Shared Neural Space: Unified Precomputed Feature Encoding for Multi-Task and Cross Domain Vision

• URL: http://arxiv.org/abs/2509.20481v1
• Date: Wed, 24 Sep 2025 18:48:58 GMT
• Title: Shared Neural Space: Unified Precomputed Feature Encoding for Multi-Task and Cross Domain Vision
• Authors: Jing Li, Oskar Bartosz, Chengyu Wang, Michal Wnuczynski, Dilshan Godaliyadda, Michael Polley,
• Abstract summary: We propose a universal Neural Space (NS), where an encoder-decoder framework pre-computes features across vision and imaging tasks.<n>Our encoder learns transformation aware, generalizable representations, which enable multiple downstream AI modules to share the same feature space.<n>We demonstrate that imaging and vision modules, such as demosaicing, denoising, depth estimation and semantic segmentation can be performed efficiently in the NS.
• Score: 6.3796451378950385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The majority of AI models in imaging and vision are customized to perform on specific high-precision task. However, this strategy is inefficient for applications with a series of modular tasks, since each requires a mapping into a disparate latent domain. To address this inefficiency, we proposed a universal Neural Space (NS), where an encoder-decoder framework pre-computes features across vision and imaging tasks. Our encoder learns transformation aware, generalizable representations, which enable multiple downstream AI modules to share the same feature space. This architecture reduces redundancy, improves generalization across domain shift, and establishes a foundation for effecient multi-task vision pipelines. Furthermore, as opposed to larger transformer backbones, our backbone is lightweight and CNN-based, allowing for wider across hardware. We furthur demonstrate that imaging and vision modules, such as demosaicing, denoising, depth estimation and semantic segmentation can be performed efficiently in the NS.

Related papers

• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• DAMamba: Vision State Space Model with Dynamic Adaptive Scan [51.81060691414399]
  State space models (SSMs) have recently garnered significant attention in computer vision.<n>We propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions.<n>Based on DAS, we propose the vision backbone DAMamba, which significantly outperforms current state-of-the-art vision Mamba models in vision tasks.
  arXiv  Detail & Related papers  (2025-02-18T08:12:47Z)
• Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation [8.443350618722564]
  This paper proposes an improved Unet model combined with an attention mechanism.<n>It introduces channel attention and spatial attention modules, enhances the model's ability to focus on important features.<n>The improved model performs well in terms of mIoU and pixel accuracy (PA), reaching 76.5% and 95.3% respectively.
  arXiv  Detail & Related papers  (2025-02-06T06:51:23Z)
• VmambaIR: Visual State Space Model for Image Restoration [36.11385876754612]
  We propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks. VmambaIR achieves state-of-the-art (SOTA) performance with much fewer computational resources and parameters.
  arXiv  Detail & Related papers  (2024-03-18T02:38:55Z)
• NiNformer: A Network in Network Transformer with Token Mixing Generated Gating Function [1.3812010983144802]
  This paper introduces a new computational block as an alternative to the Vision Transformer ViT block.<n>The newly proposed block reduces the computational requirements by replacing the normal attention layers with a Network in Network structure.<n>It provides better performance than the baseline architectures on multiple datasets applied in the image classification task of the vision domain.
  arXiv  Detail & Related papers  (2024-03-04T19:08:20Z)
• Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model [83.85856356798531]
  VistaLLM is a visual system that addresses coarse- and fine-grained vision-language tasks. It employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences. We also introduce a novel task, AttCoSeg, which boosts the model's reasoning and grounding capability over multiple input images.
  arXiv  Detail & Related papers  (2023-12-19T18:53:01Z)
• Vision Transformers: From Semantic Segmentation to Dense Prediction [139.15562023284187]
  We explore the global context learning potentials of vision transformers (ViTs) for dense visual prediction. Our motivation is that through learning global context at full receptive field layer by layer, ViTs may capture stronger long-range dependency information. We formulate a family of Hierarchical Local-Global (HLG) Transformers, characterized by local attention within windows and global-attention across windows in a pyramidal architecture.
  arXiv  Detail & Related papers  (2022-07-19T15:49:35Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)
• CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training [112.96224800952724]
  We propose cascaded modulation GAN (CM-GAN) to generate plausible image structures when dealing with large holes in complex images. In each decoder block, global modulation is first applied to perform coarse semantic-aware synthesis structure, then spatial modulation is applied on the output of global modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios.
  arXiv  Detail & Related papers  (2022-03-22T16:13:27Z)
• Vision Transformer with Convolutions Architecture Search [72.70461709267497]
  We propose an architecture search method-Vision Transformer with Convolutions Architecture Search (VTCAS) The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture. It enhances the robustness of the neural network for object recognition, especially in the low illumination indoor scene.
  arXiv  Detail & Related papers  (2022-03-20T02:59:51Z)
• Less is More: Pay Less Attention in Vision Transformers [61.05787583247392]
  Less attention vIsion Transformer builds upon the fact that convolutions, fully-connected layers, and self-attentions have almost equivalent mathematical expressions for processing image patch sequences. The proposed LIT achieves promising performance on image recognition tasks, including image classification, object detection and instance segmentation.
  arXiv  Detail & Related papers  (2021-05-29T05:26:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.