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Shijie Huang

5 accepted papers

2025

ArtEditor: Learning Customized Instructional Image Editor from Few-Shot Examples

ICCV 2025poster

We introduce ArtEditor, a novel framework for instruction-based image editing that learns unique editing styles from few-shot examples. While image editing has seen significant advancements, customized instructional editing remains underexplored. Existing methods often rely on complex, multi-stage p…

2025

Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering

NeurIPS 2025poster

Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents (text, images, tables) and performing cross-modal reasoning. Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches, frequently missi…

Cited by 0SourcecodeScholar
2025

WLuav: An Air-Ground Robot with High Ground Adaptability and Trajectory Tracking Performance

IROS 2025

Air-ground robots have received more and more attention and applications due to their air-to-ground motion performance and excellent energy efficiency. However, airground robots have many gaps including complex structure mechanisms, low terrain adaptability and low-precision controllers to significa

Cited by 0SourceScholar
2024

1DFormer: A Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking

IJCAI 2024poster

Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for sequential and structural modeling of multi…

Cited by 0SourcePDFScholar
2024

NaMa: Neighbor-Aware Multi-Modal Adaptive Learning for Prostate Tumor Segmentation on Anisotropic MR Images

AAAI 2024technical

Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods 1) fail to adaptively assess subject-specific informat…

Cited by 2SourcePDFScholar