While contrastive language image pretraining (CLIP) have exhibited impressive performance by learning highly semantic and generalized representations, recent works have exposed a fundamental drawback in its syntactic properties, that includes interpreting fine-grained attributes, actions, spatial relations, states, and details that require compositional reasoning. One reason for this is that natural captions often do not capture all the visual details of a scene. This leads to unaddressed visual concepts being misattributed to the wrong words. And the pooled image and text features, ends up acting as a bag of words, hence losing the syntactic information. In this work, we ask: Is it possible to enhance CLIP's fine-grained and syntactic abilities without compromising its semantic properties? We show that this is possible by adapting CLIP efficiently on a high-quality, comprehensive, and relatively small dataset. We demonstrate our adaptation strategy on VidSitu, a video situation recognition dataset annotated with verbs and rich semantic role labels (SRL). We use the SRL and verb information to create rule-based detailed captions, making sure they capture most of the visual concepts. Combined with hard negatives and hierarchical losses, these annotations allow us to learn a powerful visual representation, dubbed Fine-Grained CLIP (FiGCLIP), that preserves semantic understanding while being detail-oriented. We evaluate on five diverse vision-language tasks in both fine-tuning and zero-shot settings, achieving consistent improvements over the base CLIP model.

通过基于 VidSitu 数据集的细节导向字幕和层级损失，我们改进了 contrastive language image pretraining (CLIP) 模型，提高了其对细粒度和句法的理解能力，并在不同任务中取得了稳定的改进。

FiGCLIP: 细粒度CLIP适应通过密集标注视频