Papers › VASR: Visual Analogies of Situation Recognition

VASR: Visual Analogies of Situation Recognition

8 Dec 2022arXiv:2212.04542archive 2025-07-28

Yonatan Bitton, Ron Yosef, Eli Strugo, Dafna Shahaf, Roy Schwartz, Gabriel Stanovsky

A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/

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calculate_accuracy vasr-dataset/vasr/experiments/utils.py official repository unverified MIT (permissive) · a9c309927ba2e283 · report
calculate_size_proportions vasr-dataset/vasr/dataset/pipeline/C_filter_visual.py official repository unverified MIT (permissive) · a09718f9096a1801 · report
count_intersecting_diff_key_AB vasr-dataset/vasr/dataset/pipeline/A_find_AB_pairs.py official repository unverified MIT (permissive) · 8da4ede1807a6c92 · report
dump_train_info vasr-dataset/vasr/experiments/utils.py official repository unverified MIT (permissive) · 5b8a29188c340a69 · report
filter_by_legit_pairs_and_sample vasr-dataset/vasr/dataset/pipeline/B_filter_textual.py official repository unverified MIT (permissive) · 40605229814712da · report
get_bbox_of_diff_item vasr-dataset/vasr/dataset/pipeline/C_filter_visual.py official repository unverified MIT (permissive) · daadf88214823ae9 · report
run_trained_scores vasr-dataset/vasr/experiments/run_zero_shot.py official repository unverified MIT (permissive) · 8753077ca7bddb4c · report
train_epoch vasr-dataset/vasr/experiments/run_trainable.py official repository unverified MIT (permissive) · f93a5d6a3488c688 · report

Tasks

Common Sense ReasoningVisual AnalogiesVisual Commonsense ReasoningVisual Reasoning

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Datasets

Introduced by this paper, per the archive.

VASR

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Visual Reasoning VASR Swin 1:1 Accuracy 52.9 #1 of 4 Archive leaderboard report
Visual Reasoning VASR ConvNeXt 1:1 Accuracy 51.2 #2 of 4 Archive leaderboard report
Visual Reasoning VASR ViT 1:1 Accuracy 50.3 #3 of 4 Archive leaderboard report
Visual Reasoning VASR DEiT 1:1 Accuracy 47.2 #4 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIP

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