A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
SegNet introduces a deep encoder-decoder architecture for semantic segmentation that uses max-pooling indices for efficient non-linear upsampling, achieving competitive accuracy with low memory and inference time.
Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, et al.
Extends hierarchical recurrent encoder-decoder networks to open-domain dialogue, showing competitiveness with n-gram models and improvement via bootstrapping from QA pairs and pretrained embeddings.
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Donut is an OCR-free encoder-decoder Transformer that directly processes images and prompts to generate text, eliminating the need for optical character recognition.
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U-Net introduces a symmetric encoder-decoder architecture with skip connections for precise biomedical image segmentation.
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An encoder-decoder architecture that reconstructs input images by masking random patches and leveraging a high proportion of masking for self-supervision.
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PaLI is a joint language-vision model that generates multilingual text from visual and textual inputs using large pre-trained encoder-decoder language models and Vision Transformers.
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BLIP introduces a unified VLP framework with a multimodal encoder-decoder and a captioning-filtering method to learn from noisy web data.