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Lionel Ni

5 accepted papers

2026

LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence

ICLR 2026poster

The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in a fundamental compromise on weakened inversion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities, suppressing high-fidel…

Cited by 0SourceScholar
2026

Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

ICLR 2026poster

Text-guided color editing in images and videos is a fundamental yet unsolved problem, requiring fine-grained manipulation of color attributes, including albedo, light source color, and ambient lighting, while preserving physical consistency in geometry, material properties, and light-matter interact…

Cited by 0SourceScholar
2024

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

ICLR 2024poster

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning. These issues could be partially addressed by introducing external knowledge graphs (KG) in LLM…

Cited by 275SourcePDFScholar
2023

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

ICLR 2023poster

We present DINO (DETR with Improved deNoising anchOr boxes), a strong end-to-end object detector. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a look forward twice scheme for box prediction, and a mixed query selection…