Abstract
Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. Models such as ViLBERT, LXMERT and UNITER have significantly lifted state of the art across a wide range of V+L benchmarks. However, little is known about the inner mechanisms that destine their impressive success. To reveal the secrets behind the scene, we present Value (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e.g., Visual Coreference Resolution, Visual Relation Detection) generalizable to standard pre-trained V+L models, to decipher the inner workings of multimodal pre-training (e.g., implicit knowledge garnered in individual attention heads, inherent cross-modal alignment learned through contextualized multimodal embeddings). Through extensive analysis of each archetypal model architecture via these probing tasks, our key observations are: (i) Pre-trained models exhibit a propensity for attending over text rather than images during inference. (ii) There exists a subset of attention heads that are tailored for capturing cross-modal interactions. (iii) Learned attention matrix in pre-trained models demonstrates patterns coherent with the latent alignment between image regions and textual words. (iv) Plotted attention patterns reveal visually-interpretable relations among image regions. (v) Pure linguistic knowledge is also effectively encoded in the attention heads. These are valuable insights serving to guide future work towards designing better model architecture and objectives for multimodal pre-training. (Code is available at https://github.com/JizeCao/VALUE).
J. Cao and L. Yu—This work was done when Jize and Licheng worked at Microsoft.
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Notes
- 1.
Our probing analysis can be readily extended to other pre-trained models as well.
- 2.
An image region is also called a visual token in this paper; these two terms will be used interchangeable throughout the paper.
- 3.
Head (i-j) means the j-th head at the i-th layer.
- 4.
Since noun phrase may contain several tokens, we use the maximum attention weight among the tokens in that phrase over an image region as the attention weight between the noun phase and the image region. The embedding of the noun phrase is the mean of all the representations of its textual tokens.
- 5.
Though both models’ embedding probers achieve higher than 94% accuracy on the VCC task, it is worth noting that text embedding input can potentially leak the link information. For instance, the phrase “A guard with a white hat” may already provide coreference information between person and the corresponding image region.
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Cao, J., Gan, Z., Cheng, Y., Yu, L., Chen, YC., Liu, J. (2020). Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_34
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