# Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval

arXiv: https://arxiv.org/abs/2402.16769 · HTML: https://mlsanity.com/paper/2402.16769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://mlsanity.com/paper/2109.14084/) (2021)
- [End-to-End Learning of Visual Representations from Uncurated Instructional Videos](https://mlsanity.com/paper/1912.06430/) (2019)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://mlsanity.com/paper/2107.05720/) (2021)
- [A Dataset for Movie Description](https://mlsanity.com/paper/1501.02530/) (2015)
- [mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections](https://mlsanity.com/paper/2205.12005/) (2022)
- [Support-set bottlenecks for video-text representation learning](https://mlsanity.com/paper/2010.02824/) (2020)
- [mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video](https://mlsanity.com/paper/2302.00402/) (2023)
- [Align and Prompt: Video-and-Language Pre-training with Entity Prompts](https://mlsanity.com/paper/2112.09583/) (2021)
- [AVLnet: Learning Audio-Visual Language Representations from Instructional Videos](https://mlsanity.com/paper/2006.09199/) (2020)
- [CenterCLIP: Token Clustering for Efficient Text-Video Retrieval](https://mlsanity.com/paper/2205.00823/) (2022)
- [E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning](https://mlsanity.com/paper/2106.01804/) (2021)
- [Object-aware Video-language Pre-training for Retrieval](https://mlsanity.com/paper/2112.00656/) (2021)
- [Minimizing FLOPs to Learn Efficient Sparse Representations](https://mlsanity.com/paper/2004.05665/) (2020)
- [SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval](https://mlsanity.com/paper/2010.00768/) (2020)
- [Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment](https://mlsanity.com/paper/2305.12218/) (2023)
- [MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval](https://mlsanity.com/paper/2204.12408/) (2022)
- [An Efficiency Study for SPLADE Models](https://mlsanity.com/paper/2207.03834/) (2022)
- [UnifieR: A Unified Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2205.11194/) (2022)
- [LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval](https://mlsanity.com/paper/2207.04858/) (2022)

## Built on by (most cited first, 1 of 1)

- [Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval](https://mlsanity.com/paper/2506.10202/) (2025)

## Abstract (arXiv)

In video-text retrieval, most existing methods adopt the dual-encoder architecture for fast retrieval, which employs two individual encoders to extract global latent representations for videos and texts. However, they face challenges in capturing fine-grained semantic concepts. In this work, we propose the UNIFY framework, which learns lexicon representations to capture fine-grained semantics and combines the strengths of latent and lexicon representations for video-text retrieval. Specifically, we map videos and texts into a pre-defined lexicon space, where each dimension corresponds to a semantic concept. A two-stage semantics grounding approach is proposed to activate semantically relevant dimensions and suppress irrelevant dimensions. The learned lexicon representations can thus reflect fine-grained semantics of videos and texts. Furthermore, to leverage the complementarity between latent and lexicon representations, we propose a unified learning scheme to facilitate mutual learning via structure sharing and self-distillation. Experimental results show our UNIFY framework largely outperforms previous video-text retrieval methods, with 4.8% and 8.2% Recall@1 improvement on MSR-VTT and DiDeMo respectively.