Kazushi Ikeda, Gen Hattori, Masaru Sugano, Yusuke Iwasawa, Yutaka Matsuo |
Pattern Recognit. Lett. | 6 |
| 2022 | Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature AlignmentabstractVision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase. Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa |
IJCAI | 3 |
| 2022 | Large Language Models are Zero-Shot ReasonersabstractPretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding ``Let's think step by step'' before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with large-scale InstructGPT model (text-davinci-002), as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars. Takeshi Kojima, Shixiang Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa |
NeurIPS | 5 |
| 2022 | Langevin Autoencoders for Learning Deep Latent Variable ModelsabstractMarkov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variable models owing to costly datapoint-wise sampling iterations and slow convergence. This paper proposes the amortized Langevin dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced with updates of an encoder that maps observations into latent variables. This amortization enables efficient posterior sampling without datapoint-wise iterations. Despite its efficiency, we prove that ALD is valid as an MCMC algorithm, whose Markov chain has the target posterior as a stationary distribution under mild assumptions. Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE). Interestingly, the LAE can be implemented by slightly modifying the traditional autoencoder. Using multiple synthetic datasets, we first validate that ALD can properly obtain samples from target posteriors. We also evaluate the LAE on the image generation task, and show that our LAE can outperform existing methods based on variational inference, such as the variational autoencoder, and other MCMC-based methods in terms of the test likelihood. Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, Yutaka Matsuo |
NeurIPS | 2 |
| 2022 | Face-mask-aware Facial Expression Recognition based on Face Parsing and Vision Transformer
Bo Yang 0057, Kazushi Ikeda, Gen Hattori, Masaru Sugano, Yusuke Iwasawa, Yutaka Matsuo |
Pattern Recognit. Lett. | 6 |
| 2021 | Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo |
ICLR | 4 |
| 2021 | Test-Time Classifier Adjustment Module for Model-Agnostic Domain GeneralizationabstractThis paper presents a new algorithm for domain generalization (DG), \textit{test-time template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike existing methods that focus on \textit{training phase}, our method focuses \textit{test phase}, i.e., correcting its prediction by itself during test time. Specifically, T3A adjusts a trained linear classifier (the last layer of deep neural networks) with the following procedure: (1) compute a pseudo-prototype representation for each class using online unlabeled data augmented by the base classifier trained in the source domains, (2) and then classify each sample based on its distance to the pseudo-prototypes. T3A is back-propagation-free and modifies only the linear layer; therefore, the increase in computational cost during inference is negligible and avoids the catastrophic failure might caused by stochastic optimization. Despite its simplicity, T3A can leverage knowledge about the target domain by using off-the-shelf test-time data and improve performance. We tested our method on four domain generalization benchmarks, namely PACS, VLCS, OfficeHome, and TerraIncognita, along with various backbone networks including ResNet18, ResNet50, Big Transfer (BiT), Vision Transformers (ViT), and MLP-Mixer. The results show T3A stably improves performance on unseen domains across choices of backbone networks, and outperforms existing domain generalization methods. Yusuke Iwasawa, Yutaka Matsuo |
NeurIPS | 1 |
| 2021 | Information-theoretic regularization for learning global features by sequential VAEabstractAbstract Sequential variational autoencoders (VAEs) with a global latent variable z have been studied for disentangling the global features of data, which is useful for several downstream tasks. To further assist the sequential VAEs in obtaining meaningful z, existing approaches introduce a regularization term that maximizes the mutual information (MI) between the observation and z. However, by analyzing the sequential VAEs from the information-theoretic perspective, we claim that simply maximizing the MI encourages the latent variable to have redundant information, thereby preventing the disentanglement of global features. Based on this analysis, we derive a novel regularization method that makes z informative while encouraging disentanglement. Specifically, the proposed method removes redundant information by minimizing the MI between z and the local features by using adversarial training. In the experiments, we trained two sequential VAEs, state-space and autoregressive model variants, using speech and image datasets. The results indicate that the proposed method improves the performance of downstream classification and data generation tasks, thereby supporting our information-theoretic perspective for the learning of global features. Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo |
Mach. Learn. | 2 |
| 2021 | Graph-based knowledge tracing: Modeling student proficiency using graph neural networksabstractRecent advancements in computer-assisted learning systems have caused an increase in the research in knowledge tracing, wherein student performance is predicted over time. Student coursework can potentially be structured as a graph. Incorporating this graph-structured nature into a knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of graph neural networks (GNNs), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information. Hiromi Nakagawa, Yusuke Iwasawa, Yutaka Matsuo |
Web Intell. | 2 |
| 2020 | Stabilizing Adversarial Invariance Induction from Divergence Minimization PerspectiveabstractAdversarial invariance induction (AII) is a generic and powerful framework for enforcing an invariance to nuisance attributes into neural network representations. However, its optimization is often unstable and little is known about its practical behavior. This paper presents an analysis of the reasons for the optimization difficulties and provides a better optimization procedure by rethinking AII from a divergence minimization perspective. Interestingly, this perspective indicates a cause of the optimization difficulties: it does not ensure proper divergence minimization, which is a requirement of the invariant representations. We then propose a simple variant of AII, called invariance induction by discriminator matching, which takes into account the divergence minimization interpretation of the invariant representations. Our method consistently achieves near-optimal invariance in toy datasets with various configurations in which the original AII is catastrophically unstable. Extentive experiments on four real-world datasets also support the superior performance of the proposed method, leading to improved user anonymization and domain generalization. Yusuke Iwasawa, Kei Akuzawa, Yutaka Matsuo |
IJCAI | 1 |
| 2019 | Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo |
ECML/PKDD (2) | 2 |
| 2019 | Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkabstractRecent advancements in computer-assisted learning systems have caused an increase in the research of knowledge tracing, wherein student performance on coursework exercises is predicted over time. From the viewpoint of data structure, the coursework can be potentially structured as a graph. Incorporating this graph-structured nature into the knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of the graph neural network (GNN), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information. Hiromi Nakagawa, Yusuke Iwasawa, Yutaka Matsuo |
WI | 2 |
| 2018 | Expressive Speech Synthesis via Modeling Expressions with Variational AutoencoderabstractRecent advances in neural autoregressive models have improve the performance of speech synthesis (SS).However, as they lack the ability to model global characteristics of speech (such as speaker individualities or speaking styles), particularly when these characteristics have not been labeled, making neural autoregressive SS systems more expressive is still an open issue.In this paper, we propose to combine VoiceLoop, an autoregressive SS model, with Variational Autoencoder (VAE).This approach, unlike traditional autoregressive SS systems, uses VAE to model the global characteristics explicitly, enabling the expressiveness of the synthesized speech to be controlled in an unsupervised manner.Experiments using the VCTK and Bliz-zard2012 datasets show the VAE helps VoiceLoop to generate higher quality speech and to control the expressions in its synthesized speech by incorporating global characteristics into the speech generating process. Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo |
INTERSPEECH | 2 |
| 2017 | Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Countermeasures by Use of Adversarial TrainingabstractDeep neural networks have been successfully applied to activity recognition with wearables in terms of recognition performance. However, the black-box nature of neural networks could lead to privacy concerns. Namely, generally it is hard to expect what neural networks learn from data, and so they possibly learn features that highly discriminate user-information unintentionally, which increases the risk of information-disclosure. In this study, we analyzed the features learned by conventional deep neural networks when applied to data of wearables to confirm this phenomenon.Based on the results of our analysis, we propose the use of an adversarial training framework to suppress the risk of sensitive/unintended information disclosure. Our proposed model considers both an adversarial user classifier and a regular activity-classifier during training, which allows the model to learn representations that help the classifier to distinguish the activities but which, at the same time, prevents it from accessing user-discriminative information. This paper provides an empirical validation of the privacy issue and efficacy of the proposed method using three activity recognition tasks based on data of wearables. The empirical validation shows that our proposed method suppresses the concerns without any significant performance degradation, compared to conventional deep nets on all three tasks. Yusuke Iwasawa, Kotaro Nakayama, Ikuko Eguchi Yairi, Yutaka Matsuo |
IJCAI | 1 |
| 2015 | Road Sensing: Personal Sensing and Machine Learning for Development of Large Scale Accessibility MapabstractThis paper proposes a methodology for developing large scale accessibility map with personal sensing by using smart phone and machine learning technologies. The strength of the proposed method is its low cost data collection, which is a key to break through stagnations of accessibility map that currently applied to limited areas. This paper developed and evaluated a prototype system that estimates types of ground surfaces by applying supervised learning techniques to activity sensing data of wheelchair users recorded by a three-axis accelerometer, focusing on knowledge extraction and visualization. As a result of evaluation using nine wheelchair users' data with Support Vector Machine, three ground surface types, curb, tactile indicator, and slope, were detected with f-score (and accuracy) of 0.63 (0.92), 0.65 (0.85), and 0.54 (0.97) respectively. Yusuke Iwasawa, Koya Nagamine, Yutaka Matsuo, Ikuko Eguchi Yairi |
ASSETS | 1 |
| 2012 | Life-Logging of Wheelchair Driving on Web Maps for Visualizing Potential Accidents and Incidents
Yusuke Iwasawa, Ikuko Eguchi Yairi |
PRICAI | 1 |
| 2011 | Do multi-touch screens help visually impaired people to recognize graphicsabstractThis paper introduces our research project for developing a novel graphic representation method with touch and sound as the universal designed touch-screen interface for visually impaired people to understand graphical information. Our previous works on single-touch screens clarified the problems about the low recognition rate of curves. To solve this, an improved graphical representation interface for multi-touch screens were implemented and evaluated. Ikuko Eguchi Yairi, Kumi Naoe, Yusuke Iwasawa, Yusuke Fukushima |
ASSETS | 3 |