Calibrated Multimodal Representation Learning with Missing Modalities
Paper • 2511.12034 • Published
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Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need 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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Calibrated Multimodal Representation Learning with Missing Modalities
Multimodal representation learning under partial-modality settings
✨ Overview
CalMRL is a multimodal representation learning framework designed for alignment calibration when some modalities are missing. CalMRL combines two complementary goals:
- Cross-modal alignment for robust shared representations
- Missing-modality calibration through posterior inference and learned generative parameters
🎯 Key Features
🔄 Partial-Modality Learning
- Handles missing video, audio, text, or subtitle signals
- Supports posterior-based feature completion with learned modality-specific parameters
🎯 Multimodal Retrieval
- Joint training over text-video, text-audio, text-video-audio, and subtitle-aware setups
- Config-driven recipes for pretraining, finetuning, and evaluation
🧠 Feature Calibration
- Uses latent posterior inference for modality completion
- Includes a warmup pipeline to estimate
W,mu, andlog_sigma
🏗️ Architecture
The current codebase is organized around three main stages:
- 🔧 Multimodal Encoding: Video, audio, text, and subtitle features are extracted with VAST-style encoders.
- 🧮 Representation Calibration: Shared embeddings are aligned while latent posterior inference estimates missing information.
- 🔄 Downstream Evaluation: Retrieval and other tasks are executed through a unified config-driven pipeline.
Citation
If this project is useful for your research, you can cite the work as:
@article{liu2025calibrated,
title={Calibrated Multimodal Representation Learning with Missing Modalities},
author={Liu, Xiaohao and Xia, Xiaobo and Wei, Jiaheng and Yang, Shuo and Su, Xiu and Ng, See-Kiong and Chua, Tat-Seng},
journal={arXiv preprint arXiv:2511.12034},
year={2025}
}
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