What Is the Synergy Between Creation & Comprehension? What You Need to Know | HackerNoon

Table of Links

Abstract and 1 Introduction

2 Background & Problem Statement

2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides?

3 DreamLLM

3.1 End-to-End Interleaved generative Pretraining (I-GPT)

3.2 Model Training

4 Experiments and 4.1 Multimodal Comprehension

4.2 Text-Conditional Image Synthesis

4.3 Multimodal Joint Creation & Comprehension

5 Discussions

5.1 Synergy between creation & Comprehension?

5. 2 What is learned by DreamLLM?

6 Related Works

7 Conclusions and References

A Additional Experiments

B Additional Qualitative Examples

C Implementation Details

D Additional Related Works

E Limitations, Failure Cases & Future Works

5 DISCUSSIONS

5.1 SYNERGY BETWEEN CREATION & COMPREHENSION?

To elucidate the synergy between multimodal creation and comprehension, we make the comparison among three methods with DREAMLLM architecture, each utilizing identical training data yet differing in their learning objectives: a) the Creation-only baseline, focused solely on text/document-conditional image synthesis; b) the Comprehension-only baseline, dedicated to word generation exclusively; c) the Joint-learning method, which is the default setting of DREAMLLM learning both image and language modeling.

Qualitative Analysis In Fig. 4, we compare answers to some examplar VQA tasks from comprehension-only and joint learning modules, respectively. It can be seen that: i) The joint-learning method exhibits superior multimodal comprehension, particularly in identifying subject relationships and attributes like object size. ii) In multimodal comprehension scenarios involving multiple image inputs, the joint-learning approach demonstrates enhanced precision. This improved performance is a natural outcome of I-GPT pretraining, allowing better modeling of multimodal correlations in various interleaved documents

Multimodal In-Context Generation Multimodal in-context generation is a critical emerging capability for MLLMs (Bommasani et al., 2021; Alayrac et al., 2022). While significant strides have been made in in-context visual question answering, in-context image synthesis remains relatively lacking in exploration. The multimodal context-conditional image synthesis capabilities of DREAMLLM, as demonstrated in Fig. 5, offer promising insights into this domain. Tasks such as in-context image edition, subject-driven image generation, and compositional generation, however, pose significant

challenges in a zero-shot setting, particularly without downstream fine-tuning as in DreamBooth (Ruiz et al., 2023) or attention modification techniques as in Prompt2Prompt (Hertz et al., 2023). Despite these hurdles, Fig. 5 illustrates DREAMLLM’s ability to generate images conditioned on the provided image context. This capability suggests promising potential for DREAMLLM in maintaining subject, identity, and semantic context, thereby paving a new way for resolving these complex tasks.