arXiv subjects

Takara Taniguchi

Publications and source records attributed to Takara Taniguchi.

6 recordsLinked to original sources

arXiv2026

EDGE-Shield: Efficient Denoising-staGE Shield for Violative Content Filtering via Scalable Reference-Based Matching

The advent of Text-to-Image generative models poses significant risks of copyright violation and deepfake generation. Since the rapid proliferation of new copyrighted works and private individuals constantly emerges, reference-based training-free content filters are essential for providing up-to-date protection without the constraints of a fixed knowledge cutoff. However, existing reference-based approaches often lack scalability when handling numerous references and require waiting for finishing image generation. To solve these problems, we propose EDGE-Shield, a scalable content filter during the denoising process that maintains practical latency while effectively blocking violative content. We leverage embedding-based matching for efficient reference comparison. Additionally, we introduce an \textit{$x$}-pred transformation that converts the model's noisy intermediate latent into the pseudo-estimated clean latent at the later stage, enhancing classification accuracy of violative content at earlier denoising stages. We conduct experiments of violative content filtering against two generative models including Z-Image-Turbo and Qwen-Image. EDGE-Shield significantly outperforms traditional reference-based methods in terms of latency; it achieves an approximate $79\%$ reduction in processing time for Z-Image-Turbo and approximate $50\%$ reduction for Qwen-Image, maintaining the filtering accuracy across different model architectures.

cs.CV↗

arXiv2026

Towards Safer Mobile Agents: Scalable Generation and Evaluation of Diverse Scenarios for VLMs

Vision Language Models (VLMs) are increasingly deployed in autonomous vehicles and mobile systems, making it crucial to evaluate their ability to support safer decision-making in complex environments. However, existing benchmarks inadequately cover diverse hazardous situations, especially anomalous scenarios with spatio-temporal dynamics. While image editing models are a promising means to synthesize such hazards, it remains challenging to generate well-formulated scenarios that include moving, intrusive, and distant objects frequently observed in the real world. To address this gap, we introduce \textbf{HazardForge}, a scalable pipeline that leverages image editing models to generate these scenarios with layout decision algorithms, and validation modules. Using HazardForge, we construct \textbf{MovSafeBench}, a multiple-choice question (MCQ) benchmark comprising 7,254 images and corresponding QA pairs across 13 object categories, covering both normal and anomalous objects. Experiments using MovSafeBench show that VLM performance degrades notably under conditions including anomalous objects, with the largest drop in scenarios requiring nuanced motion understanding.

cs.CV↗

arXiv2025

University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system

Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/.

cs.CV↗

arXiv2025

OnomatoGen: Onomatopoeia Generation with the Alpha-Channel in Manga

Onomatopoeia is an important element for textual messaging in manga. Unlike character dialogue in manga, onomatopoeic expressions are visually stylized, with variations in shape, size, and placement that reflect the scene's intensity and mood. Despite its important role, onomatopoeia has not received much attention in manga generation. In this paper, we focus on onomatopoeia generation and propose OnomatoGen, which stylizes plain text to an onomatopoeic style. We empirically show the unique properties of onomatopoeia generation, which differ from typical text stylization methods, and that OnomatoGen can effectively stylize plain text in an onomatopoeia style.

cs.MM↗

arXiv2025

Enumeration Algorithm for Genus-4 Superspecial Hyperelliptic Curves with Automorphism Group $Q_8$

In this paper, we propose an algorithm to enumerate genus-4 superspecial hyperelliptic curves whose automorphism groups isomorphic to the quaternion group. By implementing this algorithm with Magma, we successfully obtain the number of isomorphism classes of such curves in every characteristic $7 \leq p < 10000$. Interestingly, the experimental results lead us to the conjecture that there exist exactly $[p/48]$ isomorphism classes of such curves if $p \equiv 1,7 \pmod{8}$, whereas such curves exist if $p \equiv 3,5 \pmod{8}$

math.AG↗

arXiv2024

Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Manga

We tackle one-shot object detection in Japanese Manga. The rising global popularity of Japanese manga has made the object detection of character faces increasingly important, with potential applications such as automatic colorization. However, obtaining sufficient data for training conventional object detectors is challenging due to copyright restrictions. Additionally, new characters appear every time a new volume of manga is released, making it impractical to re-train object detectors each time to detect these new characters. Therefore, one-shot object detection, where only a single query (reference) image is required to detect a new character, is an essential task in the manga industry. One challenge with one-shot object detection in manga is the large variation in the poses and facial expressions of characters in target images, despite having only one query image as a reference. Another challenge is that the frequency of character appearances follows a long-tail distribution. To overcome these challenges, we propose a data augmentation method in feature space to increase the variation of the query. The proposed method augments the feature from the query by adding Gaussian noise, with the noise variance at each channel learned during training. The experimental results show that the proposed method improves the performance for both seen and unseen classes, surpassing data augmentation methods in image space.

cs.CV↗