Papers - Aoki, Yoshimitsu

TAG: Guidance-Free Open-Vocabulary Semantic Segmentation

Kawano Y., Aoki Y.

IEEE Access (IEEE Access)  12   88322 - 88331 2024

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Semantic segmentation is a crucial task in computer vision, where each pixel in an image is classified into a category. However, traditional methods face significant challenges, including the need for pixel-level annotations and extensive training. Furthermore, because supervised learning uses a limited set of predefined categories, models typically struggle with rare classes and cannot recognize new ones. Unsupervised and open-vocabulary segmentation, proposed to tackle these issues, faces challenges, including the inability to assign specific class labels to clusters and the necessity of user-provided text queries for guidance. In this context, we propose a novel approach, TAG which achieves Training, Annotation, and Guidance-free open-vocabulary semantic segmentation. TAG utilizes pre-trained models such as CLIP and DINO to segment images into meaningful categories without additional training or dense annotations. It retrieves class labels from an external database, providing flexibility to adapt to new scenarios. Our TAG achieves state-of-the-art results on PascalVOC, PascalContext and ADE20K for open-vocabulary segmentation without given class names, i.e. improvement of +15.3 mIoU on PascalVOC.

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Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization

Shiba S., Klose Y., Aoki Y., Gallego G.

IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Transactions on Pattern Analysis and Machine Intelligence)  46 ( 12 ) 7742 - 7759 2024

ISSN  01628828

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Event cameras respond to scene dynamics and provide signals naturally suitable for motion estimation with advantages, such as high dynamic range. The emerging field of event-based vision motivates a revisit of fundamental computer vision tasks related to motion, such as optical flow and depth estimation. However, state-of-the-art event-based optical flow methods tend to originate in frame-based deep-learning methods, which require several adaptations (data conversion, loss function, etc.) as they have very different properties. We develop a principled method to extend the Contrast Maximization framework to estimate dense optical flow, depth, and ego-motion from events alone. The proposed method sensibly models the space-time properties of event data and tackles the event alignment problem. It designs the objective function to prevent overfitting, deals better with occlusions, and improves convergence using a multi-scale approach. With these key elements, our method ranks first among unsupervised methods on the MVSEC benchmark and is competitive on the DSEC benchmark. Moreover, it allows us to simultaneously estimate dense depth and ego-motion, exposes the limitations of current flow benchmarks, and produces remarkable results when it is transferred to unsupervised learning settings. Along with various downstream applications shown, we hope the proposed method becomes a cornerstone on event-based motion-related tasks. Code is available at <uri>https://github.com/tub-rip/event_based_optical_flow</uri>

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MaskDiffusion: Exploiting Pre-trained Diffusion Models for Semantic Segmentation

Kawano Y., Aoki Y.

IEEE Access 12   127283 - 127293 2024

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Semantic segmentation is essential in computer vision for various applications, yet traditional approaches face significant challenges, including the high cost of annotation and extensive training for supervised learning. Additionally, due to the limited predefined categories in supervised learning, models typically struggle with infrequent classes and are unable to predict novel classes. To address these limitations, we propose MaskDiffusion, an innovative approach that leverages pretrained frozen Stable Diffusion to achieve open-vocabulary semantic segmentation without the need for additional training or annotation, leading to improved performance compared to similar methods. We also demonstrate the superior performance of MaskDiffusion in handling open vocabularies, including fine-grained and proper noun-based categories, thus expanding the scope of segmentation applications. Overall, our MaskDiffusion shows significant qualitative and quantitative improvements in contrast to other comparable unsupervised segmentation methods, i.e. on the Potsdam dataset (+10.5 mIoU compared to GEM) and COCO-Stuff (+14.8 mIoU compared to DiffSeg). All code and data are released at https://github.com/Valkyrja3607/MaskDiffusion.

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Guided by the Way: The Role of On-the-route Objects and Scene Text in Enhancing Outdoor Navigation

Sun Y., Qiu Y., Aoki Y., Kataoka H.

Proceedings - IEEE International Conference on Robotics and Automation    5198 - 5204 2024

ISSN  10504729

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In outdoor environments, Vision-and-Language Navigation (VLN) requires an agent to rely on multi-modal cues from real-world urban environments and natural language instructions. While existing outdoor VLN models predict actions using a combination of panorama and instruction features, this approach ignores objects in the environment and learns data bias to fail navigation. According to our preliminary findings, most instances of navigation failure in previous models were due to turning or stopping at the wrong place. In contrast, humans intuitively frequently use identifiable objects or store names as reference landmarks, ensuring accurate turns and stops, especially in unfamiliar places. To address this insight gap, we propose an Object-Attention VLN (OAVLN) model that helps the agent focus on relevant objects during training and understand the environment better. Our model outperforms previous methods in all evaluation metrics under both seen and unseen scenarios on two existing benchmark datasets, Touchdown and map2seq.

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PCT: Perspective Cue Training Framework for Multi-Camera BEV Segmentation

Ishikawa H., Iida T., Konishi Y., Aoki Y.

IEEE International Conference on Intelligent Robots and Systems    13253 - 13260 2024

ISSN  21530858

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Generating annotations for bird's-eye-view (BEV) segmentation presents significant challenges due to the scenes' complexity and the high manual annotation cost. In this work, we address these challenges by leveraging the abundance of unlabeled data available. We propose the Perspective Cue Training (PCT) framework, a novel training framework that utilizes pseudo-labels generated from unlabeled perspective images using publicly available semantic segmentation models trained on large street-view datasets. PCT applies a perspective view task head to the image encoder shared with the BEV segmentation head, effectively utilizing the unlabeled data to be trained with the generated pseudo-labels. Since image encoders are present in nearly all camera-based BEV segmentation architectures, PCT is flexible and applicable to various existing BEV architectures. In this paper, we applied PCT for semi-supervised learning (SSL) and unsupervised domain adaptation (UDA). Additionally, we introduce strong input perturbation through Camera Dropout (CamDrop) and feature perturbation via BEV Feature Dropout (BFD), which are crucial for enhancing SSL capabilities using our teacher-student framework. Our comprehensive approach is simple and flexible but yields significant improvements over various baselines for SSL and UDA, achieving competitive performances even against the current state-of-the-art.

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3D Human Scan with A Moving Event Camera

Kohyama K., Shiba S., Aoki Y.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops    5586 - 5596 2024

ISSN  21607508

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Capturing the 3D human body is one of the important tasks in computer vision with a wide range of applications such as virtual reality and sports analysis. However, conventional frame cameras are limited by their temporal resolution and dynamic range, which imposes constraints in real-world application setups. Event cameras have the advantages of high temporal resolution and high dynamic range (HDR), but the development of event-based methods is necessary to handle data with different characteristics. This paper proposes a novel event-based method for 3D pose estimation and human mesh recovery. Prior work on event-based human mesh recovery require frames (images) as well as event data. The proposed method solely relies on events; it carves 3D voxels by moving the event camera around a stationary body, reconstructs the human pose and mesh by attenuated rays, and fit statistical body models, preserving high-frequency details. The experimental results show that the proposed method outperforms conventional frame-based methods in the estimation accuracy of both pose and body mesh. We also demonstrate results in challenging situations where other frame-based methods suffer from motion blur. This is the first-of-its-kind to demonstrate event-only human mesh recovery, and we hope that it is the first step toward achieving robust and accurate 3D human body scanning from vision sensors.

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Time-weighted motion history image for human activity classification in sports

Komori H., Isogawa M., Mikami D., Nagai T., Aoki Y.

Sports Engineering (Sports Engineering)  26 ( 1 )  2023.12

ISSN  13697072

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Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.

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Action Quality Assessment Model Using Specialists’ Gaze Location and Kinematics Data—Focusing on Evaluating Figure Skating Jumps

Hirosawa S., Kato T., Yamashita T., Aoki Y.

Sensors (Sensors)  23 ( 22 )  2023.11

ISSN  14248220

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Action quality assessment (AQA) tasks in computer vision evaluate action quality in videos, and they can be applied to sports for performance evaluation. A typical example of AQA is predicting the final score from a video that captures an entire figure skating program. However, no previous studies have predicted individual jump scores, which are of great interest to competitors because of the high weight of competition. Despite the presence of unnecessary information in figure skating videos, human specialists can focus and reduce information when they evaluate jumps. In this study, we clarified the eye movements of figure skating judges and skaters while evaluating jumps and proposed a prediction model for jump performance that utilized specialists’ gaze location to reduce information. Kinematic features obtained from the tracking system were input into the model in addition to videos to improve accuracy. The results showed that skaters focused more on the face, whereas judges focused on the lower extremities. These gaze locations were applied to the model, which demonstrated the highest accuracy when utilizing both specialists’ gaze locations. The model outperformed human predictions and the baseline model (RMSE:0.775), suggesting a combination of human specialist knowledge and machine capabilities could yield higher accuracy.

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Boosting Semantic Segmentation by Conditioning the Backbone with Semantic Boundaries

Ishikawa H., Aoki Y.

Sensors (Sensors)  23 ( 15 )  2023.08

ISSN  14248220

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In this paper, we propose the Semantic-Boundary-Conditioned Backbone (SBCB) framework, an effective approach to enhancing semantic segmentation performance, particularly around mask boundaries, while maintaining compatibility with various segmentation architectures. Our objective is to improve existing models by leveraging semantic boundary information as an auxiliary task. The SBCB framework incorporates a complementary semantic boundary detection (SBD) task with a multi-task learning approach. It enhances the segmentation backbone without introducing additional parameters during inference or relying on independent post-processing modules. The SBD head utilizes multi-scale features from the backbone, learning low-level features in early stages and understanding high-level semantics in later stages. This complements common semantic segmentation architectures, where features from later stages are used for classification. Extensive evaluations using popular segmentation heads and backbones demonstrate the effectiveness of the SBCB. It leads to an average improvement of (Formula presented.) in IoU and a (Formula presented.) gain in the boundary F-score on the Cityscapes dataset. The SBCB framework also improves over- and under-segmentation characteristics. Furthermore, the SBCB adapts well to customized backbones and emerging vision transformer models, consistently achieving superior performance. In summary, the SBCB framework significantly boosts segmentation performance, especially around boundaries, without introducing complexity to the models. Leveraging the SBD task as an auxiliary objective, our approach demonstrates consistent improvements on various benchmarks, confirming its potential for advancing the field of semantic segmentation.

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Outdoor Vision-and-Language Navigation Needs Object-Level Alignment

Sun Y., Qiu Y., Aoki Y., Kataoka H.

Sensors (Sensors)  23 ( 13 )  2023.07

ISSN  14248220

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In the field of embodied AI, vision-and-language navigation (VLN) is a crucial and challenging multi-modal task. Specifically, outdoor VLN involves an agent navigating within a graph-based environment, while simultaneously interpreting information from real-world urban environments and natural language instructions. Existing outdoor VLN models predict actions using a combination of panorama and instruction features. However, these methods may cause the agent to struggle to understand complicated outdoor environments and ignore the details in the environments to fail to navigate. Human navigation often involves the use of specific objects as reference landmarks when navigating to unfamiliar places, providing a more rational and efficient approach to navigation. Inspired by this natural human behavior, we propose an object-level alignment module (OAlM), which guides the agent to focus more on object tokens mentioned in the instructions and recognize these landmarks during navigation. By treating these landmarks as sub-goals, our method effectively decomposes a long-range path into a series of shorter paths, ultimately improving the agent’s overall performance. In addition to enabling better object recognition and alignment, our proposed OAlM also fosters a more robust and adaptable agent capable of navigating complex environments. This adaptability is particularly crucial for real-world applications where environmental conditions can be unpredictable and varied. Experimental results show our OAlM is a more object-focused model, and our approach outperforms all metrics on a challenging outdoor VLN Touchdown dataset, exceeding the baseline by 3.19% on task completion (TC). These results highlight the potential of leveraging object-level information in the form of sub-goals to improve navigation performance in embodied AI systems, paving the way for more advanced and efficient outdoor navigation.

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A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

Shiba S., Aoki Y., Gallego G.

Advanced Intelligent Systems (Advanced Intelligent Systems)  5 ( 3 )  2023.03

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Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. A novel, computationally efficient regularizer based on geometric principles to mitigate event collapse is proposed. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, this regularizer is the only effective solution for event collapse without trading off the runtime. It is hoped that this work opens the door for future applications that unlocks the advantages of event cameras. Project page: https://github.com/tub-rip/event_collapse.

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Efficient Transformer-Based Compressed Video Modeling via Informative Patch Selection

Suzuki T., Aoki Y.

Sensors (Sensors)  23 ( 1 )  2023.01

ISSN  14248220

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Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are less informative and assigning short code lengths to them (e.g., MPEG4) have successfully reduced the redundancy of videos. Inspired by this idea, we propose Informative Patch Selection (IPS), which efficiently reduces the inference cost by excluding redundant patches from the input of the Transformer-based video model. The redundancy of each patch is calculated from motions and residuals obtained while decoding a compressed video. The proposed method is simple and effective in that it can dynamically reduce the inference cost depending on the input without any policy model or additional loss term. Extensive experiments on action recognition demonstrated that our method could significantly improve the trade-off between the accuracy and inference cost of the Transformer-based video model. Although the method does not require any policy model or additional loss term, its performance approaches that of existing methods that do require them.

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Listening Human Behavior: 3D Human Pose Estimation with Acoustic Signals

Shibata Y., Kawashima Y., Isogawa M., Irie G., Kimura A., Aoki Y.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition)  2023-June   13323 - 13332 2023

ISSN  10636919

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Given only acoustic signals without any high-level information, such as voices or sounds of scenes/actions, how much can we infer about the behavior of humans? Unlike existing methods, which suffer from privacy issues because they use signals that include human speech or the sounds of specific actions, we explore how low-level acoustic signals can provide enough clues to estimate 3D human poses by active acoustic sensing with a single pair of microphones and loudspeakers (see Fig. 1). This is a challenging task since sound is much more diffractive than other signals and therefore covers up the shape of objects in a scene. Accordingly, we introduce a framework that encodes multichannel audio features into 3D human poses. Aiming to capture subtle sound changes to reveal detailed pose information, we explicitly extract phase features from the acoustic signals together with typical spectrum features and feed them into our human pose estimation network. Also, we show that reflected or diffracted sounds are easily influenced by subjects' physique differences e.g., height and muscularity, which deteriorates prediction accuracy. We reduce these gaps by using a subject discriminator to improve accuracy. Our experiments suggest that with the use of only low-dimensional acoustic information, our method outperforms baseline methods. The datasets and codes used in this project will be publicly available.

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Unsupervised Domain-Adaptive Semantic Segmentation with Uncertainty Loss

Kawano Y., Nota Y., Aoki Y.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  89 ( 12 ) 921 - 925 2023

ISSN  09120289

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In recent years, deep learning models have achieved great success in many fields, but their training relies on large labeled data. Annotation to obtain labeled samples is challenging, time-consuming, and expensive, and has been a major challenge in deep learning. In this study, we propose a method for unsupervised domain adaptation that can reduce annotation cost in terms of accuracy and time compared to conventional methods by using the uncertainty of the model in semantic segmentation. Unsupervised domain adaptation aims to adapt models trained on synthetic data to real-world data without the need for costly annotation of real images. Since the training data are images and correct labels created by the game engine, there is no need to manually annotate them. However, if the source (synthetic) data is trained using conventional supervised learning, the performance is significantly degraded because the domain is different from the target (real image) data.This study closes the domain gap between source and target by calculating the uncertainty in the target data from the model output on a pixel-by-pixel basis and minimizing this uncertainty as loss. Adding this uncertainty loss to conventional unsupervised domain adaptation methods results in a model that is more robust to the target domain and achieves state-of-the-art results.

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Preliminary Action Recognition

Kawano Y., Nagasaki Y., Hara K., Aoki Y., Kataoka H.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  89 ( 12 ) 915 - 920 2023

ISSN  09120289

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In this paper, we propose preliminary action recognition which is an action anticipation method that can be used in real-time. Preliminary action recognition as a solution to action anticipation, which is the task of detecting the action of a person before the person performs the action. We define preliminary action class before action class that is anticipated by recognizing the preliminary actions specific to the action before it occurs, just as humans do. Conventional action anticipation methods have two problems for real-time use. First, it requires 10 to 20 seconds of video to anticipate 1 second ahead, and it can only anticipate every 10 seconds. Second, the video input in training always switches actions 1 second after the end of the video. This problem setting can be a leakage because there is no action break in real-time operation. Proposed method solves this problem and makes it possible to operate action prediction in real-time. In addition, since action classes and preliminary action classes tend to share a lot of similarities, it is difficult to distinguish between them. We propose a model that can discriminate the subtle differences in motion by increasing the latent distance between adjacent classes in terms of time.

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Non-Deep Active Learning for Deep Neural Networks

Kawano Y., Nota Y., Mochizuki R., Aoki Y.

Sensors (Sensors)  22 ( 14 )  2022.07

ISSN  14248220

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One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabeled images. Based on this prediction, an uncertainty indicator is generated for each unlabeled image. Images with a high uncertainty index are considered to have a high information content, and are selected for annotation. Our proposed method is based on a very simple and powerful idea: select samples near the decision boundary of the model. Experimental results on multiple datasets show that the proposed method achieves higher accuracy than conventional active learning methods on multiple tasks and up to 14 times faster execution time from (Formula presented.) s to (Formula presented.) s. The proposed method outperforms the current SoTA method by 1% accuracy on CIFAR-10.

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Event Collapse in Contrast Maximization Frameworks

Shiba S., Aoki Y., Gallego G.

Sensors (Sensors)  22 ( 14 )  2022.07

ISSN  14248220

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Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space–time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models.

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Supporting collective physical activities by interactive floor projection in a special-needs school setting

Oki M., Akizuki S., Bourreau B., Takahashi I., Aoki Y., Yamamoto J., Suzuki K.

International Journal of Child-Computer Interaction (International Journal of Child-Computer Interaction)  32 2022.06

ISSN  22128689

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This paper presents an algorithm to provide floor projection feedback according to the local distance and density of individuals. It is realized by a large-space floor projection system with a feedback function based on human tracking with laser ranging image sensors. The purpose is to support the cognition of spatial–temporal structures of groups of adolescents with neurodevelopmental disorders (NDs) that are conducting organized physical activity (PA). Observation and evaluation of behavioral changes in adolescents with NDs, when they were active with or without the floor projection based on the proposed algorithm, were conducted to validate its effectiveness. We observed that the proposed algorithm can be implemented in different organized PAs. It had the effect to help individuals in a behavior to keep a close distance to each other as a group rather than to keep the same distance apart from each other while walking.

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Fast Event-Based Optical Flow Estimation by Triplet Matching

Shiba S., Aoki Y., Gallego G.

IEEE Signal Processing Letters (IEEE Signal Processing Letters)  29   2712 - 2716 2022

ISSN  10709908

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Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (>10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.

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Detection of Osteoarthritis from Multimodal Hand Data

Andrade Guerreiro J.J., Aoki Y., Saito S., Suzuki K.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS)  2022-July   3607 - 3610 2022

ISSN  1557170X

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Osteoarthritis (OA) describes a degenerative joint disorder that is prevalent among older people and typically results in swollen and inflamed joints. The aim of this paper is to develop a method using images, videos and thermal data of 100 patients taken at Keio University Hospital to detect OA in hands. By using hand pose estimation on the video data, joint angles can be calculated and subsequently transformed into feature vectors. For the thermal and RGB images, hand keypoint detectors were trained to identify and crop the appropriate joints within the images. The resulting extracted features are combined and further trained on Support Vector Machines and Convolutional Neural Networks to obtain the final binary classification for each joint. While the proposed method generally shows favorable accuracy and F1-scores on the Proximal (PIP) and Distal Interphalangeal (DIP) joints, the performance on the Metacarpophalangeal (MCP) joints is limited by the low occurrence of affected joints in the dataset. We further compare the different modalities and found that, apart from the combined approach, using video data provides the best results. Clinical Relevance - The proposed method shows promising first results for the usage of visual and thermal data in combination with machine learning in order to detect OA

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