Paper ID: 2401.07669

FiGCLIP: Fine-Grained CLIP Adaptation via Densely Annotated Videos

Darshan Singh S, Zeeshan Khan, Makarand Tapaswi

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.

Submitted: Jan 15, 2024