G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection

• URL: http://arxiv.org/abs/2402.04672v1
• Date: Wed, 7 Feb 2024 08:57:59 GMT
• Title: G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
• Authors: Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, Nanyang Ye
• Abstract summary: Differentiable Neural Architecture Search (NAS) is known for its high capacity for complex data fitting. Generalizable loss (G-loss) is an OoD-aware objective, preventing NAS from over-fitting. Experimental results on the S-DGOD urban-scene datasets demonstrate that the proposed G-NAS achieves SOTA performance compared to baseline methods.
• Score: 55.86838901572496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we focus on a realistic yet challenging task, Single Domain Generalization Object Detection (S-DGOD), where only one source domain's data can be used for training object detectors, but have to generalize multiple distinct target domains. In S-DGOD, both high-capacity fitting and generalization abilities are needed due to the task's complexity. Differentiable Neural Architecture Search (NAS) is known for its high capacity for complex data fitting and we propose to leverage Differentiable NAS to solve S-DGOD. However, it may confront severe over-fitting issues due to the feature imbalance phenomenon, where parameters optimized by gradient descent are biased to learn from the easy-to-learn features, which are usually non-causal and spuriously correlated to ground truth labels, such as the features of background in object detection data. Consequently, this leads to serious performance degradation, especially in generalizing to unseen target domains with huge domain gaps between the source domain and target domains. To address this issue, we propose the Generalizable loss (G-loss), which is an OoD-aware objective, preventing NAS from over-fitting by using gradient descent to optimize parameters not only on a subset of easy-to-learn features but also the remaining predictive features for generalization, and the overall framework is named G-NAS. Experimental results on the S-DGOD urban-scene datasets demonstrate that the proposed G-NAS achieves SOTA performance compared to baseline methods. Codes are available at https://github.com/wufan-cse/G-NAS.

Related papers

• Object Style Diffusion for Generalized Object Detection in Urban Scene [69.04189353993907]
  We introduce a novel single-domain object detection generalization method, named GoDiff. By integrating pseudo-target domain data with source domain data, we diversify the training dataset. Experimental results demonstrate that our method not only enhances the generalization ability of existing detectors but also functions as a plug-and-play enhancement for other single-domain generalization methods.
  arXiv  Detail & Related papers  (2024-12-18T13:03:00Z)
• PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection [4.592579302639643]
  Single-Domain Generalized Object Detection(S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector. Existing S-DGOD approaches often rely on data augmentation strategies, including a composition of visual transformations, to enhance the detector's generalization ability. We propose PhysAug, a novel physical model-based non-ideal imaging condition data augmentation method, to enhance the adaptability of the S-DGOD tasks.
  arXiv  Detail & Related papers  (2024-12-16T14:18:01Z)
• PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model [77.00221501105788]
  Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification. We present the first work that studies the generalizability of state space models (SSMs) in DG PCC. We propose a novel framework, PointDGMamba, that excels in strong generalizability toward unseen domains.
  arXiv  Detail & Related papers  (2024-08-24T12:53:48Z)
• Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
  Unsupervised domain generalization is fast gaining attention but is still far from well-studied. Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features. DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
  arXiv  Detail & Related papers  (2024-07-10T11:11:36Z)
• DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models [56.584561770857306]
  We propose a novel conditional Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. Specifically, we consider the neural architectures as directed graphs and propose a graph diffusion model for generating them. We validate the effectiveness of DiffusionNAG through extensive experiments in two predictor-based NAS scenarios: Transferable NAS and Bayesian Optimization (BO)-based NAS. When integrated into a BO-based algorithm, DiffusionNAG outperforms existing BO-based NAS approaches, particularly in the large MobileNetV3 search space on the ImageNet 1K dataset.
  arXiv  Detail & Related papers  (2023-05-26T13:58:18Z)
• SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification [44.27324696068285]
  We propose a Single-dataset Unified Generalization (SUG) framework to alleviate the unforeseen domain differences faced by a well-trained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method, which can constrain the learned representations to be domain-agnostic and discriminative. Then, a Sample-level Domain-aware Attention (SDA) strategy is presented, which can selectively enhance easy-to-adapt samples from different sub-domains.
  arXiv  Detail & Related papers  (2023-05-16T04:36:04Z)
• GeNAS: Neural Architecture Search with Better Generalization [14.92869716323226]
  Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization.
  arXiv  Detail & Related papers  (2023-05-15T12:44:54Z)
• CLIP the Gap: A Single Domain Generalization Approach for Object Detection [60.20931827772482]
  Single Domain Generalization tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. We propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss.
  arXiv  Detail & Related papers  (2023-01-13T12:01:18Z)
• Generic Neural Architecture Search via Regression [27.78105839644199]
  We propose a novel and generic neural architecture search (NAS) framework, termed Generic NAS (GenNAS) GenNAS does not use task-specific labels but instead adopts textitregression on a set of manually designed synthetic signal bases for architecture evaluation. We then propose an automatic task search to optimize the combination of synthetic signals using limited downstream-task-specific labels.
  arXiv  Detail & Related papers  (2021-08-04T08:21:12Z)

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.