STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen and Bowen Zhang and Liangliang Cao and Jiguang Shen and Tom Gunter and Albin Madappally Jose and Alexander Toshev and Jonathon Shlens and Ruoming Pang and Yinfei Yang
arXiv e-Print archive - 2023 via Local arXiv
Keywords: cs.CV
First published: 2024/04/16 (just now)
Abstract: Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, represent images and texts as dense embeddings and calculate the similarity in the dense embedding space as the matching score. On the other hand, sparse semantic features like bag-of-words models are more interpretable, but believed to suffer from inferior accuracy than dense representations. In this work, we show that it is possible to build a sparse semantic representation that is as powerful as, or even better than, dense presentations. We extend the CLIP model and build a sparse text and image representation (STAIR), where the image and text are mapped to a sparse token space. Each token in the space is a (sub-)word in the vocabulary, which is not only interpretable but also easy to integrate with existing information retrieval systems. STAIR model significantly outperforms a CLIP model with +$4.9\%$ and +$4.3\%$ absolute Recall@1 improvement on COCO-5k text$\rightarrow$image and image$\rightarrow$text retrieval respectively. It also achieved better performance on both of ImageNet zero-shot and linear probing compared to CLIP.
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Chen Chen and Bowen Zhang and Liangliang Cao and Jiguang Shen and Tom Gunter and Albin Madappally Jose and Alexander Toshev and Jonathon Shlens and Ruoming Pang and Yinfei Yang
arXiv e-Print archive - 2023 via Local arXiv
Keywords: cs.CV
First published: 2024/04/16 (just now)
Abstract: Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, represent images and texts as dense embeddings and calculate the similarity in the dense embedding space as the matching score. On the other hand, sparse semantic features like bag-of-words models are more interpretable, but believed to suffer from inferior accuracy than dense representations. In this work, we show that it is possible to build a sparse semantic representation that is as powerful as, or even better than, dense presentations. We extend the CLIP model and build a sparse text and image representation (STAIR), where the image and text are mapped to a sparse token space. Each token in the space is a (sub-)word in the vocabulary, which is not only interpretable but also easy to integrate with existing information retrieval systems. STAIR model significantly outperforms a CLIP model with +$4.9\%$ and +$4.3\%$ absolute Recall@1 improvement on COCO-5k text$\rightarrow$image and image$\rightarrow$text retrieval respectively. It also achieved better performance on both of ImageNet zero-shot and linear probing compared to CLIP.
[link]
This paper aims to learn a sparse semantic representation of texts and images instead of a dense representation trained by CLIP or ALIGN.
The sparse embeddings are achieved by:
(1) For an input (image or text), extract it to a feature (using Transformer) $h$ where $h_{j}$ corresponds to the $jth$ word in the input.
(2) Each $j$ word embedding will be transformed to $p(h_{j})$ in vocabulary space $V$ by using a mapping function (in this paper, this is BERT Masked Language Model MLM). So each $p(h_{j})$ is a token in a vocabulary space $V$.
(3) A max pooling layer will be applied to $p(h_{j})$ to get a value denoted for that token. So in the end, we will have a sparse vector living in V-dimensional space.
https://i.imgur.com/BTvndLR.png
Training:
To achieve two goals (1) aligning text and images in the sparse embedding and (2) grounding the sparse vector with the human-understandable word in the vocabulary, they proposed 3-stage training:
Stage 1: Training image embedding with masked tokens. In the first stage, they co-trained both the image and text encoders and apply a binary mask on the text embedding. By matching with the masked text embedding, the image encoder is learned to ground its image embedding on the tokens from the pairing text. Therefore, after the stage 1 training, the image embedding is living in the vocabulary’s interpretable space.
Stage 2: Training with frozen image encoder. In this stage, they focus on grounding the text embedding to the same interpretable space where the image embedding is trained to reside in from stage 1. The key idea is to let the image encoder teach the text encoder as a teacher model. After stage 2 training, both image and text embeddings are in the same human-interpretable embedding space constructed by the vocabulary.
Stage 3: Fine-tuning both encoders, they boosted the image-text matching performance by finetuning both encoders jointly.
https://i.imgur.com/PWrEbkk.png
To further encourage the sparsity, they proposed to use FLOPs regularization loss such that only a small number of token embeddings in V are non-zeros.
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