[ACM MM 2025] BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance

The official implementation of ACM MM 2025 paper BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance

📌 Citation

If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:

@inproceedings{le2025bima,
  title={BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance},
  author={Le, Huy and Chung, Nhat and Kieu, Tung and Nguyen, Anh and Le, Ngan},
  booktitle={ACM MM},
  year={2025},
}

📕 Overview

Text-video retrieval (TVR) systems often suffer from visual-linguistic biases present in datasets, which cause pre-trained vision-language models to overlook key details. To address this, we propose BiMa, a novel framework designed to mitigate biases in both visual and textual representations. Our approach begins by generating scene elements that characterize each video by identifying relevant entities/objects and activities. For visual debiasing, we integrate these scene elements into the video embeddings, enhancing them to emphasize fine-grained and salient details. For textual debiasing, we introduce a mechanism to disentangle text features into content and bias components, enabling the model to focus on meaningful content while separately handling biased information. Extensive experiments and ablation studies across five major TVR benchmarks (i.e., MSR-VTT, MSVD, LSMDC, ActivityNet, and DiDeMo) demonstrate the competitive performance of BiMa. Additionally, the model's bias mitigation capability is consistently validated by its strong results on out-of-distribution retrieval tasks.

Setup code environment

conda create -n bima python=3.9
conda activate bima
pip install -r requirements.txt

Download CLIP Model

cd tvr/models
wget https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt
wget https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt

🎗️ Acknowledgments

• This code implementation are adopted from CLIP4clip, DRL, PCME, StyleMeUp.