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20232026
most citedLearning Mask-aware CLIP Representations for Zero-Shot Segmentation

13 citations · 28 across the 18 of their papers we have counts for

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cs.CV2026

Let ViT Speak: Generative Language-Image Pre-training

Yan Fang, Mengcheng Lan, Zilong Huang +7

In this paper, we present \textbf{Gen}erative \textbf{L}anguage-\textbf{I}mage \textbf{P}re-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers…

cs.CV2025

AlignGen: Boosting Personalized Image Generation with Cross-Modality Prior Alignment

Yiheng Lin, Shifang Zhao, Ting Liu +4

Personalized image generation aims to integrate user-provided concepts into text-to-image models, enabling the generation of customized content based on a given prompt. Recent zero…

cs.CV2025

PreFM: Online Audio-Visual Event Parsing via Predictive Future Modeling

Xiao Yu, Yan Fang, Xiaojie Jin +2

Audio-visual event parsing plays a crucial role in understanding multimodal video content, but existing methods typically rely on offline processing of entire videos with huge mode…

cs.CV2025

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning

Shifang Zhao, Yiheng Lin, Lu Han +2

While anomaly detection has made significant progress, generating detailed analyses that incorporate industrial knowledge remains a challenge. To address this gap, we introduce Omn…

cs.CV2025

3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation

Wenxin Chen, Mengxue Qu, Weitai Kang +3

3D Referring Expression Segmentation (3D-RES) typically requires extensive instance-level annotations, which are time-consuming and costly. Semi-supervised learning (SSL) mitigates…

cs.CV2025★ 2 cited

IPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental Segmentation

Xiao Yu, Yan Fang, Yao Zhao +1

Class incremental learning aims to enable models to learn from sequential, non-stationary data streams across different tasks without catastrophic forgetting. In class incremental…