View publication

Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities. This paper studies the following question: Can we augment CLIP training with task-specific vision models from model zoos to improve its visual representations? Towards this end, we leverage open-source task-specific vision models to generate pseudo-labels for an uncurated and noisy image-text dataset. Subsequently, we train CLIP models on these pseudo-labels in addition to the contrastive training on image and text pairs. This simple setup shows substantial improvements of up to 16.3% across different vision tasks, including segmentation, detection, depth estimation, and surface normal estimation. Importantly, these enhancements are achieved without compromising CLIP's existing capabilities, including its proficiency in promptable zero-shot classification.

Related readings and updates.

What Can CLIP Learn From Task-specific Experts?

This paper has been accepted to the UniReps Workshop in NeurIPS 2023. Contrastive language image pretraining has become the standard approach for training vision language models. Despite the utility of CLIP visual features as global representations for images, they have limitations when it comes to tasks involving object localization, pixel-level understanding of the image, or 3D perception. Multi-task training is a popular solution to address…
See paper details

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding

This paper was accepted at the UniReps Workshop at NeurIPS 2023, and the eLVM Workshop at CVPR 2024. The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In…
See paper details