# A CLIP-Enhanced Method for Video-Language Understanding

arXiv: https://arxiv.org/abs/2110.07137 · HTML: https://mlsanity.com/paper/2110.07137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 13 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://mlsanity.com/paper/2005.00200/) (2020)
- [ActBERT: Learning Global-Local Video-Text Representations](https://mlsanity.com/paper/2011.07231/) (2020)
- [UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation](https://mlsanity.com/paper/2002.06353/) (2020)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation](https://mlsanity.com/paper/2106.04632/) (2021)
- [A Straightforward Framework For Video Retrieval Using CLIP](https://mlsanity.com/paper/2102.12443/) (2021)

## Built on by (most cited first, 6 of 6)

- [Phone2Proc: Bringing Robust Robots Into Our Chaotic World](https://mlsanity.com/paper/2212.04819/) (2022)
- [i-Code: An Integrative and Composable Multimodal Learning Framework](https://mlsanity.com/paper/2205.01818/) (2022)
- [Vote'n'Rank: Revision of Benchmarking with Social Choice Theory](https://mlsanity.com/paper/2210.05769/) (2022)
- [Multimodal Subtask Graph Generation from Instructional Videos](https://mlsanity.com/paper/2302.08672/) (2023)
- [Multi-Query Video Retrieval](https://mlsanity.com/paper/2201.03639/) (2022)
- [CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT under Uneven Illumination](https://mlsanity.com/paper/2502.19450/) (2025)

## Abstract (arXiv)

This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark.github.io/challenge2021.html). We propose a CLIP-Enhanced method to incorporate the image-text pretrained knowledge into downstream video-text tasks. Combined with several other improved designs, our method outperforms the state-of-the-art by 2.4% (57.58 to 60.00) Meta-Ave score on VALUE benchmark.