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20232026
most cited2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision

1 citations · 1 across the 5 of their papers we have counts for

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cs.CV2026

Programmable World Model

Zheng-Hui Huang, Guixu Lin, Jiacheng Lin +8

Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent world state and enforcing prog…

cs.CV2026

Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution

Ren Wang, Yung-Yu Chuang

The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although diffusion-based SR methods excel at generating perceptually realis…

cs.CV2026

Reflection Separation from a Single Image via Joint Latent Diffusion

Zheng-Hui Huang, Zhixiang Wang, Yu-Lun Liu +1

Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to recover both layers in glare or…

cs.CV2026

Generative World Renderer

Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan +6

Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge thi…

cs.CV2024

Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation

Ji-Jia Wu, Andy Chia-Hao Chang, Chieh-Yu Chuang +6

This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs wit…

cs.CV2023★ 1 cited

2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision

Cheng-Kun Yang, Min-Hung Chen, Yung-Yu Chuang +1

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3…