# Scalable Object Detection using Deep Neural Networks

arXiv: https://arxiv.org/abs/1312.2249 · HTML: https://mlsanity.com/paper/1312.2249/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2013.

## Built on by (most cited first, 30 of 31)

- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [MeMOT: Multi-Object Tracking with Memory](https://mlsanity.com/paper/2203.16761/) (2022)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [The surprising impact of mask-head architecture on novel class segmentation](https://mlsanity.com/paper/2104.00613/) (2021)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss](https://mlsanity.com/paper/2105.06345/) (2021)
- [Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection](https://mlsanity.com/paper/2103.04009/) (2021)
- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery](https://mlsanity.com/paper/2503.19202/) (2025)
- [A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction](https://mlsanity.com/paper/2508.01585/) (2025)
- [Maneuver-based Anchor Trajectory Hypotheses at Roundabouts](https://mlsanity.com/paper/2104.11180/) (2021)
- [Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction](https://mlsanity.com/paper/2206.14647/) (2022)
- [Evolution of a Web-Scale Near Duplicate Image Detection System](https://mlsanity.com/paper/2209.08433/) (2022)
- [Directly Optimizing IoU for Bounding Box Localization](https://mlsanity.com/paper/2304.07256/) (2023)
- [TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation](https://mlsanity.com/paper/2409.16678/) (2024)
- [A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application](https://mlsanity.com/paper/2101.07383/) (2021)
- [MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm](https://mlsanity.com/paper/2201.04341/) (2022)
- [Convergence of a New Learning Algorithm](https://mlsanity.com/paper/2202.12829/) (2022)
- [Scale Invariant Semantic Segmentation with RGB-D Fusion](https://mlsanity.com/paper/2204.04679/) (2022)
- [OSIS: Efficient One-stage Network for 3D Instance Segmentation](https://mlsanity.com/paper/2303.07011/) (2023)
- [A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection](https://mlsanity.com/paper/2305.09407/) (2023)
- [Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions](https://mlsanity.com/paper/2311.10780/) (2023)
- [Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving](https://mlsanity.com/paper/2402.02026/) (2024)
- [Improving Detection of Person Class Using Dense Pooling](https://mlsanity.com/paper/2410.20966/) (2024)
- [Region-Aware Deformable Convolutions](https://mlsanity.com/paper/2509.15436/) (2025)

## Abstract (arXiv)

Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.