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 vision and language task



clear / well-organized [ R1 ]; our approach "very interesting " [ R3 ] and novel [ R2 R3 ]; our results significant and

Neural Information Processing Systems

We thank the reviewers for the thoughtful feedback! We respond to select comments below but will address all feedback. We investigate the RefCOCO+ task. We will perform more task specific task in supplementary. VCR extends to answer justifications like "[Person3] is delivering These ablations are valuable and will be added to the paper.


Multimodal Deep Learning

Akkus, Cem, Chu, Luyang, Djakovic, Vladana, Jauch-Walser, Steffen, Koch, Philipp, Loss, Giacomo, Marquardt, Christopher, Moldovan, Marco, Sauter, Nadja, Schneider, Maximilian, Schulte, Rickmer, Urbanczyk, Karol, Goschenhofer, Jann, Heumann, Christian, Hvingelby, Rasmus, Schalk, Daniel, Aßenmacher, Matthias

arXiv.org Artificial Intelligence

FIGURE 1: LMU seal (left) style-transferred to Van Gogh's Sunflower painting (center) and blended with the prompt - Van Gogh, sunflowers - via CLIP+VGAN (right). In the last few years, there have been several breakthroughs in the methodologies used in Natural Language Processing (NLP) as well as Computer Vision (CV). Beyond these improvements on single-modality models, large-scale multimodal approaches have become a very active area of research. In this seminar, we reviewed these approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other Chapter 3.1 and Chapter 3.2), as well as models in which one modality is utilized to enhance representation learning for the other (Chapter 3.3 and Chapter 3.4). To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced (Chapter 3.5). Finally, we also cover other modalities (Chapter 4.1 and Chapter 4.2) as well as general-purpose multi-modal models (Chapter 4.3), which are able to handle different tasks on different modalities within one unified architecture.