6 papers
Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery
Huy Quang Ung, Guillaume Habault, Roberto Legaspi +3
Rapid and accurate post-disaster building damage assessment is essential, yet remains a challenging task. Unmanned Aerial Vehicle (UAV) imagery offers a timely and high-resolution…
Hierarchical Semantic Correlation-Aware Masked Autoencoder for Unsupervised Audio-Visual Representation Learning
Donghuo Zeng, Hao Niu, Masato Taya
Learning aligned multimodal embeddings from weakly paired, label-free corpora is challenging: pipelines often provide only pre-extracted features, clips contain multiple events, an…
Variance & Greediness: A comparative study of metric-learning losses
Donghuo Zeng, Hao Niu, Zhi Li +1
Metric learning is central to retrieval, yet its effects on embedding geometry and optimization dynamics are not well understood. We introduce a diagnostic framework, VARIANCE (int…
Learning Audio-Visual Embeddings with Inferred Latent Interaction Graphs
Donghuo Zeng, Hao Niu, Yanan Wang +1
Learning robust audio-visual embeddings requires bringing genuinely related audio and visual signals together while filtering out incidental co-occurrences - background noise, unre…
CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding
Huy Quang Ung, Guillaume Habault, Yasutaka Nishimura +8
The rise of Visual-Language Models (LVLMs) has unlocked new possibilities for seamlessly integrating visual and textual information. However, their ability to interpret cartographi…
An Empirical Study for Representations of Videos in Video Question Answering via MLLMs
Zhi Li, Yanan Wang, Hao Niu +2
Multimodal large language models have recently achieved remarkable progress in video question answering (VideoQA) by jointly processing visual, textual, and audio information. Howe…