# LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection

arXiv: https://arxiv.org/abs/2503.00747 · HTML: https://mlsanity.com/paper/2503.00747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 74 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers](https://mlsanity.com/paper/2203.04838/) (2022)
- [ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning](https://mlsanity.com/paper/2206.13559/) (2022)
- [MPViT: Multi-Path Vision Transformer for Dense Prediction](https://mlsanity.com/paper/2112.11010/) (2021)
- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation](https://mlsanity.com/paper/2404.04256/) (2024)
- [ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions](https://mlsanity.com/paper/2403.07392/) (2024)
- [StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation](https://mlsanity.com/paper/2408.01343/) (2024)
- [Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection](https://mlsanity.com/paper/2408.04326/) (2024)
- [Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark](https://mlsanity.com/paper/2402.02242/) (2024)
- [A social path to human-like artificial intelligence](https://mlsanity.com/paper/2405.15815/) (2024)
- [Light Field Salient Object Detection: A Review and Benchmark](https://mlsanity.com/paper/2010.04968/) (2020)
- [Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance](https://mlsanity.com/paper/2110.00698/) (2021)
- [Light Field Saliency Detection with Deep Convolutional Networks](https://mlsanity.com/paper/1906.08331/) (2019)
- [DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection](https://mlsanity.com/paper/2012.15124/) (2020)
- [LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise](https://mlsanity.com/paper/2210.15392/) (2022)
- [Guided Focal Stack Refinement Network for Light Field Salient Object Detection](https://mlsanity.com/paper/2305.05260/) (2023)
- [LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection](https://mlsanity.com/paper/2411.06652/) (2024)
- [OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation](https://mlsanity.com/paper/2307.15588/) (2023)
- [LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras](https://mlsanity.com/paper/2401.16712/) (2024)

## Abstract (arXiv)

Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF representations, leaving the field without a unified learning framework. To bridge this gap, we present LFX, the first unified framework for LF perception. LFX establishes a representation-invariant feature modulation space, enabling it to adapt to heterogeneous LF representations and diverse perception tasks. Specifically, we propose Field-of-Parallax Angular Subspace Modeling (FoP-ASM), which assigns an independent angular marker to each auxiliary view, enabling view-wise independent modeling. Meanwhile, shared manifold subspace constraints and regularization losses enforce globally consistent semantic modulation across views. Extensive evaluations across three LF benchmarks show that LFX achieves state-of-the-art results across distinct LF representations, outperforming representation-specific methods by up to 12% and 20% with 0.029/0.027 MAE for salient object detection, and achieving 84.37 mIoU for semantic segmentation. The source code will be made publicly available at https://github.com/FeiT-FeiTeng/LFX.