VL-BERT: Pre-training of Generic Visual-Linguistic Representations (1908.08530v4)

Abstract: We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark. Code is released at \url{https://github.com/jackroos/VL-BERT}.

Overview of VL-BERT: Pre-training of Generic Visual-Linguistic Representations

The paper entitled "VL-BERT: Pre-training of Generic Visual-Linguistic Representations" introduces VL-BERT, a pre-trained model designed to produce joint visual-linguistic representations. Utilizing the versatile Transformer architecture, VL-BERT integrates visual and linguistic input features to excel across a range of visual-linguistic tasks such as visual question answering (VQA), visual commonsense reasoning (VCR), and referring expression comprehension. This paper underscores the significance of leveraging large-scale pretraining on a combination of visual-linguistic and textual datasets to foster better alignment between visual and linguistic modalities, thereby enhancing performance on downstream tasks.

Core Concepts and Methodology

VL-BERT is built upon the robust foundation of the Transformer model, known for its exemplary performance in NLP tasks. It extends the traditional input to accommodate both word and visual region-of-interest (RoI) features. Key attributes include:

• Multi-modal inputs: Each input element to VL-BERT is either a word from a sentence or an RoI from an image, the latter of which is embedded using features extracted from a Fast R-CNN.
• Combining features: To integrate the visual RoI features effectively, the embeddings consist of token embeddings, visual feature embeddings, segment embeddings, and sequence position embeddings. This amalgamation ensures that both visual and linguistic information is represented comprehensively.
• Pre-training strategy: VL-BERT's pre-training employs the Conceptual Captions dataset, coupled with BooksCorpus and English Wikipedia for textual inputs. This dual-corpus strategy diversifies the training scenarios and adds robustness to the learned representations.

The pre-training objectives are designed to align visual and linguistic clues more effectively:

1. Masked LLMing with Visual Clues (MLM-VC): Words in sentences are randomly masked, and VL-BERT predicts these masked words based on the contextual cues available from the remaining words and the visual RoIs.
2. Masked RoI Classification with Linguistic Clues (MRCL): Similar to MLM-VC, but in this case, RoIs are masked and classified using surrounding linguistic context.

Empirical Results and Implications

The paper presents extensive empirical evidence showing that VL-BERT outperforms existing models on several benchmark tasks:

• Visual Commonsense Reasoning: VL-BERT significantly outstrips previous methods like R2C and concurrent models such as ViLBERT, achieving state-of-the-art results on both the sub-tasks (Q -> A and QA -> R) and the holistic task (Q -> AR).
• Visual Question Answering: The model outperforms BUTD and achieves results comparable to advanced models like LXMERT, particularly benefiting from pretraining on both visual-linguistic and textual corpora.
• Referring Expression Comprehension: VL-BERT shows marked improvements over models like MAttNet and performs on par with ViLBERT, demonstrating its effectiveness in tasks requiring fine-grained visual grounding.

Theoretical and Practical Implications

VL-BERT's approach underscores the critical role of pre-training in aligning and integrating multi-modal information. By adapting the Transformer model to process both visual and linguistic inputs jointly, VL-BERT learns sophisticated representations that bolster performance across a multitude of visual-linguistic tasks. This model presents a significant step toward more unified multi-modal AI systems.

The implications of this research extend to several domains:

• Machine Learning Research: The success of VL-BERT highlights the potential of multi-modal pre-trained models, encouraging further exploration into integrated visual-linguistic representations and their applications.
• Applied AI: In practical applications like automated assistants, educational tools, and accessibility technologies, VL-BERT's improvements in complex visual comprehension tasks can enhance user interactions and improve system responses.
• Future Developments in AI: The paper opens avenues for exploring additional pre-training tasks, integrating more diverse datasets, and refining architectures to further enhance cross-modal learning.

Conclusion

VL-BERT exemplifies a profound advance in the pre-training of visual-linguistic representations, leveraging the strengths of the Transformer model and extensive pre-training to achieve superior performance on complex tasks. The paper provides compelling empirical results and sets the groundwork for future research in developing even more effective multi-modal AI systems.