Show Me Examples: Inferring Visual Concepts from Image Sets

Researchers introduced Visual Concept Inference from Sets (VICIS), a task evaluating vision-language models' ability to infer shared concepts from image sets. They found state-of-the-art VLMs perform poorly and proposed a training framework that generates more accurate and diverse outputs, generalizing to unseen concepts and modalities.

content type paper /research/ published July 2026 Show Me Examples: Inferring Visual Concepts from Image Sets AuthorsNick Stracke†, Kolja Bauer†, Josh Susskind, Miguel Angel Bautista Martin, Björn Ommer† Show Me Examples: Inferring Visual Concepts from Image Sets AuthorsNick Stracke†, Kolja Bauer†, Josh Susskind, Miguel Angel Bautista Martin, Björn Ommer† Vision-language models VLMs can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, current models fail to infer shared concepts from sets of example images and apply them to new inputs. We introduce Visual Concept Inference from Sets VICIS , a task that evaluates this capability. Given a small context set of images sharing a concept and a query image, the model must generate new images that preserve the context-defined concept while remaining consistent with the query. We show that state-of-the-art VLMs perform poorly on this task, often ignoring the visual context or defaulting to biased generations. To address this gap, we propose a training framework and architecture that learn to infer visual concepts from image sets and extract concept-specific embeddings from queries. Experiments on synthetic data and large-scale ImageNet/WordNet data show that our model generates more accurate and diverse outputs and generalizes to unseen concepts and modalities such as sketches. Finding Experts in Transformer Models October 12, 2021 research area Methods and Algorithms /research/?domain=Methods%20and%20Algorithms , research area Speech and Natural Language Processing /research/?domain=Speech%20and%20Natural%20Language%20Processing conference BayLearn /research/?event=BayLearn In this work we study the presence of expert units in pre-trained Transformer Models TM , and how they impact a model’s performance. We define expert units to be neurons that are able to classify a concept with a given average precision, where a concept is represented by a binary set of sentences containing the concept or not . Leveraging the OneSec dataset Scarlini et al., 2019 , we compile a dataset of 1641 concepts that allows diverse… Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.