AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal
  Understanding
Aligning visual features with language embeddings is a key challenge in
vision-language models (VLMs). The performance of such models hinges on having
a good connector that maps visual features generated by a vision encoder to a
shared embedding space with the LLM while preserving semantic similarity.
Existing connectors, such as multilayer perceptrons (MLPs), often produce
out-of-distribution or noisy inputs, leading to misalignment between the
modalities. In this work, we propose a novel vision-text alignment method,
AlignVLM, that maps visual features to a weighted average of LLM text
embeddings. Our approach leverages the linguistic priors encoded by the LLM to
ensure that visual features are mapped to regions of the space that the LLM can
effectively interpret. AlignVLM is particularly effective for document
understanding tasks, where scanned document images must be accurately mapped to
their textual content. Our extensive experiments show that AlignVLM achieves
state-of-the-art performance compared to prior alignment methods. We provide
further analysis demonstrating improved vision-text feature alignment and
robustness to noise.
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Ahmed Masry
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Juan A. Rodriguez
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Juan A. Rodriguez
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Tianyu Zhang
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Suyuchen Wang
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Suyuchen Wang
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Chao Wang
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Chao Wang
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Aarash Feizi
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Abhay Puri
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Xiangru Jian
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Sathwik Tejaswi Madhusudhan
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Sathwik Tejaswi Madhusudhan
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Marco Pedersoli
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Marco Pedersoli
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Bang Liu
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Nicolas Chapados
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Issam H. Laradji
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David Vazquez
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Perouz Taslakian
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Spandana Gella
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Sai Rajeswar
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal
  Understanding
Aligning visual features with language embeddings is a key challenge in
vision-language models (VLMs). The performance of such models hinges on having
a good connector that maps visual features generated by a vision encoder to a
shared embedding space with the LLM while preserving semantic similarity.
Existing connectors, such as multilayer perceptrons (MLPs), often produce
out-of-distribution or noisy inputs, leading to misalignment between the
modalities. In this work, we propose a novel vision-text alignment method,
AlignVLM, that maps visual features to a weighted average of LLM text
embeddings. Our approach leverages the linguistic priors encoded by the LLM to
ensure that visual features are mapped to regions of the space that the LLM can
effectively interpret. AlignVLM is particularly effective for document
understanding tasks, where scanned document images must be accurately mapped to
their textual content. Our extensive experiments show that AlignVLM achieves
state-of-the-art performance compared to prior alignment methods. We provide
further analysis demonstrating improved vision-text feature alignment and
robustness to noise.
A new vision-text alignment method, AlignVLM, effectively maps visual features to LLM embeddings, improving performance in document understanding and robustness to noise.
vision-language models VLMs multilayer perceptrons MLPs AlignVLM LLM linguistic priors document understanding feature alignment