BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Yoichi Sato

Yoichi Sato contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionArtificial IntelligenceMachine Learningeess.IVHuman-Computer InteractionNeural and Evolutionary Computingphysics.med-phTissues and Organs

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
9works
0followers
8topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

9 published item(s)

preprint2026arXiv

Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEM

Vector-HaSH and the Tolman-Eichenbaum Machine propose the hippocampal-entorhinal circuit factorizes content from a grid-cell scaffold, supporting compositional memory via ripple-mediated replay. Human electrophysiology shows multi-hop replay fidelity decays multiplicatively. We show VaCoAl, an algebro-deterministic hyperdimensional memory built from Galois-field linear-feedback shift registers, supplies a shared algebraic object.Specifically: (i) deterministic Galois-field diffusion offers a substrate-level alternative to random projections, ensuring quasi-orthogonality and bit-exact reproducibility; (ii) the path-integral Confidence Ratio provides a tractable model of multiplicative decay in multi-hop replay; (iii) VaCoAl's STDP-like path selection follows from architectural demands - similarity preservation and bounded search - constraining hippocampal computation.We map two distinct VaCoAl regimes to the EC-CA3 direct and EC-DG-CA3 trisynaptic pathways. Cellular evidence, including mossy-fiber detonator transmission and granule-cell sparse coding, supports a reading where the DG-CA3 pathway implements biophysical homologues of Galois-field arithmetic with approximate reversibility.Crucially, we connect this to Pearl's Ladder of Causation. Reversible GF(2) binding supplies the surgical-modification algebra required by the do-operator (rung 2). The dual architecture (Regime A anchoring the factual world, Regime B minting counterfactual worlds) supplies the parallel non-interfering substrate counterfactual reasoning provably requires (rung 3), yielding a profound Pearl-based evolutionary rationale.The framing proceeds in two tiers: VaCoAl is offered first as architectural correspondence, then as biophysical realization with approximate reversibility. We prove formal correspondences and derive testable iEEG predictions, bridging computational neuroscience and hyperdimensional computing.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SocialDirector: Training-Free Social Interaction Control for Multi-Person Video Generation

Video generation has advanced rapidly, producing photorealistic videos from text or image prompts. Meanwhile, film production and social robotics increasingly demand multi-person videos with rich social interactions, including conversations, gestures, and coordinated actions. However, existing models offer no explicit control over interactions, such as who performs which action, when it occurs, and toward whom it is directed. This often results in wrong person performing unintended actions (actor-action mismatch), disordered social dynamics, and wrong action targets. To address these challenges, we present SocialDirector, a training-free interaction controller that enhances the generation model by modulating cross-attention maps. SocialDirector contains two modules: Social Actor Masking and Directional Reweighting. Social Actor Masking constrains each person's visual tokens to attend only to their own textual descriptions via a spatiotemporal mask, avoiding actor-action mismatch and disordered social dynamics. Directional Reweighting amplifies attention to directional words (e.g., "leftward", "right"), leading each action towards its intended target. To evaluate generated social interactions, we annotate existing datasets with interaction descriptions and build a fully automated evaluation pipeline powered by open-source VLMs. Experiments on different video generation models show that SocialDirector significantly improves interaction fidelity and approaches the upper bound set by real videos.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Background Mixup Data Augmentation for Hand and Object-in-Contact Detection

Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos. For training an object detector, a method called Mixup, which overlays two training images to mitigate data bias, has been empirically shown to be effective for data augmentation. However, in hand-object detection, mixing two hand-manipulation images produces unintended biases, e.g., the concentration of hands and objects in a specific region degrades the ability of the hand-object detector to identify object boundaries. We propose a data-augmentation method called Background Mixup that leverages data-mixing regularization while reducing the unintended effects in hand-object detection. Instead of mixing two images where a hand and an object in contact appear, we mix a target training image with background images without hands and objects-in-contact extracted from external image sources, and use the mixed images for training the detector. Our experiments demonstrated that the proposed method can effectively reduce false positives and improve the performance of hand-object detection in both supervised and semi-supervised learning settings.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Domain Adaptive Hand Keypoint and Pixel Localization in the Wild

We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e.g., outdoors) when we only have labeled images taken under very different conditions (e.g., indoors). In the real world, it is important that the model trained for both tasks works under various imaging conditions. However, their variation covered by existing labeled hand datasets is limited. Thus, it is necessary to adapt the model trained on the labeled images (source) to unlabeled images (target) with unseen imaging conditions. While self-training domain adaptation methods (i.e., learning from the unlabeled target images in a self-supervised manner) have been developed for both tasks, their training may degrade performance when the predictions on the target images are noisy. To avoid this, it is crucial to assign a low importance (confidence) weight to the noisy predictions during self-training. In this paper, we propose to utilize the divergence of two predictions to estimate the confidence of the target image for both tasks. These predictions are given from two separate networks, and their divergence helps identify the noisy predictions. To integrate our proposed confidence estimation into self-training, we propose a teacher-student framework where the two networks (teachers) provide supervision to a network (student) for self-training, and the teachers are learned from the student by knowledge distillation. Our experiments show its superiority over state-of-the-art methods in adaptation settings with different lighting, grasping objects, backgrounds, and camera viewpoints. Our method improves by 4% the multi-task score on HO3D compared to the latest adversarial adaptation method. We also validate our method on Ego4D, egocentric videos with rapid changes in imaging conditions outdoors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ego4D: Around the World in 3,000 Hours of Egocentric Video

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

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 segmentation model to a new domain is required in hand segmentation. Here, we focus on appearance gaps for hand regions and backgrounds separately. We propose (i) foreground-aware image stylization and (ii) consensus pseudo-labeling for domain adaptation of hand segmentation. We stylize source images independently for the foreground and background using target images as style. To resolve the domain shift that the stylization has not addressed, we apply careful pseudo-labeling by taking a consensus between the models trained on the source and stylized source images. We validated our method on domain adaptation of hand segmentation from real and simulation images. Our method achieved state-of-the-art performance in both settings. We also demonstrated promising results in challenging multi-target domain adaptation and domain generalization settings. Code is available at https://github.com/ut-vision/FgSty-CPL.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Surgical Skill Assessment via Video Semantic Aggregation

Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas. Existing works often resort to a CNN-LSTM joint framework that models long-term relationships by LSTMs on spatially pooled short-term CNN features. However, this practice would inevitably neglect the difference among semantic concepts such as tools, tissues, and background in the spatial dimension, impeding the subsequent temporal relationship modeling. In this paper, we propose a novel skill assessment framework, Video Semantic Aggregation (ViSA), which discovers different semantic parts and aggregates them across spatiotemporal dimensions. The explicit discovery of semantic parts provides an explanatory visualization that helps understand the neural network's decisions. It also enables us to further incorporate auxiliary information such as the kinematic data to improve representation learning and performance. The experiments on two datasets show the competitiveness of ViSA compared to state-of-the-art methods. Source code is available at: bit.ly/MICCAI2022ViSA.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide information on their locations have been developed. However, prior systems for assisting people in finding objects require users to register the target objects in advance. This requirement imposes a cumbersome burden on the users, and the system cannot help remind them of unexpectedly lost objects. We propose GO-Finder ("Generic Object Finder"), a registration-free wearable camera based system for assisting people in finding an arbitrary number of objects based on two key features: automatic discovery of hand-held objects and image-based candidate selection. Given a video taken from a wearable camera, Go-Finder automatically detects and groups hand-held objects to form a visual timeline of the objects. Users can retrieve the last appearance of the object by browsing the timeline through a smartphone app. We conducted a user study to investigate how users benefit from using GO-Finder and confirmed improved accuracy and reduced mental load regarding the object search task by providing clear visual cues on object locations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Computer-Aided Diagnosis System Using Artificial Intelligence for Hip Fractures -Multi-Institutional Joint Development Research-

[Objective] To develop a Computer-aided diagnosis (CAD) system for plane frontal hip X-rays with a deep learning model trained on a large dataset collected at multiple centers. [Materials and Methods]. We included 5295 cases with neck fracture or trochanteric fracture who were diagnosed and treated by orthopedic surgeons using plane X-rays or computed tomography (CT) or magnetic resonance imaging (MRI) who visited each institution between April 2009 and March 2019 were enrolled. Cases in which both hips were not included in the photographing range, femoral shaft fractures, and periprosthetic fractures were excluded, and 5242 plane frontal pelvic X-rays obtained from 4,851 cases were used for machine learning. These images were divided into 5242 images including the fracture side and 5242 images without the fracture side, and a total of 10484 images were used for machine learning. A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were used as frameworks, and EfficientNet-B4, which is pre-trained ImageNet model, was used. In the final evaluation, accuracy, sensitivity, specificity, F-value and area under the curve (AUC) were evaluated. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the diagnostic basis of the CAD system. [Results] The diagnostic accuracy of the learning model was accuracy of 96. 1 %, sensitivity of 95.2 %, specificity of 96.9 %, F-value of 0.961, and AUC of 0.99. The cases who were correct for the diagnosis showed generally correct diagnostic basis using Grad-CAM. [Conclusions] The CAD system using deep learning model which we developed was able to diagnose hip fracture in the plane X-ray with the high accuracy, and it was possible to present the decision reason.

0 citations0 reviewsNo code linkedOpen access