Exploiting Unlabeled Videos for Video-Text Retrieval via Pseudo-Supervised Learning

Large-scale pre-trained vision-language models (e.g., CLIP) have shown incredible generalization performance in downstream tasks such as video-text retrieval (VTR). Traditional approaches have leveraged CLIP's robust multi-modal alignment ability for VTR by directly fine-tuning vision and text encoders with clean video-text data. Yet, these techniques rely on carefully annotated video-text pairs, a process that is costly and labor-intensive. In this context, we introduce a new approach, Pseudo-Supervised Selective Contrastive Learning (PS-SCL). PS-SCL minimizes the dependency on manually-labeled text annotations by generating pseudo-supervisions from unlabeled video data for training. We first exploit CLIP's visual recognition capabilities to generate pseudo-texts automatically. These pseudo-texts contain diverse visual concepts from the video and serve as weak textual guidance. Moreover, we introduce Selective Contrastive Learning (SeLeCT), which prioritizes and selects highly correlated video-text pairs from pseudo-supervised video-text pairs. By doing so, SeLeCT enables more effective multi-modal learning under weak pairing supervision. Experimental results demonstrate that our method outperforms CLIP zero-shot performance by a large margin on multiple video-text retrieval benchmarks, e.g., 8.2% R@1 for video-to-text on MSRVTT, 12.2% R@1 for video-to-text on DiDeMo, and 10.9% R@1 for video-to-text on ActivityNet, respectively.