Boosting in Location Space
The goal of object detection is to find objects in an image. An object detector accepts an image and produces a list of locations as $(x,y)$ pairs. Here we introduce a new concept: {\bf location-based boosting}. Location-based boosting differs from previous boosting algorithms because it optimizes a new spatial loss function to combine object detectors, each of which may have marginal performance, into a single, more accurate object detector. A structured representation of object locations as a list of $(x,y)$ pairs is a more natural domain for object detection than the spatially unstructured representation produced by classifiers. Furthermore, this formulation allows us to take advantage of the intuition that large areas of the background are uninteresting and it is not worth expending computational effort on them. This results in a more scalable algorithm because it does not need to take measures to prevent the background data from swamping the foreground data such as subsampling or applying an ad-hoc weighting to the pixels. We first present the theory of location-based boosting, and then motivate it with empirical results on a challenging data set.
Code (0)
등록된 구현이 없습니다.
Tasks
Objectobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify th…
Federated LearningLearning Positive-Incentive Point Sampling in Neural Implicit Fields for Object Pose Estimation
Learning neural implicit fields of 3D shapes is a rapidly emerging field that enables shape representation at arbitrary resolutions. Due to the flexibility, neural implicit fields have succeeded in many research areas, i…
Pose EstimationBoosting Classifiers with Noisy Inference
We present a principled framework to address resource allocation for realizing boosting algorithms on substrates with communication or computation noise. Boosting classifiers (e.g., AdaBoost) make a final decision via a …
Privacy-Preserving Gradient Boosting Decision Trees
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of dif…
Privacy PreservingSensitivityP-BOOST: Parallel Boosting of Optimal Narrow-Band Direction of Arrival Estimators
Optimal Maximum Likelihood (ML), narrow-band direction finding cannot be easily initialized in coherent and low signal to noise ratio environments. Sparse under-determined solvers are considered as viable solutions to th…