# Video + CLIP Baseline for Ego4D Long-term Action Anticipation

arXiv: https://arxiv.org/abs/2207.00579 · HTML: https://mlsanity.com/paper/2207.00579/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [A CLIP-Hitchhiker's Guide to Long Video Retrieval](https://mlsanity.com/paper/2205.08508/) (2022)

## Built on by (most cited first, 18 of 18)

- [VideoLLM-online: Online Video Large Language Model for Streaming Video](https://mlsanity.com/paper/2406.11816/) (2024)
- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [HierVL: Learning Hierarchical Video-Language Embeddings](https://mlsanity.com/paper/2301.02311/) (2023)
- [VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation](https://mlsanity.com/paper/2408.16730/) (2024)
- [Intention-Conditioned Long-Term Human Egocentric Action Forecasting](https://mlsanity.com/paper/2207.12080/) (2022)
- [Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models](https://mlsanity.com/paper/2405.20305/) (2024)
- [PALM: Predicting Actions through Language Models](https://mlsanity.com/paper/2311.17944/) (2023)
- [Object-centric Video Representation for Long-term Action Anticipation](https://mlsanity.com/paper/2311.00180/) (2023)
- [Egocentric Video Task Translation](https://mlsanity.com/paper/2212.06301/) (2022)
- [Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023](https://mlsanity.com/paper/2306.16545/) (2023)
- [Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation](https://mlsanity.com/paper/2508.01742/) (2025)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Technical Report for Ego4D Long Term Action Anticipation Challenge 2023](https://mlsanity.com/paper/2307.01467/) (2023)
- [Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation](https://mlsanity.com/paper/2407.11954/) (2024)
- [MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation](https://mlsanity.com/paper/2501.08837/) (2025)
- [Egocentric Video Task Translation @ Ego4D Challenge 2022](https://mlsanity.com/paper/2302.01891/) (2023)
- [Bidirectional Action Sequence Learning for Long-term Action Anticipation with Large Language Models](https://mlsanity.com/paper/2508.00374/) (2025)
- [MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation](https://mlsanity.com/paper/2509.11394/) (2025)

## Abstract (arXiv)

In this report, we introduce our adaptation of image-text models for long-term action anticipation. Our Video + CLIP framework makes use of a large-scale pre-trained paired image-text model: CLIP and a video encoder Slowfast network. The CLIP embedding provides fine-grained understanding of objects relevant for an action whereas the slowfast network is responsible for modeling temporal information within a video clip of few frames. We show that the features obtained from both encoders are complementary to each other, thus outperforming the baseline on Ego4D for the task of long-term action anticipation. Our code is available at github.com/srijandas07/clip_baseline_LTA_Ego4d.