End-to-End Multi-Object Detection with a Regularized Mixture Model

• URL: http://arxiv.org/abs/2205.08714v3
• Date: Fri, 28 Apr 2023 06:08:12 GMT
• Title: End-to-End Multi-Object Detection with a Regularized Mixture Model
• Authors: Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo, Inseop Chung, Nojun Kwak
• Abstract summary: Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes. We propose a novel framework to train an end-to-end multi-object detector consisting of only two terms: negative log-likelihood (NLL) and a regularization term.
• Score: 26.19278003378703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS). However, during training, they still heavily rely on heuristics and hand-crafted processes which deteriorate the reliability of the predicted confidence score. In this paper, we propose a novel framework to train an end-to-end multi-object detector consisting of only two terms: negative log-likelihood (NLL) and a regularization term. In doing so, the multi-object detection problem is treated as density estimation of the ground truth bounding boxes utilizing a regularized mixture density model. The proposed \textit{end-to-end multi-object Detection with a Regularized Mixture Model} (D-RMM) is trained by minimizing the NLL with the proposed regularization term, maximum component maximization (MCM) loss, preventing duplicate predictions. Our method reduces the heuristics of the training process and improves the reliability of the predicted confidence score. Moreover, our D-RMM outperforms the previous end-to-end detectors on MS COCO dataset.

Related papers

• Analytic Continual Test-Time Adaptation for Multi-Modality Corruption [23.545997349882857]
  Test-Time Adaptation (TTA) aims to help pre-trained models bridge the gap between source and target datasets. We propose a novel approach, Multi-modality Dynamic Analytic Adapter (MDAA) for MM-CTTA tasks. MDAA achieves state-of-the-art performance on MM-CTTA while ensuring reliable model adaptation.
  arXiv  Detail & Related papers  (2024-10-29T01:21:24Z)
• MOLA: Enhancing Industrial Process Monitoring Using Multi-Block Orthogonal Long Short-Term Memory Autoencoder [3.7028696448588487]
  We introduce MOLA: a Multi-block Orthogonal Long short-term memory Autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. We propose a multi-block monitoring structure, which categorizes the process variables into multiple blocks by leveraging expert process knowledge. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman Process.
  arXiv  Detail & Related papers  (2024-10-10T00:49:43Z)
• Variational Density Propagation Continual Learning [0.0]
  Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data. This paper proposes a framework for adapting to data distribution drift modeled by benchmark Continual Learning datasets.
  arXiv  Detail & Related papers  (2023-08-22T21:51:39Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Threshold-Consistent Margin Loss for Open-World Deep Metric Learning [42.03620337000911]
  Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures. Inconsistency often complicates the threshold selection process when deploying commercial image retrieval systems. We propose a novel variance-based metric called Operating-Point-Inconsistency-Score (OPIS) that quantifies the variance in the operating characteristics across classes.
  arXiv  Detail & Related papers  (2023-07-08T21:16:41Z)
• Training Normalizing Flows with the Precision-Recall Divergence [73.92251251511199]
  We show that achieving a specified precision-recall trade-off corresponds to minimising -divergences from a family we call the em PR-divergences We propose a novel generative model that is able to train a normalizing flow to minimise any -divergence, and in particular, achieve a given precision-recall trade-off.
  arXiv  Detail & Related papers  (2023-02-01T17:46:47Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions [91.63716984911278]
  We introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks.
  arXiv  Detail & Related papers  (2021-11-11T14:28:12Z)
• Multivariate Density Estimation with Deep Neural Mixture Models [0.0]
  Deep neural networks (DNNs) have seldom been applied to density estimation. This paper extends our previous work on Neural Mixture Densities (NMMs) A maximum-likelihood (ML) algorithm for estimating Deep NMMs (DNMMs) is handed out. The class of probability density functions that can be modeled to any degree of precision via DNMMs is formally defined.
  arXiv  Detail & Related papers  (2020-12-06T23:03:48Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.