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Rethinking Encoder-Decoder Flow Through Shared Structures

arXiv 2501.14535 ↗First posted 24 Jan 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate feature maps sequentially. We introduce banks, shared structures that are used by each decoding block to provide additional context in the decoding process. These structures, through applying them via resampling and feature fusion, improve performance on depth estimation for state-of-the-art transformer-based architectures on natural and synthetic images whilst training on large-scale datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 24 Jan 2025.

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References · 26

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  4. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  5. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
  6. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  7. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  8. 2022EVA: Exploring the Limits of Masked Visual Representation Learning at Scalecited by 685
  9. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
  10. 2021ImageNet-21K Pretraining for the Massescited by 607
  11. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  12. 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 593
14 more
  1. 2016RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentationcited by 463
  2. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 323
  3. 2023MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimationcited by 173
  4. 2023FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterizationcited by 161
  5. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123
  6. 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
  7. 2022Fast Vision Transformers with HiLo Attentioncited by 78
  8. 2024DepthFM: Fast Monocular Depth Estimation with Flow Matchingcited by 76
  9. 2023SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applicationscited by 66
  10. 2023Learning to Upsample by Learning to Samplecited by 57
  11. 2024SHViT: Single-Head Vision Transformer with Memory Efficient Macro Designcited by 55
  12. 2024Denoising Vision Transformerscited by 40
  13. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  14. 2021Real-time Monocular Depth Estimation with Sparse Supervision on Mobilecited by 12

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