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Zixin Guo

4 accepted papers

2026

INSTANCERSR: REAL-WORLD SUPER-RESOLUTION VIA INSTANCE-AWARE REPRESENTATION ALIGNMENT

ICASSP 2026poster

Existing real-world super-resolution (RSR) methods based on generative priors have achieved remarkable progress in producing high-quality and globally consistent reconstructions. However, they often struggle to recover fine-grained details of diverse object instances in complex real-world scenes. Th…

Cited by 0SourcePDFScholar
2026

Imagine How To Change: Explicit Procedure Modeling for Change Captioning

ICLR 2026poster

Change captioning generates descriptions that explicitly describe the differences between two visually similar images. Existing methods operate on static image pairs, thus ignoring the rich temporal dynamics of the change procedure, which is the key to understand not only what has changed but also h…

Cited by 0SourcecodeScholar
2025

Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning

EMNLP 2025

Image Difference Captioning (IDC) methods have advanced in highlighting subtle differences between similar images, but their performance is often constrained by limited training data. Using Large Multimodal Models (LMMs) to describe changes in image pairs mitigates data limits but adds noise. These

Cited by 0SourcePDFScholar
2024

TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling

ECCV 2024poster

"Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle these issues, we present TexGen, a novel multi-view sampli…