The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label DarkAct@a4883ae0e104080bcb5adbb6441e1dfad6899c04
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label DarkAct@a4883ae0e104080bcb5adbb6441e1dfad6899c04Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Check out the documentation for more information.
DarkAct: A RGB-Thermal Dataset and Fusion Framework for Multimodal Low-Light Action Recognition
Official implementation of DarkAct (CVPR 2026), a large-scale RGB-Thermal video dataset and a dedicated fusion framework for robust human action recognition under low-light conditions.
Recently, we have expand our datasets to VQA which named as DarkAct++.
📌 Project Overview
Human action recognition (HAR) in low-light environments is severely challenged by degraded visibility, illumination variance, and loss of appearance cues. We address this gap by:
- Constructing DarkAct: The first large-scale high-quality RGB-Thermal video dataset for low-light HAR, with diverse illumination, viewpoints, and scenes.
- Benchmarking State-of-the-Art Models: Systematically evaluate unimodal HAR, multimodal fusion, and vision-language foundation models (VLMs) on DarkAct, revealing their limitations in low-light cross-spectral scenarios.
- Proposing DarkAct-Net: A novel RGB-Thermal fusion framework with Motion-Aware Attention (MAA) and Light-Adaptive Fusion (LAF) modules, achieving SOTA performance for low-light action recognition.
📊 DarkAct Dataset
Core Features
DarkAct is designed with three key dimensions of diversity for realistic low-light HAR scenarios:
- Multi-Illumination: Varying visibility from weakly visible to almost invisible.
- Multi-View: Upward, eye-level, and downward viewing angles.
- Multi-Scene: Diverse indoor/outdoor environments (terraces, corridors, squares, bedrooms, etc.) in urban/rural central China.
Data Statistics
| Item | Details |
|---|---|
| Total Paired RGB-T Videos | 12,778 |
| Action Categories | 27 (daily human actions, e.g., walking, drinking, typing, squatting) |
| Training/Test Split | 9,040 / 3,738 |
| Frame Rate | 30 fps |
| Total Duration | ~19.2 hours |
| Video Duration | 4.5 ~ 19.1s (avg. 5.4s) |
| Volunteers | 11 adults (7 male, 4 female) |
| Capture Device | Dahua DH-TPC-BF2241 binocular camera (RGB + Thermal-Infrared) |
Action Categories
Walking, calling, running, pouring, using smartphone, clapping, photographing, picking up object, jumping, pushing, taking off coat, putting on coat, drinking, searching, sitting, closing door, squatting, standing, turning, typing, climbing stairs, waving, holding umbrella, opening door, sleeping, lifting, crouching.
license: mit task_categories: - video-classification
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