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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2507.23357 |
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| _version_ | 1866915478356623360 |
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| author | Bourceanu, Radu-Andrei De La Fuente, Neil Grimm, Jan Jardan, Andrei Manucharyan, Andriy Weiss, Cornelius Cremers, Daniel Pflugfelder, Roman |
| author_facet | Bourceanu, Radu-Andrei De La Fuente, Neil Grimm, Jan Jardan, Andrei Manucharyan, Andriy Weiss, Cornelius Cremers, Daniel Pflugfelder, Roman |
| contents | This report analyzes the evolution of key design patterns in computer vision by examining six influential papers. The analysis begins with foundational architectures for image recognition. We review ResNet, which introduced residual connections to overcome the vanishing gradient problem and enable effective training of significantly deeper convolutional networks. Subsequently, we examine the Vision Transformer (ViT), which established a new paradigm by applying the Transformer architecture to sequences of image patches, demonstrating the efficacy of attention-based models for large-scale image recognition. Building on these visual representation backbones, we investigate generative models. Generative Adversarial Networks (GANs) are analyzed for their novel adversarial training process, which challenges a generator against a discriminator to learn complex data distributions. Then, Latent Diffusion Models (LDMs) are covered, which improve upon prior generative methods by performing a sequential denoising process in a perceptually compressed latent space. LDMs achieve high-fidelity synthesis with greater computational efficiency, representing the current state-of-the-art for image generation. Finally, we explore self-supervised learning techniques that reduce dependency on labeled data. DINO is a self-distillation framework in which a student network learns to match the output of a momentum-updated teacher, yielding features with strong k-NN classification performance. We conclude with Masked Autoencoders (MAE), which utilize an asymmetric encoder-decoder design to reconstruct heavily masked inputs, providing a highly scalable and effective method for pre-training large-scale vision models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures Bourceanu, Radu-Andrei De La Fuente, Neil Grimm, Jan Jardan, Andrei Manucharyan, Andriy Weiss, Cornelius Cremers, Daniel Pflugfelder, Roman Computer Vision and Pattern Recognition This report analyzes the evolution of key design patterns in computer vision by examining six influential papers. The analysis begins with foundational architectures for image recognition. We review ResNet, which introduced residual connections to overcome the vanishing gradient problem and enable effective training of significantly deeper convolutional networks. Subsequently, we examine the Vision Transformer (ViT), which established a new paradigm by applying the Transformer architecture to sequences of image patches, demonstrating the efficacy of attention-based models for large-scale image recognition. Building on these visual representation backbones, we investigate generative models. Generative Adversarial Networks (GANs) are analyzed for their novel adversarial training process, which challenges a generator against a discriminator to learn complex data distributions. Then, Latent Diffusion Models (LDMs) are covered, which improve upon prior generative methods by performing a sequential denoising process in a perceptually compressed latent space. LDMs achieve high-fidelity synthesis with greater computational efficiency, representing the current state-of-the-art for image generation. Finally, we explore self-supervised learning techniques that reduce dependency on labeled data. DINO is a self-distillation framework in which a student network learns to match the output of a momentum-updated teacher, yielding features with strong k-NN classification performance. We conclude with Masked Autoencoders (MAE), which utilize an asymmetric encoder-decoder design to reconstruct heavily masked inputs, providing a highly scalable and effective method for pre-training large-scale vision models. |
| title | Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.23357 |