6 papers
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…
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…
CoTasks: Chain-of-Thought based Video Instruction Tuning Tasks
Yanan Wang, Julio Vizcarra, Zhi Li +2
Despite recent progress in video large language models (VideoLLMs), a key open challenge remains: how to equip models with chain-of-thought (CoT) reasoning abilities grounded in fi…
GTS-LUM: Reshaping User Behavior Modeling with LLMs in Telecommunications Industry
Liu Shi, Tianwu Zhou, Wei Xu +6
As telecommunication service providers shifting their focus to analyzing user behavior for package design and marketing interventions, a critical challenge lies in developing a uni…