Multimodal Association

Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background. Multi-Modal-CelebA-HQ can be used to train and evaluate algorithms of text-to-image-generation, text-guided image manipulation, sketch-to-image generation, and GANs for face generation and editing. Source: Multi-Modal CelebA-HQ Dataset Image Source: Xia et al

We introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark, specifically designed to assess the long-context capabilities of MLLMs. Besides multi-image input, we employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. Essentially, MMNeedle evaluates MLLMs by stress-testing their capability to locate a target sub-image (needle) within a set of images (haystack) based on textual instructions and descriptions of image contents. This setup necessitates an advanced understanding of extensive visual contexts and effective information retrieval within long-context image inputs.

A large-scale multi-modal dataset to facilitate research and studies that concentrate on vision-wireless systems. The Vi-Fi dataset is a large-scale multi-modal dataset that consists of vision, wireless and smartphone motion sensor data of multiple participants and passer-by pedestrians in both indoor and outdoor scenarios. In Vi-Fi, vision modality includes RGB-D video from a mounted camera. Wireless modality comprises smartphone data from participants including WiFi FTM and IMU measurements. The presence of Vi-Fi dataset facilitates and innovates multi-modal system research, especially, vision-wireless sensor data fusion, association and localization. (Data collection was in accordance with IRB protocols and subject faces have been blurred for subject privacy.)

Alex-20: contains ~1.3M general inorganic materials curated from the Alexandria database, with energy above the convex hull less than 0.1 eV/atom and no more than 20 atoms in unit cell.

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Recent advances in large language models have led to the development of multimodal LLMs (MLLMs), which take both image data and text as an input. Virtually all of these models have been announced within the past year, leading to a significant need for benchmarks evaluating the abilities of these models to reason truthfully and accurately on a diverse set of tasks. When Google announced Gemini (Gemini Team et al., 2023), they showcased its ability to solve rebuses—wordplay puzzles which involve creatively adding and subtracting letters from words derived from text and images. The diversity of rebuses allows for a broad evaluation of multimodal reasoning capabilities, including image recognition, multi- step reasoning, and understanding the human creator’s intent. We present REBUS: a collection of 333 hand-crafted rebuses spanning 13 diverse cate- gories, including hand-drawn and digital images created by nine contributors. Samples are presented in Table 1. Notably, GPT-4V, the most powerful model we evaluated, answered only 24% of puzzles correctly, highlighting the poor capabilities of MLLMs in new and unex- pected domains to which human reasoning generalizes with comparative ease. Open-source models perform even worse, with a median accuracy below 1%. We notice that models often give faithless explanations, fail to change their minds after an initial approach doesn’t work, and remain highly uncalibrated on their own abilities.

This dataset is a patched version of The Taste & Affect Music Database by D. Guedes et al. It is a set of captions that describe 100 musical pieces and associate with them gustatory keywords on the basis of Guedes findings.