1 citations · 1 across the 9 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
From Pixel to Mask: A Survey of Out-of-Distribution Segmentation
Wenjie Zhao, Jia Li, Yunhui Guo
Out-of-distribution (OoD) detection and segmentation have attracted growing attention as concerns about AI security rise. Conventional OoD detection methods identify the existence…
: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time
Sarthak Kumar Maharana, Saksham Singh Kushwaha, Baoming Zhang +4
While recent audio-visual models have demonstrated impressive performance, their robustness to distributional shifts at test-time remains not fully understood. Existing robustness…
H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detection
Yuhang Liu, Wenjie Zhao, Yunhui Guo
Task Incremental Learning (TIL) is a specialized form of Continual Learning (CL) in which a model incrementally learns from non-stationary data streams. Existing TIL methodologies…
Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?
Jia Li, Wenjie Zhao, Ziru Huang +2
Unlike traditional visual segmentation, audio-visual segmentation (AVS) requires the model not only to identify and segment objects but also to determine whether they are sound sou…
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…
Continual Audio-Visual Sound Separation
Weiguo Pian, Yiyang Nan, Shijian Deng +3
In this paper, we introduce a novel continual audio-visual sound separation task, aiming to continuously separate sound sources for new classes while preserving performance on prev…