The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
py unknown | __key__ string | __url__ string |
|---|---|---|
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EgoShift
Bounding the Action-Only Shortcut in Egocentric Spatial Updating for Vision–Language Models · WACV 2027, Evaluation & Datasets track
Faisal Ahmed, John Quarles, Kevin Desai — Department of Computer Science, University of Texas at San Antonio
Paper · Code · Project page
Three egocentric spatial-reasoning tasks over simulated indoor scenes, with the evidence behind every number in the paper.
| task | question | held-out items |
|---|---|---|
| A | Same object seen twice, or two different objects? | 5,838 |
| B | After a stated in-place turn, where is the object? | 1,098 |
| C | In the second view: visible, out of view, or occluded? | 5,850 |
When a question states a camera turn, that turn alone rules out some answers, so
a predictor given only the stated action already beats chance. EgoShift computes
the highest score that shortcut can reach before any model is run, from the
camera geometry, and builds every turn to sit on that ceiling. For Task B's
held-out set the ceiling is 504/1,098 = 45.90%, and the build matches it in
every one of the ten action cells.
Start here
egoshift_reviewer_bundle.tar.gz 169 MB, everything below
croissant.json Croissant + Responsible AI metadata
CHECKSUMS.json SHA-256 for every file here
tar xzf egoshift_reviewer_bundle.tar.gz && cd egoshift_reviewer_bundle
Layout
code/ construction, scoring and audit tools, plus tests
task_{a,b,c}/ questions for all three splits, dev-split answers,
manifests, and a seeded 120-item frame sample
model_panel/ 276,403 raw model completions across the 36 reported models
excluded_runs/ runs that are not part of the reported panel, with reasons
audits/ 124 build and audit reports
structure/ held-out cell structure with all bearings removed
human/ task-level human aggregates only
licenses/ LICENSE and NOTICE for the shipped bytes
code/inside the bundle is a frozen snapshot taken at submission. For a maintained, tested copy — the one CI runs against — use github.com/UTSA-VIRLab/EgoShift.
What you can check without the answer key
The structural target. structure/task_b_action_cell_counts.json holds the
per-cell counts and the derived ceiling for all 1,098 Task-B items.
Answer distributions and the failure mode. The raw completions contain each
model's parsed answer per item. structure/task_b_holdout_structure.jsonl gives
the rotation and turn for each item, so the per-cell answer behaviour reported in
the paper can be recomputed directly.
Scene disjointness. The same structure files carry scene_id and
physical_scene_component_id for held-out items, so the claim that no scene
component is shared across splits is checkable.
The full pipeline on dev. Development-split answers ship, so the scorer and audits can be run end to end and compared against the shipped audit reports. Against the maintained repository:
git clone https://github.com/UTSA-VIRLab/EgoShift.git
cd EgoShift && pip install -e ".[test,full]" && make check
Runs that are not in the panel
excluded_runs/ holds attempts that the paper does not count, each with a stated
reason in EXCLUSIONS.json. All three are documented access or runtime
failures whose files contain probe and error stubs rather than answers. They ship
so that an unavailable model is visible as a recorded failure rather than a
silent gap in the panel.
Licensing
Annotations, questions, metadata and audit outputs are CC-BY-4.0. Rendered frames
derive from AI2-THOR and ProcTHOR and are redistributed under the upstream
Apache-2.0 terms with attribution and the NOTICE file preserved. licenses/ in
the reviewer bundle carries both. Held-out answers are withheld entirely.
What is deliberately withheld
Held-out answers for all three tasks. All bearing geometry for held-out items: the post-turn bearing determines the answer outright, and the source bearing plus the stated rotation recovers it, so neither travels. Human per-rater and per-item responses, the intake files, and the pseudonym map.
Large archives
The full rendered frame sets are separate and are not needed to reproduce the
paper's tables: egoshift_task_a.tar.gz (5.9 GB), egoshift_task_b.tar.gz
(573 MB), egoshift_task_c.tar.gz (3.9 GB), egoshift_surveys.tar.gz (299 MB,
the blank annotator survey instruments).
Reporting results
Report accuracy per stated turn, not pooled. That is the failure this benchmark documents: pooled Task-B accuracy puts the best configuration at 83.6% against a trained-human 84.6%, which reads as nearly solved, while the same system trails the panel badly at 90° and 180°.
Citation
@inproceedings{ahmed2027egoshift,
title = {EgoShift: Bounding the Action-Only Shortcut in Egocentric
Spatial Updating for Vision--Language Models},
author = {Ahmed, Faisal and Quarles, John and Desai, Kevin},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2027}
}
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