Polyhedral Conic Classifiers for Visual Object Detection and Classification

2017-07-01 · CVPR 2017 7 · Hakan Cevikalp, Bill Triggs

We propose a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these tasks the classification problems are both numerically imbalanced -- positive (object class) training and test windows are much rarer than negative (non-class) ones -- and geometrically asymmetric -- the positive samples typically form compact, visually-coherent groups while negatives are much more diverse, including anything at all that is not a well-centred sample from the target class. It is difficult to cover such negative classes using training samples, and doubly so in `open set' applications where run-time negatives may stem from classes that were not seen at all during training. So there is a need for discriminants whose decision regions focus on tightly circumscribing the positive class, while still taking account of negatives in zones where the two classes overlap. This paper introduces a family of quasi-linear "polyhedral conic" discriminants whose positive regions are distorted L1 balls. The methods have properties and run-time complexities comparable to linear Support Vector Machines (SVMs), and they can be trained from either binary or positive-only samples using constrained quadratic programs related to SVMs. Our experiments show that they significantly outperform both linear SVMs and existing one-class discriminants on a wide range of object detection, open set recognition and conventional closed-set classification tasks.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Classification General ClassificationObjectobject-detection Object DetectionOpen Set Learning

Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition

2021-02-24 · Hakan Cevikalp, Bedirhan Uzun, Okan Köpüklü, Gurkan Ozturk

In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function. The p…

Anomaly Detection General Classification Open Set Learning

Polyhedral Conic Classifier for CTR Prediction

2024-06-06 · Beyza Turkmen, Ramazan Tarik Turksoy, Hasan Saribas, Hakan Cevikalp

This paper introduces a novel approach for click-through rate (CTR) prediction within industrial recommender systems, addressing the inherent challenges of numerical imbalance and geometric asymmetry. These challenges st…

Click-Through Rate Prediction Diversity Prediction Recommendation Systems

SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions

2026-04-24 · Kang Liu, Jianchen Hu, Wei Peng arxiv

Classical ReLU-based Input Convex Neural Networks (ICNNs) are equivalent to the optimal value functions of Linear Programming (LP). This intrinsic structural equivalence restricts their representational capacity to piece…

Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts

2020-08-03 · Nicolas Gonthier, Saïd Ladjal, Yann Gousseau

Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on …

Multiple Instance Learning object-detection Object Detection Weakly Supervised Object Detection

On the convex hull of convex quadratic optimization problems with indicators

2022-01-02 · Linchuan Wei, Alper Atamtürk, Andrés Gómez, Simge Küçükyavuz

We consider the convex quadratic optimization problem with indicator variables and arbitrary constraints on the indicators. We show that a convex hull description of the associated mixed-integer set in an extended space …