Determinantal Point Process as an alternative to NMS

• URL: http://arxiv.org/abs/2008.11451v2
• Date: Thu, 20 Jun 2024 11:18:54 GMT
• Title: Determinantal Point Process as an alternative to NMS
• Abstract summary: A subsetal point process (DPP) inspired alternative to non-maximum suppression (NMS) has become an integral step in all state-of-the-art object detection frameworks. We pose NMS as a subset selection problem and posit that directly incorporating DPP like framework can improve the overall performance of the object detection system.
• Score: 24.802857965503982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a determinantal point process (DPP) inspired alternative to non-maximum suppression (NMS) which has become an integral step in all state-of-the-art object detection frameworks. DPPs have been shown to encourage diversity in subset selection problems. We pose NMS as a subset selection problem and posit that directly incorporating DPP like framework can improve the overall performance of the object detection system. We propose an optimization problem which takes the same inputs as NMS, but introduces a novel sub-modularity based diverse subset selection functional. Our results strongly indicate that the modifications proposed in this paper can provide consistent improvements to state-of-the-art object detection pipelines.

Related papers

• Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs [55.715754117956514]
  Multiple Sequence Alignments provide protein language models with explicit evolutionary context.<n>MSA subsampling is unavoidable under limited token budgets.<n>We introduce AP-REASONER, which infers a fixed-budget MSA subset using factor-graph reasoning.
  arXiv  Detail & Related papers  (2026-07-24T13:55:21Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios [54.58186816693791]
  environments constantly change over time and space, posing significant challenges for object detectors trained based on a closed-set assumption.<n>We propose a new mechanism, converting the fine-tuning process to a specific- parameter generation.<n>In particular, we first design a dual-path LoRA-based domain-aware adapter that disentangles features into domain-invariant and domain-specific components.
  arXiv  Detail & Related papers  (2025-06-30T17:14:12Z)
• WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection [47.93621184800604]
  Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams.<n>Current state-of-the-art detectors have yet to achieve perfect quality.<n>We introduce WWAggr -- a novel task-specific method of ensemble aggregation based on the Wasserstein distance.
  arXiv  Detail & Related papers  (2025-06-09T13:52:10Z)
• Localized Distributional Robustness in Submodular Multi-Task Subset Selection [5.116582735311639]
  We propose a regularization term which makes use of the relative entropy to the standard multi-task objective. We show that this novel formulation is equivalent to a monotone increasing function composed with a submodular function. We conclude that our novel formulation produces a solution that is locally distributional robust, and computationally inexpensive.
  arXiv  Detail & Related papers  (2024-04-04T19:06:29Z)
• Dynamically configured physics-informed neural network in topology optimization applications [4.403140515138818]
  The physics-informed neural network (PINN) can avoid generating enormous amounts of data when solving forward problems. A dynamically configured PINN-based topology optimization (DCPINN-TO) method is proposed. The accuracy of the displacement prediction and optimization results indicate that the DCPINN-TO method is effective and efficient.
  arXiv  Detail & Related papers  (2023-12-12T05:35:30Z)
• AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems [49.1574468325115]
  Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications. We develop a component-oriented framework for DOPs called Adjustable Components for Dynamic Problems (AbCD) Our results highlight existing problems in the DOP field that need to be addressed in the future development of algorithms and components.
  arXiv  Detail & Related papers  (2023-10-09T08:11:31Z)
• AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [103.41269503488546]
  Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. We propose a novel method AdjointDPM, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs. It then uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters.
  arXiv  Detail & Related papers  (2023-07-20T09:06:21Z)
• Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection [43.481591776038144]
  Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. We propose a novel backbone for WSOD based on our tailored Vision Transformer named Weakly Supervised Transformer Detection Network (WSTDN)
  arXiv  Detail & Related papers  (2023-03-27T08:42:45Z)
• Multi-Objective GFlowNets [59.16787189214784]
  We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse optimal solutions, based on GFlowNets.
  arXiv  Detail & Related papers  (2022-10-23T16:15:36Z)
• Expert-Guided Symmetry Detection in Markov Decision Processes [0.0]
  We propose a paradigm that aims to detect the presence of some transformations of the state-action space for which the MDP dynamics is invariant. The results show that the model distributional shift is reduced when the dataset is augmented with the data obtained by using the detected symmetries.
  arXiv  Detail & Related papers  (2021-11-19T16:12:30Z)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach [128.62787284435007]
  We propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence. In particular, we show that the inductive bias introduced by the permutation-invariant neural architecture enables MF-PPO to outperform existing competitors.
  arXiv  Detail & Related papers  (2021-05-18T04:35:41Z)
• End-to-End Object Detection with Fully Convolutional Network [71.56728221604158]
  We introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection. A simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. Our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.
  arXiv  Detail & Related papers  (2020-12-07T09:14:55Z)
• Decomposition in Decision and Objective Space for Multi-Modal Multi-Objective Optimization [15.681236469530397]
  Multi-modal multi-objective optimization problems (MMMOPs) have multiple subsets within the Pareto-optimal Set. Prevalent multi-objective evolutionary algorithms are not purely designed to search for multiple solution subsets, whereas, algorithms designed for MMMOPs demonstrate degraded performance in the objective space. This motivates the design of better algorithms for addressing MMMOPs.
  arXiv  Detail & Related papers  (2020-06-04T03:18:47Z)

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.