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20242026
most citedModality-Inconsistent Continual Learning of Multimodal Large Language Models

1 citations · 1 across the 4 of their papers we have counts for

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11 papers

cs.LG2026

Audio-Visual Continual Test-Time Adaptation without Forgetting

Sarthak Kumar Maharana, Akshay Mehra, Bhavya Ramakrishna +2

Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modali…

cs.LG20261 cited

Modality-Inconsistent Continual Learning of Multimodal Large Language Models

Weiguo Pian, Shijian Deng, Shentong Mo +3

In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language Models (MLLMs) that involves tasks with…

cs.CV2026

CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction

Wenjie Zhao, Jia Li, Mingrui Liu +2

``How long can I live and remain free of cancer?'' is often the first question a patient asks after receiving a cancer diagnosis and treatment. Accurate survival prediction helps a…

cs.SD2026

OmniSonic: Towards Universal and Holistic Audio Generation from Video and Text

Weiguo Pian, Saksham Singh Kushwaha, Zhimin Chen +4

In this paper, we propose Universal Holistic Audio Generation (UniHAGen), a task for synthesizing comprehensive auditory scenes that include both on-screen and off-screen sounds ac…

cs.CV2026

A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding

Yue Zhang, Liqiang Jing, Jia Li +4

Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…

cs.CV2025

MetaRank: Task-Aware Metric Selection for Model Transferability Estimation

Yuhang Liu, Wenjie Zhao, Xin Wang +1

Selecting an appropriate pre-trained source model is a critical, yet computationally expensive, task in transfer learning. Model Transferability Estimation (MTE) methods address th…