2 citations · 3 across the 20 of their papers we have counts for
13 papers · 1 filter
From "What-If" to "What-Is": Counterfactual Thinking-Inspired Semantic Alignment for Visual Brain Decoding
Kaitao Yan, Chi Liu, Congcong Zhu +5
Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to studying how visual information…
Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence
Ling Lin, Yang Bai, Congcong Zhu +6
Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks. However, their open-ended reasoning pro…
One Video, One World: Turning Monocular Video into Physical 4D Scenes
Junhao Chen, Boran Zhang, Mingjin Chen +7
We introduce \textbf{OVOW}, the first training-free system that reconstructs \emph{instance-level, simulation-ready} 4D mesh scenes from a single monocular video. Recent 4D reconst…
Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment
Liwen Yu, Chi Liu, Xiaotong Han +3
Automated Aesthetic Quality Assessment (AQA) treats images primarily as static pixel vectors, aligning predictions with human-rating scores largely through semantic perception. How…
OODBench: Out-of-Distribution Benchmark for Large Vision-Language Models
Ling Lin, Yang Bai, Heng Su +5
Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and i…
Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field
Haoqin Hong, Ding Fan, Fubin Dou +4
Recently, 3D Gaussian Splatting (3DGS), an explicit scene representation technique, has shown significant promise for dynamic novel-view synthesis from monocular video input. Howev…
Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method
Chi Liu, Jincheng Liu, Congcong Zhu +5
Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize…
Causal Fingerprints of AI Generative Models
Hui Xu, Chi Liu, Congcong Zhu +3
AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for source attribution. Prior methods r…
Stage-wise Adaptive Label Distribution for Facial Age Estimation
Bo Wu, Zhiqi Ai, Jun Jiang +2
Label ambiguity poses a significant challenge in age estimation tasks. Most existing methods address this issue by modeling correlations between adjacent age groups through label d…
STSA: Spatial-Temporal Semantic Alignment for Visual Dubbing
Zijun Ding, Mingdie Xiong, Congcong Zhu +1
Existing audio-driven visual dubbing methods have achieved great success. Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly de…
Reasoning Structural Relation for Occlusion-Robust Facial Landmark Localization
Congcong Zhu, Xiaoqiang Li, Jide Li +2
In facial landmark localization tasks, various occlusions heavily degrade the localization accuracy due to the partial observability of facial features. This paper proposes a struc…
Unsupervised Learning of Multi-level Structures for Anomaly Detection
Songmin Dai, Jide Li, Lu Wang +3
The main difficulty in high-dimensional anomaly detection tasks is the lack of anomalous data for training. And simply collecting anomalous data from the real world, common distrib…
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos
Congcong Zhu, Hao Liu, Zhenhua Yu +1
In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing…