ChanganYao/DeepImbalancedRegressionForMLLMs

MLLM Deep Imbalanced Regression Benchmarks

The official dataset release for CCC-GRPO: Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression, accepted at ICML 2026.

Yao Du, Shanshan Song, Xiaomeng Li

[Paper] [Code]

Overview

This repository contains four multimodal deep imbalanced regression benchmarks used to evaluate CCC-GRPO. The training splits follow naturally long-tailed target distributions, while the test splits are balanced. No validation splits are used.

The benchmark covers 129,563 image-text regression samples across facial age estimation, movie rating prediction, and skeletal age estimation.

Benchmark

Dataset	Train	Test	Total	Target	Domain
AgeDB-DIR	12,208	2,140	14,348	Age (years)	In-the-wild faces
IMDB-WIKI-DIR	81,911	11,016	92,927	Age (years)	Web-scale faces
IMDB-Movie-DIR	7,049	1,203	8,252	IMDb movie score	Movie posters
BoneAge-DIR	12,528	1,508	14,036	Bone maturity (months)	Medical imaging
Total	113,696	15,867	129,563	-	-

Repository Structure

Images and sample metadata are packaged as WebDataset tar shards:

agedb/{train,test}/*.tar
imdb_wiki/{train,test}/*.tar
imdb_movie/{train,test}/*.tar
boneage/{train,test}/*.tar

Each WebDataset sample contains an image and a JSON record with:

Field	Description
dataset	Benchmark name
split	train or test
image	Image filename inside the shard
problem	Regression prompt
solution	Numerical target

The original training and test annotations are also provided:

Dataset	Train annotation	Test annotation
AgeDB-DIR	agedb/agedb_train.jsonl	agedb/test_conversation_from_agedb.json
IMDB-WIKI-DIR	imdb_wiki/imdb_train_peak_compressed_3500_leq100.jsonl	imdb_wiki/test_conversation_from_imdb_leq100.json
IMDB-Movie-DIR	imdb_movie/train.jsonl	imdb_movie/test.json
BoneAge-DIR	boneage/boneage_train.jsonl	boneage/test_conversation_from_boneage.json

Loading

Download the complete repository:

hf download ChanganYao/DeepImbalancedRegressionForMLLMs \
  --repo-type dataset \
  --local-dir data

Load one benchmark directly with datasets:

from datasets import load_dataset

dataset = load_dataset(
    "webdataset",
    data_files={
        "train": "hf://datasets/ChanganYao/DeepImbalancedRegressionForMLLMs/agedb/train/*.tar",
        "test": "hf://datasets/ChanganYao/DeepImbalancedRegressionForMLLMs/agedb/test/*.tar",
    },
)

Replace agedb with imdb_wiki, imdb_movie, or boneage to load another benchmark.

CCC-GRPO

CCC-GRPO introduces batch-level Concordance Correlation Coefficient supervision for multimodal deep imbalanced regression. Instead of assigning rewards independently to each prediction, it evaluates numerical responses in the context of the mini-batch to align the prediction and target distributions.

Training and evaluation code is available at xmed-lab/CCC-GRPO.

Citation

@misc{du2026injectingdistributionalawarenessmllms,
  title={Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression},
  author={Yao Du and Shanshan Song and Xiaomeng Li},
  year={2026},
  eprint={2605.01402},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}