6 citations · 7 across the 11 of their papers we have counts for
14 papers
Paraphrase Consensus Is Not Self-Consistency: Instruction Reliance and Robustness in VLA Policies
Hanlong Li, Sihan Tan, Takeshi Ashizawa +2
Vision-language-action (VLA) policies fine-tuned on LIBERO are trained and evaluated on the same ten instruction strings, and rewording the instruction changes base OpenVLA's succe…
BCNav: Bearing-Conditioned Depth Policies for Sound Source Navigation
Yaozhong Kang, Jiang Wang, Takeshi Ashizawa +2
The ability to navigate toward sound sources extends a robot's reach beyond its visual field, enabling response to auditory events in unknown environments. To equip robots with thi…
AMBIT: Anticipatory Multimodal Body Recruitment for Bimanual Tracking on a Humanoid
Hanlong Li, Sihan Tan, Takeshi Ashizawa +2
A humanoid with 5-DoF arms cannot track generic bimanual end-effector trajectories with its arms alone; pelvis and waist motion must be recruited, but which motion, and when, is no…
UNITE-AUDIO: Joint Learning of Continuous Tokenization and Latent Flow Matching for Text-to-Audio Generation
Runwu Shi, Kai Li, Yujin Wang +8
Text-to-audio (TTA) generation aims to synthesize realistic audio that faithfully reflects natural-language descriptions. Most TTA systems adopt a two-stage latent paradigm: an aud…
Exploring Efficient Waveform Diffusion Models for Foley Sound Generation
Runwu Shi, Chang Li, Jiahui Li +7
Recent advances in diffusion models have enabled high-fidelity Foley sound generation directly in the waveform space. Existing waveform diffusion models primarily rely on time-doma…
From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation
Nabeela Khan, Bowen Wu, Runwu Shi +5
Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment o…
What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study
Yaozhong Kang, Jiang Wang, Runwu Shi +3
Neural networks outperform classical GCC-PHAT for Time-Difference-of-Arrival (TDOA) estimation in noise and reverberation, yet their internal strategy remains unexplored. To uncove…
Fast-SDE: Efficient Single-Microphone Sound Source Distance Estimation in Reverberant Environments
Jiang Wang, Runwu Shi, Yaozhong Kang +3
Sound source distance estimation (SDE) is a critical capability in human-robot interaction. An inappropriate interaction distance not only reduces the reliability of speech acquisi…
Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data
Ragib Amin Nihal, Benjamin Yen, Runwu Shi +2
Training data for bioacoustics is scattered across taxa, regions, and institutions. Centralizing it all is often infeasible. We show that independently fine-tuned BEATs encoders ca…
Unsupervised Single-Channel Audio Separation with Diffusion Source Priors
Runwu Shi, Chang Li, Jiang Wang +5
Single-channel audio separation aims to separate individual sources from a single-channel mixture. Most existing methods rely on supervised learning with synthetically generated pa…
Cross-Attention with Confidence Weighting for Multi-Channel Audio Alignment
Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa +1
Multi-channel audio alignment is a key requirement in bioacoustic monitoring, spatial audio systems, and acoustic localization. However, existing methods often struggle to address…
Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis
Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa +1
Marine mammal vocalization analysis depends on interpreting bioacoustic spectrograms. Vision Language Models (VLMs) are not trained on these domain-specific visualizations. We inve…
Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data
Ragib Amin Nihal, Benjamin Yen, Runwu Shi +2
Passive acoustic monitoring (PAM) systems generate continuous recordings spanning months, yet automated bioacoustic analysis of whale calls requires two separate annotation efforts…
From Blurry to Brilliant Detection: YOLO-Based Aerial Object Detection with Super Resolution
Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa +2
Aerial object detection presents challenges from small object sizes, high density clustering, and image quality degradation from distance and motion blur. These factors create an i…