# Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning

arXiv: https://arxiv.org/abs/1804.05448 · HTML: https://mlsanity.com/paper/1804.05448/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2018.

## Built on by (most cited first, 21 of 21)

- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [End-to-end Generative Pretraining for Multimodal Video Captioning](https://mlsanity.com/paper/2201.08264/) (2022)
- [Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation](https://mlsanity.com/paper/2104.02026/) (2021)
- [UnIVAL: Unified Model for Image, Video, Audio and Language Tasks](https://mlsanity.com/paper/2307.16184/) (2023)
- [Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation](https://mlsanity.com/paper/2206.07771/) (2022)
- [Quantized GAN for Complex Music Generation from Dance Videos](https://mlsanity.com/paper/2204.00604/) (2022)
- [Audio-Visual Class-Incremental Learning](https://mlsanity.com/paper/2308.11073/) (2023)
- [Space-Time Memory Network for Sounding Object Localization in Videos](https://mlsanity.com/paper/2111.05526/) (2021)
- [The MSR-Video to Text Dataset with Clean Annotations](https://mlsanity.com/paper/2102.06448/) (2021)
- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior](https://mlsanity.com/paper/2310.02522/) (2023)
- [Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition](https://mlsanity.com/paper/2401.06287/) (2024)
- [Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight](https://mlsanity.com/paper/2212.02053/) (2022)
- [Implicit and Explicit Commonsense for Multi-sentence Video Captioning](https://mlsanity.com/paper/2303.07545/) (2023)
- [Repetitive Activity Counting by Sight and Sound](https://mlsanity.com/paper/2103.13096/) (2021)
- [Feature-Supervised Action Modality Transfer](https://mlsanity.com/paper/2108.03329/) (2021)
- [Contrastive Learning of Visual-Semantic Embeddings](https://mlsanity.com/paper/2110.08872/) (2021)
- [Collaborative Three-Stream Transformers for Video Captioning](https://mlsanity.com/paper/2309.09611/) (2023)
- [Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusion](https://mlsanity.com/paper/2310.16267/) (2023)
- [Attention Based Encoder Decoder Model for Video Captioning in Nepali (2023)](https://mlsanity.com/paper/2312.07418/) (2023)
- [AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward](https://mlsanity.com/paper/2608.06930/) (2026)

## Abstract (arXiv)

A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.