Object-Proposal Evaluation Protocol is 'Gameable'

Object proposals have quickly become the de-facto pre-processing step in a number of vision pipelines (for object detection, object discovery, and other tasks). Their performance is usually evaluated on partially annotated datasets. In this paper, we argue that the choice of using a partially annotated dataset for evaluation of object proposals is problematic -- as we demonstrate via a thought experiment, the evaluation protocol is 'gameable', in the sense that progress under this protocol does not necessarily correspond to a "better" category independent object proposal algorithm. To alleviate this problem, we: (1) Introduce a nearly-fully annotated version of PASCAL VOC dataset, which serves as a test-bed to check if object proposal techniques are overfitting to a particular list of categories. (2) Perform an exhaustive evaluation of object proposal methods on our introduced nearly-fully annotated PASCAL dataset and perform cross-dataset generalization experiments; and (3) Introduce a diagnostic experiment to detect the bias capacity in an object proposal algorithm. This tool circumvents the need to collect a densely annotated dataset, which can be expensive and cumbersome to collect. Finally, we plan to release an easy-to-use toolbox which combines various publicly available implementations of object proposal algorithms which standardizes the proposal generation and evaluation so that new methods can be added and evaluated on different datasets. We hope that the results presented in the paper will motivate the community to test the category independence of various object proposal methods by carefully choosing the evaluation protocol.Comment: 15 pages, 11 figures, 4 table

Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.Comment: Accepted by TPAM

Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster

Interseção sobre união probabilística para treinamento e avaliação de detectores de objetos orientados

Using localization loss terms based on the Intersection-over-Union (IoU) is a recent and promising trend for object detection. However, exploring such loss functions for oriented bounding boxes is a complex task since the IoU is not differentiable. In this work, we propose to represent object regions through probability density functions and define a similarity metric between two objects based on the Hellinger Distance that can be viewed as a Probabilistic IoU (ProbIoU). When Gaussian distributions are used (called Gaussian Bounding Boxes, or GBBs), the ProbIoU presents a differentiable closed-form expression that can be used as a localization loss for object detection. We present a simple mapping scheme from traditional bounding boxes to GBBs, allowing the proposed ProbIoU-based loss terms to be seamlessly integrated into any object detector. Finally, we show that GBBs can represent generic segmentation masks, and they induce a natural binary representation as elliptical regions (EGBBs) that adhere better to the segmentation masks than bounding boxes. Our experimental results show that the proposed localization loss term produces competitive results for object detection using bounding boxes and that EGBBs seem a better alternative for instance segmentation than bounding boxes.O uso de termos de perda de localização baseados no Intersection-over-Union (IoU) é uma tendência recente e promissora para detecção de objetos. No entanto, explorar tais funções de perda para caixas delimitadoras orientadas é uma tarefa desafiadora, pois a IoU não é diferenciável. Neste trabalho, propomos representar regiões de objetos através de funções de densidade de probabilidade e definir uma métrica de similaridade entre dois objetos baseada na Distância de Hellinger que pode ser vista como uma IoU Probabilística (ProbIoU). Quando são usadas distribuições gaussianas (chamadas Gaussian Bounding Boxes, ou GBBs), o ProbIoU apresenta uma expressão de forma fechada diferenciável que pode ser usada como perda de localização para detecção de objetos. Apresentamos um esquema de mapeamento simples de caixas delimitadoras tradicionais para GBBs, permitindo que os termos de perda baseados em ProbIoU propostos sejam perfeitamente integrados a qualquer detector de objetos. Finalmente, mostramos que GBBs podem representar máscaras de segmentação genéricas e induzem uma representação binária natural como regiões elípticas (EGBBs) que aderem melhor às máscaras de segmentação do que caixas delimitadoras. Nossos resultados experimentais mostram que o termo de perda de localização proposto produz resultados competitivos para detecção de objetos usando caixas delimitadoras, e que EGBBs parecem uma alternativa melhor para segmentação de instâncias do que caixas delimitadoras