--- license: cc-by-4.0 pretty_name: SOCO-LVLM viewer: false task_categories: - visual-question-answering tags: - computer-vision - multimodal - object-correspondence - synthetic-data --- # SOCO-LVLM SOCO-LVLM provides multiple-choice semantic object correspondence evaluation data for LVLMs. This is the SOCO-LVLM v1 release, derived from SOCOv1. The original SOCO correspondence benchmark is available in the [GenIntelLab/SOCO](https://huggingface.co/datasets/GenIntelLab/SOCO) dataset repository. ## Repository Layout ```text GenIntelLab/SOCO-LVLM SOCO_LVLM/ soco_lvlm_img.tsv soco_lvlm_imgtxt.tsv soco_lvlm_txt.tsv README.md ``` ## Variants - `soco_lvlm_img.tsv`: image-input evaluation variant (approximately 3.24 GB). - `soco_lvlm_imgtxt.tsv`: image-and-text evaluation variant (approximately 3.24 GB). - `soco_lvlm_txt.tsv`: text-input evaluation variant (approximately 1.63 GB). Each TSV uses the columns `question`, `image`, `image_path`, `answer`, `index`, `g_index`, `qid`, `category`, `A`, `B`, `C`, and `D`. ## Download Install the Hub client: ```bash pip install -U huggingface_hub ``` Download all three variants: ```bash hf download GenIntelLab/SOCO-LVLM --repo-type dataset --local-dir SOCO-LVLM ``` Download only one variant in Python: ```python from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="GenIntelLab/SOCO-LVLM", repo_type="dataset", filename="SOCO_LVLM/soco_lvlm_img.tsv", ) ``` Replace the filename with `soco_lvlm_imgtxt.tsv` or `soco_lvlm_txt.tsv` to select a different evaluation variant. ## Citation ```bibtex @misc{duenkel2026soco, title = {SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models}, author = {D{\"u}nkel, Olaf and Sunagad, Basavaraj and Wang, Haoran and Hoffmann, David T. and Theobalt, Christian and Kortylewski, Adam}, year = {2026}, eprint = {2605.31597}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2605.31597} } ```