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This paper introduces CLIP-Knowledge Dis-tillation (KD), which aims to enhance a small student CLIP model supervised by a pre-trained large teacher CLIP model. The state-of-the-art TinyCLIP [48] also investigates CLIP distillation.
In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a new approach facilitating full test-time adaptation (TTA) of this VLM. Our method employs a diverse set of templates for text prompts, augmenting the existing framework of CLIP.
We propose Chinese CLIP, a simple imple-mentation of CLIP pretrained on our collected large-scale Chinese image-text pair data, and we propose a two-stage pretraining method to achieve high pretraining eficiency and im-proved downstream performance.
Since over-fitting is not a major concern, the details of train-ing CLIP are simplified compared to Zhang et al. (2020). We train CLIP from scratch instead of initializing with pre-trained weights. We remove the non-linear projection be-tween the representation …
With several diagnostic tools, we find that compared to CLIP, both MIM and FD-CLIP possess several prop-erties that are intuitively good, which may provide in-sights on their superior fine-tuning performance. We generalize our method to various pre-training mod-els and observe consistent gains.
CLIP [31] consists of two core encoders: a text encoder T and a visual encoder I, which are jointly trained by massive noisy image-text pairs with contrastive loss.
While the conventional fine-tuning paradigm fails to benefit from CLIP, we find the image encoder of CLIP al-ready possesses the ability to directly work as a segmentation model.
- The study highlights the sensitivity of initialization to CLIP, as even when the two modalities are initialized in close proximity, the CLIP loss still induces a modality gap.
parameters from the CLIP (ViT-B/32). Con-cretely, for the position embedding in sequential type and tight type, we initialize them by repeating the position embedding from CLIP’s text encoder. Similarly, the transformer encoder is initialized by the corresponding layers’ weight of he pretrained CLIP’s imag
mework of our PCL-CLIP model for supervised Re-ID. Different from CLIP-ReID that consists of a prompt learning stage and a fine-tuning stage, our approach directly fine-tune CLIP with a s
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