Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

Multimodal learning has rapidly evolved, with models like CLIP, UNITER, and ALBEF leading the charge. These models leverage contrastive learning and masked token reconstruction to align visual and textual semantics, achieving breakthroughs in tasks like visual question answering and image-text retrieval. However, most existing methods focus on local feature alignment, neglecting the importance of global semantic representations. As multimodal data complexity increases, capturing holistic semantics becomes critical for understanding context, reasoning, and generation. Recent trends aim to incorporate global context, but effective mechanisms remain underexplored, especially for video data where temporal dynamics add complexity.

The main challenge lies in the limited ability of current masked modeling approaches to learn comprehensive global semantics. While local token reconstruction ensures fine-grained alignment, it fails to capture the overall meaning of masked regions, leading to weak global- local cross-modal alignment. This deficiency hampers downstream tasks requiring deep semantic understanding, such as reasoning and content generation. Addressing this gap requires a method that can recover missing global semantics from the unmasked modality, thereby enhancing the overall semantic richness and alignment accuracy.

The key innovation is the introduction of Semantic Completion Learning (SCL), which explicitly focuses on recovering missing global semantics using the other modality’s complete information. This is achieved through MVSC and MLSC tasks, which leverage contrastive learning to maximize the mutual information between recovered and original features. The architecture supports both image and video inputs, with a flexible visual encoder that captures spatial and temporal cues. Unlike prior work, SCL emphasizes holistic semantic recovery, leading to richer global representations and improved cross-modal alignment, validated by state-of-the-art results across multiple benchmarks.

• �� Input: Visual data (images/videos) and textual data, tokenized into patches and words.
• �� Masking: Randomly mask parts of images and texts, creating incomplete inputs.
• �� Semantic Recovery: Use the complete modality to recover the global semantics of masked regions via MVSC and MLSC tasks.
• �� Contrastive Loss: Apply InfoNCE to maximize similarity between recovered features and original complete features, with negative samples from other data.
• �� Architecture: Employ a modified ViT for spatial-temporal visual encoding, with a dual-stream transformer for cross-modal fusion.
• �� Training: Combine contrastive, matching, and masking objectives to optimize semantic alignment and feature robustness.

Pretraining on datasets like COCO, Visual Genome, and Conceptual Captions, with high masking ratios (80% images, 40% texts). Evaluation on VQA2.0, NLVR2, Flickr30K, COCO, MSRVTT, and LSMDC benchmarks. Ablation studies remove components to verify their impact, and comparisons with SOTA models demonstrate performance gains. Hyperparameters include temperature τ in InfoNCE, batch size, and mask ratios. Fine-tuning and zero-shot evaluations confirm robustness and transferability. The experiments validate the effectiveness of SCL in improving cross-modal understanding and retrieval accuracy.

SCL achieves 78.78% accuracy on VQA2.0, surpassing previous models by over 1%. In image-text retrieval, IR@1 reaches 60.14% on COCO, outperforming models like ALBEF and TCL. On video retrieval, R@1 improves by 4.6% on MSRVTT. Ablation shows removing SCL reduces IR@1 by 3-6%, highlighting its importance. Zero-shot retrieval results demonstrate superior generalization, with notable gains across datasets. These results confirm SCL's capacity to enhance global semantic features and cross-modal alignment.

The model can be used in intelligent assistants, multimedia retrieval, and content moderation, where understanding nuanced multimodal semantics is crucial. Its ability to process both images and videos makes it suitable for real-time applications like video summarization and multimodal content creation. The robust global semantic representations also benefit downstream tasks like reasoning, captioning, and cross-modal translation, enabling more natural human-AI interactions. Long-term, such models could underpin autonomous systems with deep contextual understanding, transforming industries like entertainment, education, and security.

High computational cost due to contrastive learning and large negative sample sets limits scalability. The approach's robustness under extreme occlusion, noise, or domain shift remains to be tested extensively. Current validation is primarily on static images and short videos; real-world, complex scenarios pose additional challenges. Future work should focus on efficiency, robustness, and extending to diverse modalities and longer temporal sequences.