Vision-language
Touch-Vision-Language-Dataset
A Touch, Vision, and Language Dataset for Multimodal Alignment
by Max (Letian) Fu, Gaurav Datta*, Huang Huang*, William Chung-Ho Panitch*, Jaimyn Drake*, Joseph Ortiz, Mustafa Mukadam, Mike Lambeta, Roberto Calandra, Ken Goldberg at UC Berkeley, Meta AI, TU Dresden and CeTI (*equal contribution).
[Paper] | [Project Page] | [Checkpoints] | [Dataset] | [Citation]
This repo contains the dataset for A Touch, Vision, and Language Dataset for Multimodal Alignment.… See the full description on the dataset page: https://huggingface.co/datasets/mlfu7/Touch-Vision-Language-Dataset.VISION_LANGUAGEA key question for understanding multimodal vs. language capabilities of models is what is
the relative strength of the spatial reasoning and understanding in each modality, as spatial understanding is
expected to be a strength for multimodality? To test this we created a procedurally generatable, synthetic dataset
to testing spatial reasoning, navigation, and counting. These datasets are challenging and by
being procedurally generated new versions can easily be created to combat the effects… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/VISION_LANGUAGE.calame-pt
CALAME-PT
Context-Aware LAnguage Modeling Evaluation for Portuguese
CALAME-PT is a PT benchmark composed of small texts (contexts) and their respective last words.
These contexts should, in theory, contain enough information so that a human or a model is capable of guessing its last word - without being too specific and/or too ambiguous.
Composition
CALAME-PT is composed of 2 "sets" of data - handwritten and generated.
Handwritten Set: contains 406… See the full description on the dataset page: https://huggingface.co/datasets/NOVA-vision-language/calame-pt.multimodal-vision-language-video-models-2026
👁️ Multimodal Vision-Language & Video Foundation Models Dataset (2026 Edition)
A structured research dataset featuring 1,000 domain-verified research papers and code repositories focused on Multimodal Vision-Language Models (VLM), Video Foundation Models, Diffusion Transformers (DiT), Visual Grounding, and World Simulators.
Built with Universal Scientific Engine V15.1 Gold, providing 47 schema attributes with verified repository attribution, modality capability matrix, vision… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/multimodal-vision-language-video-models-2026.Spatial-Blind-Spots-in-Vision-Language-Modelslicense: mit
model_evaluated:
name: Qwen3-VL-2B-Instruct
url: https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct
evaluation_notebook:
https://www.kaggle.com/code/wajidhassanmoosa/blind-spot-qwen3-2b
evaluation_setup: |
The model evaluated in this study is Qwen3-VL-2B-Instruct.
Evaluation was conducted using the Hugging Face Transformers library
with automatic device mapping (device_map="auto") and "bfloat16" dtype selection.
For each example:
The image was provided as part of a… See the full description on the dataset page: https://huggingface.co/datasets/hassan-wajid/Spatial-Blind-Spots-in-Vision-Language-Models.visionlanguagemodelog