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In the realm of vision-language understanding, the proficiency of models in interpreting and reasoning over visual content has become a cornerstone for numerous applications.
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Chefer, H., Gur, S., Wolf, L.: Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 397–406 (2021)
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Wang, Y., Chen, Z., Jiang, H., Song, S., Han, Y., Huang, G.: Adaptive focus for efficient video recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 16249–16258 (2021)
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Zhu, M., Han, K., Wu, E., Zhang, Q., Nie, Y., Lan, Z., Wang, Y.: Dynamic resolution network. Advances in Neural Information Processing Systems pp. 34, 27319–27330 (2021)
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Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning. pp. 12888–12900. PMLR (2022)
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