VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
Replicating In-Context Learning (ICL) in computer vision remains challenging due to task heterogeneity. We propose \textbf{VIRAL}, a framework that elicits visual reasoning from a pre-trained image editing model by formulating ICL as conditional generation via visual analogy ($x_s : x_t :: x_q : y_q$). We adapt a frozen Diffusion Transformer (DiT) using role-aware multi-image conditioning and introduce a Mixture-of-Experts LoRA to mitigate gradient interference across diverse tasks. Additionally, to bridge the gaps in current visual context datasets, we curate a large-scale dataset spanning perception, restoration, and editing. Experiments demonstrate that VIRAL outperforms existing methods, validating that a unified V-ICL paradigm can handle the majority of visual tasks, including open-domain editing. Our code is available at https://anonymous.4open.science/r/VIRAL-744A
Code (0)
등록된 구현이 없습니다.
Tasks
Visual ReasoningImage EditingSimilar Papers 제목 키워드 기반
Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model
Visual In-Context Learning (ICL) has emerged as a promising research area due to its capability to accomplish various tasks with limited example pairs through analogical reasoning. However, training-based visual ICL has …
Image InpaintingIn-Context LearningVisual PromptingVisual ReasoningIn-Context Analogical Reasoning with Pre-Trained Language Models
Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. While it is thought to be essential for robust reasoning …
In-Context LearningRelational ReasoningA Relaxed Drift Diffusion Model for Phylogenetic Trait Evolution
Understanding the processes that give rise to quantitative measurements associated with molecular sequence data remains an important issue in statistical phylogenetics. Examples of such measurements include geographic co…
Bayesian InferenceWhen GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit
AI-generated content and misinformation are increasingly prevalent on social networks. While prior research primarily examined textual misinformation, fewer studies have focused on visual content's role in virality. In t…
Towards Analogy-Based Explanations in Machine Learning
Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogi…
BIG-bench Machine LearningInterpretable Machine Learning