README.md

---
license: cc-by-4.0
task_categories:
  - image-to-text
  - visual-question-answering
tags:
  - Multimodal benchmark
  - Vision-Language Models
  - Compositionality
  - Localism-aware compositionality
  - Multimodal knowledge editing
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/lace_train.parquet
      - split: test
        path: data/lace_test.parquet
  - config_name: keyword_dict
    data_files:
      - split: train
        path: data/train_keyword_dict.parquet
      - split: test
        path: data/test_keyword_dict.parquet
---

# 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](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html) |
| **License** | CC BY 4.0 |

<!-- 
## Tasks

### 1. LoGoCap — Multi-grained Local and Global Compositional Captioning

LoGoCap evaluates a model's *static selectivity*: can it simultaneously understand the global scene while identifying and grounding constituent local objects?

- **Local captioning**: given an atomic region (a single object marked with a colored bounding box), generate a region-specific caption.
- **Global captioning**: given a compound region (a group of atomic regions), generate a single coherent caption that integrates all constituent local parts while introducing holistic scene-level context (moods, relations, atmosphere) not present in any individual local caption.

Evaluation uses standard captioning metrics (BLEU, ROUGE-1, METEOR) against human-annotated reference captions.

### 2. MMComE — Multimodal Compositional Knowledge Editing

MMComE evaluates a model's *dynamic robustness*: can it apply a localized counterfactual edit (e.g., replacing *referee* with *spectator*) consistently across region-marked images, while preserving all unrelated global semantics?

A multimodal edit request is defined as a tuple `(I, r, ph → ph*)`, where:
- `I` is the image, `r` is the target region
- `ph` is the original phrase to be replaced, `ph*` is the counterfactual substitute

The model is evaluated on whether it correctly reflects `ph*` in both in-scope regions (edited) and correctly retains all out-of-scope regions (unedited).


## Intended Use

LACE-Bench is designed for:

- Evaluating **localism-aware compositionality** — whether VLMs can selectively deploy local and global compositional operations as the task demands
- Measuring **global binding stability**: how consistently local semantic units of atomic regions bind into global captions
- Quantifying **cross-scale interference**: the degree to which local counterfactual edits propagate into unintended global semantic regions
- Benchmarking **fine-tuning strategies** (e.g., LoRA, blur+bbox visual grounding) for compositional captioning -->


## 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**

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


## Citation

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