89 datasets found

Face aging techniques have used generative adversarial networks (GANs) and style transfer learning to transform one’s appearance to look younger/older. Identity is maintained by...

The LLaVA 158k dataset is a large-scale multimodal learning dataset, which is used for training and testing multimodal large language models.

The MMR benchmark is designed to evaluate MLLMs' comprehension of visual content and robustness against misleading questions, ensuring models truly leverage multimodal inputs...

Multi-ZOL is a Chinese dataset for Target-oriented Multimodal Sentiment Classification (TMSC). The dataset contains text and image data, where the text data is used to determine...

Twitter15 and Twitter17 are two English datasets for Target-oriented Multimodal Sentiment Classification (TMSC). The datasets contain text and image data, where the text data is...

The Hateful Memes Dataset consists of a training set of 8500 images, a dev set of 500 images & a test set of 1000 images. The meme text is present on the images, but also...

Unpaired vision-language pre-training via cross-modal CutMix.

Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments.

Openclip: A large-scale multimodal dataset for vision and language understanding.

InternLM2 is a vision-language large model that supports images with any aspect ratio from 336 pixels up to 4K HD, facilitating its deployment in real-world contexts.

FocusCLIP: Multimodal Subject-Level Guidance for Zero-Shot Transfer in Human-Centric Tasks. This paper introduces FocusCLIP, an enhancement for CLIP pretraining using a new ROI...

The dataset used in this paper is a custom dataset generated for fine-tuning open-sourced models.

The NTU-RGBD dataset is a large-scale dataset for 3D human activity analysis, containing 56,000 videos and 60 actions performed by 40 people from 80 different views.

QVHighlights is a dataset for video highlight detection, which consists of over 10,000 videos annotated with human-written text queries.

The WavCaps dataset contains chatGPT-assisted weakly-labeled audio captioning data.

The Multimodal Visual Patterns (MMVP) benchmark is a dataset used to evaluate the visual question answering capabilities of multimodal large language models (MLLMs).

Degree datasets are constructed by gradually adjusting the degree of alignment between image and text.

The dataset used in the paper is a multimodal learning task for robots.

A benchmark for evaluating the perception ability of Large Vision-Language Models (LVLMs) via various subtasks and scenarios.

Dysca is a dynamic and scalable benchmark for evaluating the perception ability of Large Vision-Language Models (LVLMs) via various subtasks and scenarios.