LACE-Bench: Localism-Aware Compositionality Evaluation Benchmark for Vision-Language Models

LACE-Bench is a benchmark for evaluating localism-aware compositionality in vision-language models (VLMs) — the ability to selectively integrate local region-level semantics with global scene-level understanding. It comprises two complementary tasks: LoGoCap and MMComE.

Dataset Card

Field	Info
Tasks	LoGoCap (Local & Global Compositional Captioning), MMComE (Multimodal Compositional Knowledge Editing)
Modality	Vision-Language
Split	Train (9,874 images) / Test (2,183 images)
Total	12,057 images
Image Source	Visual Genome
License	CC BY 4.0

Data Fields

Each record corresponds to one image and contains the following fields:

Field	Type	Description
image_id	string	Visual Genome image identifier
regions	list[object]	Annotated bounding box regions (atomic)
narratives	string	Description of the full image
keywords	list[object]	Key noun concepts grounded in WordNet
relation_centric_regions	list[object]	Groups of region IDs with a human-written relational annotation

regions

Each atomic region corresponds to a single object marked with a distinct colored bounding box.

Key	Type	Description
id	string	Region identifier ({image_id}_{region_index})
color	string	Bounding box color used for visual grounding (aqua / yellow / lime / red / blue / orange / magenta)
x, y	float	Top-left corner coordinates of the bounding box
width, height	float	Width and height of the bounding box
captions	list[object]	Human-annotated region-level captions (see below)
object_ids	list[int]	Linked object IDs from Visual Genome
relationships	list[object]	Scene graph relationships associated with this region (see below)

regions[].captions

Key	Type	Description
caption	string	Original human-written caption for the region (e.g. "the tall clock on the street")
counterfactual_caption	string	Minimally edited caption where one noun is replaced with a plausible but incorrect alternative (e.g. "the tall dart board on the street")

regions[].relationships

Key	Type	Description
relationship_id	int	Visual Genome relationship identifier
predicate	string	Relation predicate between subject and object (e.g. "on")
synsets	list[string]	WordNet synsets for the predicate (e.g. ["along.r.01"])
subject_id	int	Visual Genome object ID of the subject
object_id	int	Visual Genome object ID of the object

---

keywords

Each entry represents a key noun concept extracted from region captions and grounded in WordNet.

Key	Type	Description
synset_id	string	WordNet synset identifier (e.g. clock.n.01)
synonyms	list[string]	Lemma names belonging to this synset (e.g. ["clock"])
nearest_ancestor	string	Closest hypernym synset in the WordNet hierarchy (e.g. timepiece.n.01)
supersense	string	Broad semantic category from WordNet lexicographer files (e.g. noun.artifact, noun.person)
counterfactual	list[object]	Human-annotated counterfactual substitutions for this concept (see below)

keywords[].counterfactual

Key	Type	Description
human_annotation	string	Plausible but incorrect substitute chosen by a human annotator (e.g. "dart board")
candidate	list[string]	Candidate substitutions presented to the annotator for selection

counterfactual is empty ([]) for concepts where no counterfactual annotation was collected.

---

relation_centric_regions

Each entry groups multiple atomic regions and provides a human-written description of the relational context among them.

Key	Type	Description
human_annotation	string	Free-form description of the spatial or semantic relationship among the grouped regions (e.g. "The central clock tower... stands as a focal point against the backdrop of the building's pillars.")
region_ids	list[string]	IDs of the atomic regions involved in this relational group (e.g. ["2358647_0", "2358647_1"])

keyword_dict Config

In addition to the per-image records (default config), the dataset ships a keyword_dict config that maps each WordNet synset ID to the list of surface phrases observed for that concept across the corresponding split's region captions. These dictionaries are useful for keyword-based lookup, counterfactual phrase matching, and lexical normalization of mentions.

Files

• data/train_keyword_dict.parquet
• data/test_keyword_dict.parquet

Schema

Field	Type	Description
synset_id	string	WordNet synset identifier (e.g. tree.n.01), matching keywords[].synset_id in the default config
phrases	list[string]	All distinct surface phrases (synonyms, plural forms, modifier-noun variants, casing variants) observed for the synset in that split

Example rows

synset_id	phrases
leaf.n.01	["leaves", "foliage", "leaf", "banana leaf", "dried leaves", "green leaves", ...]
tree.n.01	["tree", "trunk", "evergreen tree", "pine trees", "fir tree", ...]
bus.n.01	["bus", "city bus", "double decker bus", "motorbus", "coach", ...]

Loading

from datasets import load_dataset
ds = load_dataset("lacebench/LACE-Bench", "keyword_dict")

Citation

@dataset{anonymous2026lacebench,
  title  = {LACE-Bench: Localism-Aware Compositionality Evaluation Benchmark for Vision-Language Models},
  author = {Anonymous},
  year   = {2026},
}