DISC21 is a benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of image transformations such as automated transformations, hand-crafted image edits and machine-learning based manipulations. This mimics real-life cases appearing in social media, for example for integrity-related problems dealing with misinformation and objectionable content. The strength of the image manipulations, and therefore the difficulty of the benchmark, is calibrated according to the performance of a set of baseline approaches. Both the query and reference set contain a majority of ``distractor'' images that do not match, which corresponds to a real-life needle-in-haystack setting, and the evaluation metric reflects that.
fruit-SALAD is a synthetic image dataset with 10,000 generated images of fruit depictions. This combined semantic category and style benchmark comprises 100 instances each of 10 easily recognizable fruit categories and 10 easy distinguishable styles. See the paper on ArXiv. The carefully designed Style Aligned Artwork Dataset (SALAD) provides a controlled and balanced platform for the comparative analysis of similarity perception of different computational models. The SALAD framework allows the comparison of how these models perform semantic category and style recognition tasks, going beyond the level of anecdotal knowledge, making them robustly quantifiable and qualitatively interpretable. We used Stable Diffusion XL and StyleAligned to create the fruit-SALAD by carefully crafting text prompts and overseing the image generation process. The code to reproduce the fruit-SALAD_10k is available at GitHub. Please note that this dataset is available for academic research purposes only.