most citedZooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

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cs.CV20261 cited

Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

Lvpan Cai, Haowei Wang, Jiayi Ji +4

The rise of AI-generated image tools has made localized forgeries increasingly realistic, posing challenges for visual content integrity. Although recent efforts have explored loca…

cs.CV2024

RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation

Changli Wu, Qi Chen, Jiayi Ji +7

3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating referring expressions with point clouds. However, traditional approaches frequently encounte…

cs.CV2024

3D-GRES: Generalized 3D Referring Expression Segmentation

Changli Wu, Yihang Liu, Jiayi Ji +6

3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches…

cs.CV2024

X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation

Yiwei Ma, Yijun Fan, Jiayi Ji +5

In recent times, automatic text-to-3D content creation has made significant progress, driven by the development of pretrained 2D diffusion models. Existing text-to-3D methods typic…

cs.CV2024

Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model

Danni Yang, Ruohan Dong, Jiayi Ji +4

Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have…

cs.CV2024

SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation

Danni Yang, Jiayi Ji, Yiwei Ma +4

In this paper, we introduce SemiRES, a semi-supervised framework that effectively leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle in appl…