activity
20232026
most citedUnveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA

2 citations · 5 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV2026

How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation

Jia Li, Wenjie Zhao, Fnu Atisri +5

Egocentric gaze estimation is commonly studied using models that process the full video with access to future frames, while real-world applications require strictly causal, online…

cs.SD2026

Do Joint Audio-Video Generation Models Understand Physics?

Zijun Cui, Xiulong Liu, Hao Fang +8

Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate…

cs.CV2026

Audio-Visual Intelligence in Large Foundation Models

You Qin, Kai Liu, Shengqiong Wu +12

Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that can perceive, generate…

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

Omni-MMSI: Toward Identity-attributed Social Interaction Understanding

Xinpeng Li, Bolin Lai, Hardy Chen +5

We introduce Omni-MMSI, a new task that requires comprehensive social interaction understanding from raw audio, vision, and speech input. The task involves perceiving identity-attr…

cs.CV2026

ARGaze: Autoregressive Transformers for Online Egocentric Gaze Estimation

Jia Li, Wenjie Zhao, Shijian Deng +6

Online egocentric gaze estimation predicts where a camera wearer is looking from first-person video using only past and current frames, a task essential for augmented reality and a…

cs.CV2025

Explainable AI-Generated Image Detection RewardBench

Michael Yang, Shijian Deng, William T. Doan +4

Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces…

cs.CV2025

Toward Gaze Target Detection of Young Autistic Children

Shijian Deng, Erin E. Kosloski, Siva Sai Nagender Vasireddy +6

The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of profes…

cs.CL2025

Self-Improvement in Multimodal Large Language Models: A Survey

Shijian Deng, Kai Wang, Tianyu Yang +2

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms…

cs.CV2025

Towards Online Multi-Modal Social Interaction Understanding

Xinpeng Li, Shijian Deng, Bolin Lai +3

In this paper, we introduce a new problem, Online-MMSI, where the model must perform multimodal social interaction understanding (MMSI) using only historical information. Given a r…

cs.LG2024★ 1 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.CL2024★ 1 cited

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

Shijian Deng, Wentian Zhao, Yu-Jhe Li +4

Self-improvement in multimodal large language models (MLLMs) is crucial for enhancing their reliability and robustness. However, current methods often rely heavily on MLLMs themsel…

cs.CV2024

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…

cs.CV2024★ 1 cited

AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation

Kai Wang, Shijian Deng, Jing Shi +2

Recent Diffusion Transformers (DiTs) have shown impressive capabilities in generating high-quality single-modality content, including images, videos, and audio. However, it is stil…

eess.AS2024

Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition

Shijian Deng, Erin E. Kosloski, Siddhi Patel +9

In this article, we introduce a novel problem of audio-visual autism behavior recognition, which includes social behavior recognition, an essential aspect previously omitted in AI-…

cs.CV2023

Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware Sound Separation

Yiyang Su, Ali Vosoughi, Shijian Deng +2

The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view. Current methods struggle with such sounds la…

cs.CV2023★ 2 cited

Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA

Ali Vosoughi, Shijian Deng, Songyang Zhang +3

To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to…