CROSS-SUPERVISED OBJECT DETECTION

1 INTRODUCTION . Deep architectures have achieved great success in many computer vision tasks including object recognition and the closely related problem of object detection . Modern detectors , such as the Faster RCNN ( Ren et al. , 2015 ) , YOLO ( Redmon et al. , 2016 ) , and RetinaNet ( Lin et al. , 2017 ) , use the same network backbone as popular recognition models . However , even with the same backbone architectures , detection and recognition models require different types of supervision . A good detector relies heavily on precise bounding boxes and labels for each instance ( we shall refer to these as instance-level annotations ) , whereas a recognition model needs only image-level labels . Needless to say , it is more time consuming and expensive to obtain high quality bounding box annotations than class labels . As a result , current detectors are limited to a small set of categories relative to their object recognition counterparts . To address this limitation , it is natural to ask , “ Is it possible to learn detectors with only class labels ? ” This problem is commonly referred to as weakly supervised object detection ( WSOD ) . Early WSOD work ( Hoffman et al. , 2014 ) showed fair performance by directly applying recognition networks to object detection . More recently , researchers have used multiple instance learning methods ( Dietterich et al. , 1997 ) to recast WSOD as a multi-label classification problem ( Bilen & Vedaldi , 2016 ) . However , these weakly supervised detectors perform poorly at localization . Most WSOD experiments have been conducted on the ILSVRC ( Russakovsky et al. , 2015 ) data set , in which images have only a single object , or on the PASCAL VOC ( Everingham et al. , 2010 ) data set , which has only 20 categories . The simplicity of these data sets limits the number and types of distractors in an image , making localization substantially easier . Learning from only class labels , it is challenging to detect objects at different scales in an image that contains many distractors . In particular , as shown in our experiments , weakly supervised object detectors do not work well in complex multi-object scenes , such as the COCO dataset ( Lin et al. , 2014 ) . To address this challenge , we focus on a form of learning in which the localization of classes with only object labels ( weakly labeled classes ) can benefit from other classes that have ground truth bounding boxes ( fully labeled classes ) . We refer to this interesting learning paradigm as crosssupervised object detection ( CSOD ) . While several works ( Hoffman et al. , 2014 ; Tang et al. , 2016 ; Yang et al. , 2019a ; Redmon & Farhadi , 2017 ) have explored this problem before , they still have the same limitation as the WSOD work we mentioned above . Those cross-supervised object detectors work under simplified scenarios ( e.g. , ILSVRC data set ) where images contain single objects and are object-centered . They struggle to learn under more complex and realistic scenarios , where there are multiple objects from potentially very different classes , and objects could be small and appear anywhere in the images . In this work , we show that by doing multi-task learning on both weaklysupervised base classes and fully-supervised novel classes , our model is able to learn a good detector under the CSOD setting . More formally , we define CSOD as follows . At training time , we are given 1 ) images contain objects from both base and novel classes , 2 ) both class labels and ground truth bounding boxes for base objects , and 3 ) only class labels for novel objects . Our goal is to detect novel objects . In CSOD , base classes and novel classes are disjoint . Thus , it can be seen as performing fullysupervised detection on the base classes and weakly supervised detection on the novel classes . It has similarities to both transfer learning and semi-supervised learning , since it transfer knowledge from base class to novel class and have more information about some instances than other instances . However , CSOD represents a distinct and novel paradigm for learning . The current weakly-supervised method has several drawbacks to learn from a multi objects image . As shown in Fig . 1 , a weakly supervised object detector tends to detect only the most discriminating part of novel objects instead of the whole object . Notice how only the head of the person , and not the whole body , is detected . Another issue is that the localizer for one object ( e.g. , the horse ) may be confused by the occurrence of another object , such as the person on the horse . This example illustrates the gap between detection and recognition : without ground truth bounding boxes , the detector acts like a standard recognition model – focusing on discriminating rather than detecting . In this paper , we explore two major mechanisms for improving on this . Our first mechanism is unifying detection and recognition . Using the same network backbone architecture , recognition and detection can be seen as image-level classification and region-level classification respectively , suggesting a strong relation between them . In particular , it suggests a shared training framework in which the same backbone is used with different heads for detection and recognition . Thus , we combine a detection head learned from ground truth bounding boxes , and a recognition head learned in a weakly supervised fashion from class labels . Unlike a traditional recognition head , our recognition head produces a class score for multiple proposals and is capable of detecting objects . The second mechanism is learning a spatial correlation module to reduce the gap between detection and recognition . It takes several high-confidence bounding boxes produced by the recognition head as input , and learns to regress ground truth bounding boxes . By combining these mechanisms together , our model outperforms all previous models when all novel objects are weakly labeled . In summary , our contributions are three-fold . First , we define a new task—cross-supervised object detection , which enables us to leverage knowledge from fully labeled base categories to help learn a robust detector from novel object class labels only . Second , we propose a unified framework in which two heads are learned from class labels and detection labels respectively , along with a spatial correlation module bridging the gap between recognition and detection . Third , we significantly outperform existing methods ( Zhang et al . ( 2018a ) ; Tang et al . ( 2017 ; 2018 ) ) on PASCAL VOC and COCO , suggesting that CSOD could be a promising approach for expanding object detection to a much larger number of categories . 2 RELATED WORK . Weakly supervised object detection . WSOD ( Kosugi et al . ( 2019 ) ; Zeng et al . ( 2019 ) ; Yang et al . ( 2019b ) ; Wan et al . ( 2019 ) ; Arun et al . ( 2019 ) ; Wan et al . ( 2018 ) ; Zhang et al . ( 2018b ) ; Ren et al . ( 2020 ) ; Zhang et al . ( 2018c ) ; Li et al . ( 2019 ) ; Gao et al . ( 2019b ) ; Kosugi et al . ( 2019 ) ) attempts to learn a detector with only image category labels . Most of these methods adopt the idea of Multiple Instance Learning ( Dietterich et al . ( 1997 ) ) to recast WSOD as a multi-label classification task . Bilen & Vedaldi ( 2016 ) propose an end-to-end network by modifying a classifier to operate at the level of image regions , serving as a region selector and a classifier simultaneously . Tang et al . ( 2017 ) and Tang et al . ( 2018 ) find that several iterations of online refinement based on the outputs of previous iterations boosts performance . Wei et al . ( 2018 ) and Diba et al . ( 2017 ) use semantic segmentation based on class activation maps ( Zhou et al . ( 2016 ) ) to help generate tight bounding boxes . However , WSOD methods tend to focus on the most discriminating part of an object and are prone to distractions from co-occurring objects . Detecting a part of the object or distractors represents convergence to a local optimum . Thus , their performance depends heavily on initialization . In comparison , our proposed cross-supervised object detector alleviates the issue of getting trapped in a local optimum by leveraging knowledge learned from fully labeled base categories . Cross-supervised object detection . There are several previous works using both image-level and instance-level annotations . Kuen et al . ( 2019 ) learned a parameter transferring function between a classifier and a detector , enabling an image-based classification network to be adapted to a regionbased classification network . Hoffman et al . ( 2014 ) and Tang et al . ( 2016 ) propose methods of adaptation for knowledge transfer from classification features to detection features . Uijlings et al . ( 2018 ) use a proposal generator trained on base classes to transfer knowledge by leveraging a MIL framework , organized in a semantic hierarchy . Hoffman et al . ( 2015 ) design a three-step framework to learn a feature representation from weakly supervised classes and strongly supervised classes jointly . However , these methods can only perform object localization in single object scenes such as ILSVRC , whereas our method can perform object detection in complex multi-object scenes as well , e.g . COCO . Also , it is worth noting that we are doing multi-task learning , which means that we jointly learn from base and novel classes . In comparison , some works ( Uijlings et al. , 2018 ) are doing transfer learning . They first learn a model on base classes and then transfer and fine-tune the model on novel classes . Gao et al . ( 2019a ) use a few instance-level labels and a large scale of image-level labels for each category in a training-mining framework , which is referred to as semisupervised detection . Zhang et al . ( 2018a ) propose a framework named MSD that learn objectness on base categories and use it to reject distractors when learning novel objects . In comparison , our spatial correlation module not only learns objectness , but also refines coarse bounding boxes . Further , our model learns from both base and novel classes instead of only novel classes . 3 CROSS-SUPERVISED OBJECT DETECTION . CSOD requires us to learn from instance-level annotations ( detection labels ) and image-level annotations ( recognition labels ) . In this section , we explain the unification of detection and recognition and introduce our framework . In the next section , we describe our novel spatial correlation module . 3.1 UNIFYING DETECTION AND RECOGNITION . How to learn a detector from both instance-level and image-level annotations ? Since detection and recognition can be seen as region-level and image-level classification respectively , a natural choice is to design a unified framework that combines a detection head and a recognition head that can learn from image-level and instance-level annotations respectively . Here we exploit several baselines to unify the detection and recognition head . ( 1 ) Finetune . We first learn through the detection head on base classes with fully labeled samples . Then , we finetune our model using the recognition head on novel classes with only class labels . ( 2 ) Two Head . We simultaneously learn the detection and recognition head on base and novel classes , respectively . The weights of the backbones are updated using the loss backpropagated from both heads jointly . ( 3 ) Two head + . Instead of learning only on novel classes , we learn the recognition head from class labels of both base and novel classes whereas the recognition head remain the same . ( 4 ) Two Branch . Instead of having two shared fully-connected layers after RoI pooling layer ( see Fig . 2 ) , we make these two fully-connected layers seperated , allowing the detection and recognition head to have separate unshared pair of fully-connected layers each . Everything else is the same as the Two Head baseline . Experiments are conducted to compare these baselines in § 5.1 and § 5.2 . Our proposed model is based on Two Head . We will discuss the details in § 3.2 . The connection between the recognition and detection head . The baselines mentioned above only use the recognition head to detect novel objects , ignoring the fact that a detection head can play the same role even better . A majority of WSOD methods ( Tang et al . ( 2017 ) ; Wan et al . ( 2019 ) ; Wei et al . ( 2018 ) ) find that re-train a new detector taking the top-scoring bounding boxes from a weakly supervised object detector as ground truth marginally improve the performance . Even with coarse and noisy pseudo bounding boxes , a standard object detector produces better detection results than a weakly supervised object detector . Keeping this hypothesis in mind , we introduce a guidance from the recognition head to the detection head . For each of the novel categories existing in a training sample , the recognition head outputs the top-scoring bounding box , which are then used by the detection head as supervision in that sample .

Summary: This paper introduces a new method for training an object detector on a dataset that consists of some object categories with instance-level bounding box annotations, as well as some other object categories with only image-level labels. The topic is interesting, important, and potentially very useful for real applications. The authors propose an idea to transfer knowledge from a weakly supervised (WS) detection head into a fully supervised (FS) detection head, by producing pseudo-ground-truth bounding boxes for classes with image-level labels. The idea is straightforward and interesting. Experiments show significant and consistent gains in various scenarios. The paper is well-written.