10 papers
Overview and Meta-Analysis of DCASE 2026 Challenge Task 6: Audio Moment Retrieval from Long Audio
Hokuto Munakata, Tatsuya Komatsu, Keisuke Imoto +3
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 6, Audio Moment Retrieval (AMR) from Long Audio. Given…
CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries
Hokuto Munakata, Takehiro Imamura, Taichi Nishimura +1
We introduce CASTELLA, a human-annotated audio benchmark for the task of audio moment retrieval (AMR). Although AMR has various useful potential applications, there is still no est…
Hallucination Localization in Video Captioning
Shota Nakada, Kazuhiro Saito, Yuchi Ishikawa +3
We propose a novel task, hallucination localization in video captioning, which aims to identify hallucinations in video captions at the span level (i.e. individual words or phrases…
Listening without Looking: Modality Bias in Audio-Visual Captioning
Yuchi Ishikawa, Toranosuke Manabe, Tatsuya Komatsu +1
Audio-visual captioning aims to generate holistic scene descriptions by jointly modeling sound and vision. While recent methods have improved performance through sophisticated moda…
ProLAP: Probabilistic Language-Audio Pre-Training
Toranosuke Manabe, Yuchi Ishikawa, Hokuto Munakata +1
Language-audio joint representation learning frameworks typically depend on deterministic embeddings, assuming a one-to-one correspondence between audio and text. In real-world set…
Language-Guided Contrastive Audio-Visual Masked Autoencoder with Automatically Generated Audio-Visual-Text Triplets from Videos
Yuchi Ishikawa, Shota Nakada, Hokuto Munakata +3
In this paper, we propose Language-Guided Contrastive Audio-Visual Masked Autoencoders (LG-CAV-MAE) to improve audio-visual representation learning. LG-CAV-MAE integrates a pretrai…
Pre-training with Synthetic Patterns for Audio
Yuchi Ishikawa, Tatsuya Komatsu, Yoshimitsu Aoki
In this paper, we propose to pre-train audio encoders using synthetic patterns instead of real audio data. Our proposed framework consists of two key elements. The first one is Mas…
DETECLAP: Enhancing Audio-Visual Representation Learning with Object Information
Shota Nakada, Taichi Nishimura, Hokuto Munakata +2
Current audio-visual representation learning can capture rough object categories (e.g., ``animals'' and ``instruments''), but it lacks the ability to recognize fine-grained details…
Language-based Audio Moment Retrieval
Hokuto Munakata, Taichi Nishimura, Shota Nakada +1
In this paper, we propose and design a new task called audio moment retrieval (AMR). Unlike conventional language-based audio retrieval tasks that search for short audio clips from…
Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection
Taichi Nishimura, Shota Nakada, Hokuto Munakata +1
We propose Lighthouse, a user-friendly library for reproducible video moment retrieval and highlight detection (MR-HD). Although researchers proposed various MR-HD approaches, the…