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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 …
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.
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
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 …
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 …
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. …
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. …
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 …
- 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.
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