MIEB: Massive Image Embedding Benchmark

Image representations are often evaluated through disjointed, task-specific protocols, leading to a fragmented understanding of model capabilities. For instance, it is unclear whether an image embedding model adept at clustering images is equally good at retrieving relevant images given a piece of text. We introduce the Massive Image Embedding Benchmark (MIEB) to evaluate the performance of image and image-text embedding models across the broadest spectrum to date. MIEB spans 38 languages across 130 individual tasks, which we group into 8 high-level categories. We benchmark 50 models across our benchmark, finding that no single method dominates across all task categories. We reveal hidden capabilities in advanced vision models such as their accurate visual representation of texts, and their yet limited capabilities in interleaved encodings and matching images and texts in the presence of confounders. We also show that the performance of vision encoders on MIEB correlates highly with their performance when used in multimodal large language models. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.

Controllable Image Generation With Composed Parallel Token Prediction

Compositional image generation requires models to generalise well in situations where two or more input concepts do not necessarily appear together in training (compositional generalisation). Despite recent progress in compositional image generation via composing continuous sampling processes such as diffusion and energy-based models, composing discrete generative processes has remained an open challenge, with the promise of providing improvements in efficiency, interpretability and simplicity. To this end, we propose a formulation for controllable conditional generation of images via composing the log-probability outputs of discrete generative models of the latent space. Our approach, when applied alongside VQ-VAE and VQ-GAN, achieves state-of-the-art generation accuracy in three distinct settings (FFHQ, Positional CLEVR and Relational CLEVR) while attaining competitive Fr\'echet Inception Distance (FID) scores. Our method attains an average generation accuracy of $80.71\%$ across the studied settings. Our method also outperforms the next-best approach (ranked by accuracy) in terms of FID in seven out of nine experiments, with an average FID of $24.23$ (an average improvement of $-9.58$). Furthermore, our method offers a $2.3\times$ to $12\times$ speedup over comparable continuous compositional methods on our hardware. We find that our method can generalise to combinations of input conditions that lie outside the training data (e.g. more objects per image) in addition to offering an interpretable dimension of controllability via concept weighting. We further demonstrate that our approach can be readily applied to an open pre-trained discrete text-to-image model without any fine-tuning, allowing for fine-grained control of text-to-image generation.