Papers with Cross-modal Transformer
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training (2020.emnlp-main)
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| Challenge: | HERO is a framework for large-scale video+language omni-representation learning. |
| Approach: | They propose a framework for large-scale video+language omni-representation learning that encodes multimodal inputs in a hierarchical structure and uses Masked Language Modeling and Masked Frame Modeling to train models. |
| Outcome: | The proposed framework achieves state-of-the-art on multiple benchmarks over text-based video/video-moment retrieval, video question answering (QA), Video-and-language Inference and video Captioning tasks across different domains. |
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations (2021.emnlp-main)
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| Challenge: | Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. |
| Approach: | They propose a cross-modal transformer for audio-and-language that learns inter-modal connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs. |
| Outcome: | The proposed model improves on multiple audio-and-language tasks and can be used in fine-tuning phase. |
Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention (2026.findings-acl)
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| Challenge: | Existing methods for multimodal summarization often inject shallow visual features into deep models, leading to representational mismatches and weak cross-modal grounding. |
| Approach: | They propose a framework that performs text summarization and representative image selection . a deep visual processor aligns the visual encoder with the language model at corresponding depths . |
| Outcome: | The proposed framework produces more accurate, visually grounded summaries and selects more representative images. |