Common Inpainted Objects In-N-Out of Context (2506.00721v1)

Abstract: We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective context learning. Each inpainted object is meticulously verified and categorized as in- or out-of-context through a multimodal LLM assessment. Our analysis reveals significant patterns in semantic priors that influence inpainting success across object categories. We demonstrate three key tasks enabled by COinCO: (1) training context classifiers that effectively determine whether existing objects belong in their context; (2) a novel Objects-from-Context prediction task that determines which new objects naturally belong in given scenes at both instance and clique levels, and (3) context-enhanced fake detection on state-of-the-art methods without fine-tuning. COinCO provides a controlled testbed with contextual variations, establishing a foundation for advancing context-aware visual understanding in computer vision and image forensics. Our code and data are at: https://github.com/YangTianze009/COinCO.

An Examination of "Common Inpainted Objects In-N-Out of Context"

The paper "Common Inpainted Objects In-N-Out of Context" introduces a dataset referred to as mytitlecolor, which seeks to address the limitations in current visual datasets related to the depiction of out-of-context scenes. Through the innovative application of diffusion-based inpainting techniques on the COCO dataset, this research generates a substantial set of 97,722 images containing both context-respecting and context-violating scenes. This synthesized data allows for an in-depth exploration into the effect of semantic priors on inpainting tasks, providing a framework for a range of applications including context classification and fake detection.

Key Contributions and Methodology

The introduction of the mytitlecolor dataset stands as a primary contribution of this paper. This resource enhances the COCO dataset by introducing variations through contextually deliberate manipulations, offering a rich ground for advancing contextual understanding in computer vision. The process involves the replacement of a single object in each original image, via Stable Diffusion's inpainting model, ensuring the maintenance of the scene's overall structure while introducing contextual inconsistency.

The research employs state-of-the-art Multimodal LLMs (MLLMs) for the assessment of whether inpainted objects are contextually appropriate. This task allows for classification into in-context and out-of-context categories, using criteria grounded in established visual coherence principles—location, size, and co-occurrence. This evaluation underscores the critical role of semantic reasoning in context evaluation.

Dataset Analysis and Verification

An important aspect of the paper is the analysis of semantic priors in inpainting success probabilities, revealing notable biases depending on object categories. The research reveals clusters of semantic coherence, providing a novel insight into the capabilities and limitations of diffusion models in rendering objects that align with typical semantic and contextual expectations. Manual verification of a subset of images supports the reliability of this automated detection and classification process, corroborating its efficacy through human-machine agreement on context classification.

Application and Model Development

The paper introduces two novel tasks that leverage the mytitlecolor dataset: context classification and an Objects-from-Context prediction task. The context classifier aims to discern whether objects fit contextually within their scenes by employing a model that processes both visual and semantic features. Despite showing reasonable performance, the results highlight areas for future improvements in context-aware modeling.

The Objects-from-Context task is framed as predicting which objects could naturally integrate into a given scene, both at instance and clique levels. This offers a promising step forward in understanding contextual object placement and scene synthesis.

Enhancements in Image Manipulation Detection

A remarkable application of the dataset is in fake detection, particularly by enhancing the contextual reasoning capabilities of existing models. The integration of context-based insights into fake detection pipelines notably improves the detection and localization of manipulated regions without necessitating fine-tuning, thus demonstrating the complementary nature of semantic and synthetic analysis.

Implications and Future Directions

The implications of this paper are manifold, fundamentally enriching the domain of contextual analysis in computer vision. By expanding the scope of available datasets to include nuanced context manipulation, this research bridges the gap in training data availability for context-aware algorithms. Additionally, it offers potential improvements in image forensics, ultimately contributing to more robust methodologies for detecting digital content manipulation.

While the paper effectively establishes a foundation for these advancements, it acknowledges the inherent subjectivity in context assessment and the constraints of current categorical frameworks. Future work could explore adaptive, open-vocabulary systems for context tasks, paving the way for even broader applications in diverse visual scenarios.

In summary, this paper significantly contributes to the field of computer vision by introducing a robust dataset that enhances contextual diversity, and by demonstrating how contextual information can be effectively leveraged in both analytical and practical applications.