# Uncertainty-aware sign language video retrieval with probability distribution modeling

arXiv: https://arxiv.org/abs/2405.19689 · HTML: https://mlsanity.com/paper/2405.19689/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Word Representations via Gaussian Embedding](https://mlsanity.com/paper/1412.6623/) (2014)
- [Disentangled Representation Learning for Text-Video Retrieval](https://mlsanity.com/paper/2203.07111/) (2022)
- [TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation](https://mlsanity.com/paper/2010.05468/) (2020)
- [CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment](https://mlsanity.com/paper/2209.06430/) (2022)
- [UATVR: Uncertainty-Adaptive Text-Video Retrieval](https://mlsanity.com/paper/2301.06309/) (2023)
- [Modeling Uncertainty with Hedged Instance Embedding](https://mlsanity.com/paper/1810.00319/) (2018)
- [Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval](https://mlsanity.com/paper/2301.12644/) (2023)

## Built on by (most cited first, 7 of 7)

- [C2RL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval](https://mlsanity.com/paper/2408.09949/) (2024)
- [PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos](https://mlsanity.com/paper/2412.01800/) (2024)
- [Scaling up Multimodal Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2408.08544/) (2024)
- [MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval](https://mlsanity.com/paper/2408.10575/) (2024)
- [Textual Inversion and Self-supervised Refinement for Radiology Report Generation](https://mlsanity.com/paper/2405.20607/) (2024)
- [Semantic Hardness Is Not Visual Hardness: Sign-Aware Hard Negative Mining for Sign Language Retrieval](https://mlsanity.com/paper/2607.09263/) (2026)
- [Kinematics-Centric Continuous Sign Language Retrieval with Gloss-Guided Boundary-Aware Alignment](https://mlsanity.com/paper/2610.05306/) (2026)

## Abstract (arXiv)

Sign language video retrieval plays a key role in facilitating information access for the deaf community. Despite significant advances in video-text retrieval, the complexity and inherent uncertainty of sign language preclude the direct application of these techniques. Previous methods achieve the mapping between sign language video and text through fine-grained modal alignment. However, due to the scarcity of fine-grained annotation, the uncertainty inherent in sign language video is underestimated, limiting the further development of sign language retrieval tasks. To address this challenge, we propose a novel Uncertainty-aware Probability Distribution Retrieval (UPRet), that conceptualizes the mapping process of sign language video and text in terms of probability distributions, explores their potential interrelationships, and enables flexible mappings. Experiments on three benchmarks demonstrate the effectiveness of our method, which achieves state-of-the-art results on How2Sign (59.1%), PHOENIX-2014T (72.0%), and CSL-Daily (78.4%).