CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
- URL: http://arxiv.org/abs/2502.01312v1
- Date: Mon, 03 Feb 2025 12:41:36 GMT
- Title: CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
- Authors: Xiao Lin, Yun Peng, Liuyi Wang, Xianyou Zhong, Minghao Zhu, Jingwei Yang, Chengju Liu, Qijun Chen,
- Abstract summary: Category-level object pose estimation aims to recover the rotation, translation and size of unseen instances within predefined categories.
Deep neural network-based methods have demonstrated remarkable performance but suffer from spurious correlations raised by "unclean" confounders.
We propose CleanPose, a novel approach integrating causal learning and knowledge distillation to enhance category-level pose estimation.
- Score: 18.453617417061245
- License:
- Abstract: Category-level object pose estimation aims to recover the rotation, translation and size of unseen instances within predefined categories. In this task, deep neural network-based methods have demonstrated remarkable performance. However, previous studies show they suffer from spurious correlations raised by "unclean" confounders in models, hindering their performance on novel instances with significant variations. To address this issue, we propose CleanPose, a novel approach integrating causal learning and knowledge distillation to enhance category-level pose estimation. To mitigate the negative effect of unobserved confounders, we develop a causal inference module based on front-door adjustment, which promotes unbiased estimation by reducing potential spurious correlations. Additionally, to further improve generalization ability, we devise a residual-based knowledge distillation method that has proven effective in providing comprehensive category information guidance. Extensive experiments across multiple benchmarks (REAL275, CAMERA25 and HouseCat6D) hightlight the superiority of proposed CleanPose over state-of-the-art methods. Code will be released.
Related papers
- Leveraging counterfactual concepts for debugging and improving CNN model performance [1.1049608786515839]
We propose to leverage counterfactual concepts aiming to enhance the performance of CNN models in image classification tasks.
Our proposed approach utilizes counterfactual reasoning to identify crucial filters used in the decision-making process.
By incorporating counterfactual explanations, we validate unseen model predictions and identify misclassifications.
arXiv Detail & Related papers (2025-01-19T15:50:33Z) - Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information [75.36597470578724]
Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks.
We propose gUided Purification (COUP) algorithm, which purifies while keeping away from the classifier decision boundary.
Experimental results show that COUP can achieve better adversarial robustness under strong attack methods.
arXiv Detail & Related papers (2024-08-12T02:48:00Z) - Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval.
To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss.
Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
arXiv Detail & Related papers (2024-07-03T13:44:20Z) - GenPose: Generative Category-level Object Pose Estimation via Diffusion
Models [5.1998359768382905]
We propose a novel solution by reframing categorylevel object pose estimation as conditional generative modeling.
Our approach achieves state-of-the-art performance on the REAL275 dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics.
arXiv Detail & Related papers (2023-06-18T11:45:42Z) - Adaptive Base-class Suppression and Prior Guidance Network for One-Shot
Object Detection [9.44806128120871]
One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image.
We propose a novel framework, namely Base-class Suppression and Prior Guidance ( BSPG) network to overcome the problem.
Specifically, the objects of base categories can be explicitly detected by a base-class predictor and adaptively eliminated by our base-class suppression module.
A prior guidance module is designed to calculate the correlation of high-level features in a non-parametric manner, producing a class-agnostic prior map to provide the target features with rich semantic cues and guide the subsequent detection process
arXiv Detail & Related papers (2023-03-24T19:04:30Z) - Fast Hierarchical Learning for Few-Shot Object Detection [57.024072600597464]
Transfer learning approaches have recently achieved promising results on the few-shot detection task.
These approaches suffer from catastrophic forgetting'' issue due to finetuning of base detector.
We tackle the aforementioned issues in this work.
arXiv Detail & Related papers (2022-10-10T20:31:19Z) - CATRE: Iterative Point Clouds Alignment for Category-level Object Pose
Refinement [52.41884119329864]
Category-level object pose and size refiner CATRE is able to iteratively enhance pose estimate from point clouds to produce accurate results.
Our approach remarkably outperforms state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed of 85.32Hz.
arXiv Detail & Related papers (2022-07-17T05:55:00Z) - A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for
Aspect and Polarity Classification in Persian Reviews [0.0]
We propose a multi-task learning model based on Convolutional Neural Networks (CNNs)
creating a model alone may not provide the best predictions and lead to errors such as bias and high variance.
This article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews.
arXiv Detail & Related papers (2022-01-17T09:54:35Z) - Improving Music Performance Assessment with Contrastive Learning [78.8942067357231]
This study investigates contrastive learning as a potential method to improve existing MPA systems.
We introduce a weighted contrastive loss suitable for regression tasks applied to a convolutional neural network.
Our results show that contrastive-based methods are able to match and exceed SoTA performance for MPA regression tasks.
arXiv Detail & Related papers (2021-08-03T19:24:25Z) - Few-shot Action Recognition with Prototype-centered Attentive Learning [88.10852114988829]
Prototype-centered Attentive Learning (PAL) model composed of two novel components.
First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective.
Second, PAL integrates a attentive hybrid learning mechanism that can minimize the negative impacts of outliers.
arXiv Detail & Related papers (2021-01-20T11:48:12Z)
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.