Over the past years, a number of deep learning approaches have achieved remarkable performance in object detection [Girshick et al.2014, Girshick2015, Ren et al.2015, Redmon et al.2016, Liu et al.2016, He et al.2017]. However, the successes of these deep detectors heavily depend on the large-scale detection benchmarks with fully-annotated bounding boxes. In practice, the fully-annotated training set may be limited for a given target detection task, which can restrict the power of deep detectors.
One popular solution is to collect extra detection images but with easily-annotated labels (e.g., image-level supervision). In this case, weakly-supervised [Diba et al.2016, Kantorov et al.2016, Bilen and Vedaldi2016, Li et al.2016, Cinbis, Verbeek, and Schmid2017, Tang et al.2017] or semi-supervised approaches [Tang et al.2016, Singh, Xiao, and Lee2016, Liang et al.2016, Dong et al.2017] can be used to relieve annotation difficulties in detection. However, the performance of these detectors is often limited, because of lacking sufficient supervision on the training images.
. Compared to the weakly/semi-supervised solution, this is often a preferable choice without extra data collection. More importantly, the source-domain knowledge is an effective supervision to generalize the learning procedure of target domain, when the training set is scarce. However, transfer learning for low-shot detection is still an open challenge due to the following reasons. First, it is inappropriate to apply the general transfer strategy of object detection (i.e., initializing deep detectors from the pretrained deep classifiers), when the target detection set is limited. This is mainly because, fine-tuning with such small target sets is often hard to eliminate the task difference between detection and classification. Second, deep detectors are more prone to overfitting during transfer learning, compared to deep classifiers. It is mainly because that, detectors have to learn more object-specific representations for both localization and classification tasks of detection. Finally, simple fine-tuning may reduce transferability, since it often ignores the important object knowledge from both source and target domains.
To address the challenges above, we propose a low-shot transfer detector (LSTD) in this paper, which is the first transfer learning solution for low-shot detection, according to our best knowledge. The main contributions are described as follows. First, we design a novel deep architecture of LSTD, which can boost low-shot detection via incorporating the strengths of two well-known deep detectors, i.e., SSD [Liu et al.2016] and Faster RCNN [Ren et al.2016], into a non-trivial deep framework. In addition, our LSTD can flexibly perform bounding box regression and object classification on two different model parts, which promotes a handy transfer learning procedure for low-shot detection. Second, we propose a novel regularized transfer learning framework for LSTD, where we can flexibly transfer from source-domain LSTD to target-domain LSTD, avoiding the task difference (such as transferring from classification to detection in the general strategy). In this case, target-domain LSTD can sufficiently integrate common object characteristics of large-scale detection data from source-domain LSTD. Furthermore, we enhance fine-tuning with a novel regularization consisting of transfer knowledge (TK) and background depression (BD). TK transfers the source-object-label knowledge for each target-domain proposal, in order to generalize low-shot learning in the target domain. BD integrates the bounding box knowledge of target images as an extra supervision on feature maps, so that LSTD can suppress background disturbances while focus on objects during transferring. Finally, our LSTD outperforms other state-of-the-art approaches on a number of low-shot detection experiments, showing that LSTD is a preferable deep detector for low-shot scenarios.
2 Related Works
Object Detection. Recent developments in object detection are driven by deep learning models [Girshick et al.2014, Girshick2015, Ren et al.2015, Redmon et al.2016, Liu et al.2016, He et al.2017]. We mainly discuss two popular deep detectors, i.e., Faster RCNN [Ren et al.2015] and SSD [Liu et al.2016], which are closely relevant to our approach. Faster RCNN is a popular region-proposal architecture, where object proposals are firstly generated from region proposal network (RPN) and then fed into Fast RCNN [Girshick2015] for end-to-end detection. SSD is a widely-used one-stage detection architecture, where the multi-layer design of bounding box regression can efficiently localize objects with various sizes. Both approaches have achieved the stat-of-the-art detection performance on the large-scale data sets (e.g. Pascal VOC and COCO). However, one may be stuck in troubles by directly using Faster RCNN and SSD with a few training examples, since the architecture design in these approaches lacks the low-shot considerations. For Faster RCNN, the bounding box regression design is not effective for low-shot detection. The main reason is that the bounding box regressor in Faster RCNN is separate for each object category. In this case, the regressor for target domain has to be randomly initialized without any pre-trained parameters from source domain. Obviously, it can deteriorate model transferability, especially for low-shot target cases. For SSD, the object classification design is not effective for low-shot detection. It is mainly because, SSD directly addresses the -object classification (i.e., objects + background) on the default boxes, without any coarse-to-fine analysis. When few images are available, the random initialization of this classifier may trap into training difficulties.
Low-shot Learning. Low-shot learning is mainly inspired by the fact that humans can learn new concepts with little supervision [Lake, Salakhutdinov, and Tenenbaum2015]
. Recently, a number of related approaches have been proposed by Bayesian program learning[Lake, Salakhutdinov, and Tenenbaum2015], memory machines [Graves, Wayne, and Danihelka2014, Santoro et al.2016, Vinyals et al.2016], and so on. However, the existing low-shot approaches are mainly designed for the standard classification task [Xu, Zhu, and Yang2017, Hariharan and Girshick2017]. For object detection, a low-shot detector [Dong et al.2017]
has been proposed recently in a semi-supervised learning framework. But like other semi-supervised[Hoffman et al.2014, Tang et al.2016, Singh, Xiao, and Lee2016, Liang et al.2016] or weakly-supervised [Diba et al.2016, Kantorov et al.2016, Bilen and Vedaldi2016, Li et al.2016, Cinbis, Verbeek, and Schmid2017] detectors, its performance is often limited because of lacking effective supervision on the training images. Finally, transfer learning [Yosinski et al.2014, Sharif Razavian et al.2014] is a reasonable choice when the training set is small, since large-scale source benchmarks can generalize the learning procedure in the low-shot target domain [Fei-Fei, Fergus, and Perona2006, Hinton, Vinyals, and Dean2015]. However, simple fine-tuning with standard deep detectors may reduce the detection performance, as both object localization and classification in these architectures lack effective transfer learning designs for low-shot detection. Furthermore, the object knowledge from both source and target domains may not be fully considered when fine-tuning with a few target images.
Different from the previous approaches, we propose a novel low-shot transfer detector (LSTD) for object detection with few annotated images. Specifically, we first introduce a flexible deep architecture of LSTD, where we leverage the advantages from both Faster RCNN and SSD to alleviate transfer difficulties in low-shot learning. Furthermore, we design a regularized transfer learning framework for LSTD. With the proposed regularization, LSTD can integrate the object knowledge from both source and target domains to enhance transfer learning of low-shot detection.
3 Low-Shot Transfer Detector (LSTD)
In this section, we describe the proposed low-shot transfer detector (LSTD) in detail. First, we introduce the basic deep architecture of our LSTD, and explain why it is an effective detector when the target data set is limited. Then, we design a novel regularized transfer learning framework for LSTD, where the background-depression and transfer-knowledge regularizations can enhance low-shot detection, by leveraging object knowledge respectively from both target and source domains.
3.1 Basic Deep Architecture of LSTD
To achieve the low-shot detection effectively, we first need to alleviate the training difficulties in the detector, when a few training images are available. For this reason, we propose a novel deep detection architecture in Fig. 1, which can take advantage of two state-of-the-art deep detectors, i.e., SSD [Liu et al.2016] and Faster RCNN [Ren et al.2016], to design effective bounding box regression and object classification for low-shot detection.
First, we design bounding box regression in the fashion of SSD. Specifically, for each of selected convolutional layers, there are a number of default candidate boxes (over different ratios and scales) at every spatial location of convolutional feature map. For any candidate box matched with a ground truth object, the regression loss (smooth L1) is used to penalize the offsets (box centers, width and height) error between the predicted and ground truth bounding boxes. As a result, this multiple-convolutional-feature design in SSD is suitable to localize objects with various sizes. This can be especially important for low-shot detection, where we lack training samples with size diversity. More importantly, the regressor in SSD is shared among all object categories, instead of being specific for each category as in Faster RCNN. In this case, the regression parameters of SSD, which are pretrained on the large-scale source domain, can be re-used as initialization in the different low-shot target domain. This avoids re-initializing bounding box regression randomly, and thus reduces the fine-tuning burdens with only a few images in the target domain.
Second, we design object classification in the fashion of Faster RCNN. Specifically, we first address the binary classification task for each default box, to check if a box belongs to an object or not. According to the classification score of each box, we choose object proposals of region proposal network (RPN) in Faster RCNN. Next, we apply the region-of-interests (ROI) pooling layer on a middle-level convolutional layer, which produces a fixed-size convolutional feature cube for each proposal. Finally, instead of using the fully-connected layers in the original Faster RCNN, we use two convolutional layers on top of ROI pooling layer for -object classification. This further reduces overfitting with fewer training parameters. Additionally, the coarse-to-fine classifier may be more effective to alleviate training difficulties of transfer learning, compared to the direct -object classification for each default box in SSD. Our key insight is that, objects in source and target may share some common traits (e.g., clear edge, uniform texture), compared with background. Hence, we propose to transfer this knowledge with the object-or-not classifier, which helps to generate better target-object proposals and thus boost the final performance. On the contrary, the direct classifier has to deal with thousands of randomly-selected proposals.
Summary. Our deep architecture aims at reducing transfer learning difficulties in low-shot detection. To achieve it, we flexibly leverage the core designs of both SSD and Faster RCNN in a non-trivial manner, i.e., the multi-convolutional-layer design for bounding box regression and the coarse-to-fine design for object classification. Additionally, our LSTD performs bounding box regression and object classification on two relatively separate places, which can further decompose the learning difficulties in low-shot detection.
3.2 Regularized Transfer Learning for LSTD
After designing a flexible deep architecture of LSTD, we introduce an end-to-end regularized transfer learning framework for low-shot detection. The whole training procedure is shown in Fig. 2. First, we train LSTD in the source domain, where we apply a large-scale source data set to train LSTD in Fig. 1. Second, we fine-tune the pre-trained LSTD in the target domain, where a novel regularization is proposed to further enhance detection with only a few training images. Specifically, the total loss of fine-tuning can be written as
where the main loss refers to the loss summation of multi-layer bounding box regression and coarse-to-fine object classification in LSTD. Note that, the object categories between source and target can be relevant but different, since low-shot detection aims at detecting the previously-unseen categories from little target data. In this case, the -object classification (i.e., objects + background) has to be randomly re-initialized in the target domain, even though the bounding box regression and object-or-not classification can be initialized from the pre-trained LSTD in the source domain. Consequently, fine-tuning with only may still suffer from the unsatisfactory overfitting. To further enhance low-shot detection in the target domain, we design a novel regularization ,
where and respectively denote the background-depression and transfer-knowledge terms, and are the coefficients for and .
|Tasks||Source (large-scale training set)||Target (1/2/5/10/30 training images per class)|
|Task 1||COCO (standard 80 classes, 118,287 training images)||ImageNet2015 (chosen 50 classes)|
|Task 2||COCO (chosen 60 classes, 98,459 training images)||VOC2007 (standard 20 classes)|
|Task 3||ImageNet2015 (chosen 181 classes, 151,603 training images)||VOC2010 (standard 20 classes)|
Background-Depression (BD) Regularization. In the proposed deep architecture of LSTD, bounding box regression is developed with the multi-convolutional-layer design of SSD. Even though this design can reduce the training difficulties for objects with various sizes, the complex background may still disturb the localization performance in the low-shot scenario. For this reason, we propose a novel background-depression (BD) regularization, by using object knowledge in the target domain (i.e., ground-truth bounding boxes in the training images). Specifically, for a training image in the target domain, we first generate the convolutional feature cube from a middle-level convolutional layer of LSTD. Then, we mask this convolutional cube with the ground-truth bounding boxes of all the objects in the image. Consequently, we can identify the feature regions that are corresponding to image background, namely . To depress the background disturbances, we use L2 regularization to penalize the activation of ,
With this , LSTD can suppress background regions while pay more attention to target objects, which is especially important for training with a few training images. It is clearly shown in Fig. 3 that our BD regularization can help LSTD to reduce the background disturbances.
Transfer-Knowledge (TK) Regularization. The coarse-to-fine classification of LSTD can alleviate the difficulties in object classification, since we can use the pretrained object-or-not classifier in the target domain. However, the -object classifier has to be randomly re-initialized for new objects (plus background) in the target domain, due to the category difference between source and target. In this case, simply fine-tuning this classifier with target data may not make full use of source-domain knowledge. As shown in Fig. 4, the target object Cow (or Aeroplane) is strongly relevant to the source-domain category Bear (or Kite), due to color (or shape) similarity. For this reason, we propose an novel transfer-knowledge (TK) regularization, where the object-label prediction of source network is used as source-domain knowledge to regularize the training of target network for low-shot detection. Note that, object classification in the detection task requires to be applied for each object proposal, instead of the entire image in the standard image classification task. Hence, we design TK regularization for each object proposal in the target domain.
(I) Source-Domain Knowledge
. First, we feed a training image respectively to source-domain and target-domain LSTDs. Then, we apply target-domain proposals into the ROI pooling layer of source-domain LSTD, which can finally generate a knowledge vector from the source-domain object classifier,
where is the pre-softmax activation vector for each object proposal, is a temperature parameter that can produce the soften label with richer label-relation information [Hinton, Vinyals, and Dean2015].
(II) Target-Domain Prediction of Source-Domain Categories. To incorporate the source-domain knowledge into the training procedure of target-domain LSTD, we next modify the target-domain LSTD into a multi-task learning framework. Specifically, we add a source-object soften classifier at the end of target-domain LSTD. For each target proposal, this classifier produces a soften prediction of source object categories,
where is the pre-softmax activation for each proposal.
(III) TK Regularization. With the knowledge of source-domain LSTD and the soften prediction of target-domain LSTD, we apply the cross entropy loss as a TK regularization,
In this case, the source-domain knowledge can be integrated into the training procedure of target domain, which generalizes LSTD for low-shot detection in the target domain.
Summary. To reduce overfitting with few training images, we propose an end-to-end regularized transfer learning framework for LSTD. According to our best knowledge, it is the first transfer learning solution for low-shot detection. The whole training procedure is shown in Alg. 1, where we sufficiently leverage the pretrained source-domain LSTD to generalize the target-domain LSTD. Furthermore, we design a novel regularization (i.e., BD and TK) to effectively enhance fine-tuning with limited target training set.
In this section, we conduct a number of challenging low-shot detection experiments to show the effectiveness of LSTD.
Data Sets. Since our LSTD is a low-shot detector within a regularized transfer learning framework, we adopt a number of detection benchmarks, i.e., COCO [Lin et al.2014], ImageNet2015 [Deng et al.2009], VOC2007 and VOC2010 [Everingham et al.2010], respectively as source and target of three transfer tasks (Table 1). The training set is large-scale in the source domain of each task, while it is low-shot in the target domain (1/2/5/10/30 training images for each target-object class). Furthermore, the object categories for source and target are carefully selected to be non-overlapped, in order to evaluate if our LSTD can detect unseen object categories from few training shots in the target domain. Finally, we use the standard PASCAL VOC detection rule on the test sets to report mean average precision (mAP) with 0.5 intersection-over-union (IOU). Note that, the target domain of task 1 is ImageNet2015 with the chosen 50 object classes. Hence, we define a test set for this target domain, where we randomly sample 100 images in each target-object class of ImageNet2015. To be fair, the training and test images in this target domain are non-overlapped. The target domains of task 2 and 3 refer to the standard VOC2007 and VOC2010. Hence, we use the standard test sets for evaluation.
Implementation Details. Unless stated otherwise, we perform our LSTD as follows.First, the basic deep architecture of LSTD is build upon VGG16 [Simonyan and Zisserman2014], similar to SSD and Faster RCNN. For bounding box regression, we use the same structure in the standard SSD. For object classification, we apply the ROI pooling layer on conv7, and add two convolutional layers (conv12: , conv13: for task 1/2/3) before the -object classifier. Second, we train LSTD in a regularized transfer learning framework (Alg. 1). In the source domain, we feed 32 training images into LSTD for each mini-batch in task 1/2/3, and train bounding box regressor and object-or-not binary classifier in the fashion of SSD [Liu et al.2016]. Subsequently, 100/100/64 proposals (after non-maximum suppression of top 1000 proposals at 0.65) are selected to train the (K+1)-object classifier. In the target domain, all the training settings are the same as the ones in the source domain, except that 64/64/64 proposals are selected to train the (K+1)-object classifier, the background depression regularization is used on conv53, the temperature parameter in the transfer-knowledge regularization is 2 as suggested in [Hinton, Vinyals, and Dean2015]. and the weight coefficients for both background depression and transfer-knowledge are 0.5. Finally, the optimization strategy for both source and target is Adam [Kingma and Ba2015]
, where the initial learning rate is 0.0002 (with 0.1 decay), the momentum/momentum2 is 0.9/0.99, and the weight decay is 0.0001. All our experiments are performed on Caffe[Jia et al.2014].
|Deep Models||Large Source||Low-shot Target|
|Shots for Task 1||1||2||5||10||30|
|Shots for Task 2||1||2||5||10||30|
|Shots for Task 3||1||2||5||10||30|
4.1 Properties of LSTD
To investigate the properties of LSTD, we evaluate the effectiveness of its key designs. To be fair, when we explore different settings for one design, all other designs are with the basic setting in the implementation details.
Basic Deep Structure of LSTD. We first evaluate the basic deep structure of LSTD respectively in the source and target domains, where we compare it with the closely-related SSD [Liu et al.2016] and Faster RCNN [Ren et al.2016]. For fairness, we choose task 1 to show the effectiveness. The main reason is that, the source data in task 1 is the standard COCO detection set, where SSD and Faster RCNN are well-trained with the state-of-the-art performance. Hence, we use the published SSD [Liu et al.2016] and Faster RCNN [Ren et al.2016] in this experiment, where the size of input images for SSD and our LSTD is , and Faster RCNN follows the settings in the original paper. In Table 2, we report mAP on the test sets of both source and target domains in task 1. One can see that, our LSTD achieves a competitive mAP in the source domain. It illustrates that LSTD can be a state-of-art deep detector for large-scale training sets. More importantly, our LSTD outperforms both SSD and Faster RCNN significantly for low-shot detection in the target domain (one training image per target category), where all approaches are simply fine-tuned from their pre-trained models in the source domain. It shows that, LSTD yields a more effective deep architecture for low-shot detection, compared to SSD and Faster RCNN. This can also be found in Fig. 5, when we change the number of training shots in the target domain. Finally, we investigate the structure robustness in LSTD itself. As the bounding box regression follows the standard SSD, we explore the (K+1)-object classifier in which we choose different convolutional layers (conv or conv) for ROI pooling. The results are comparable in Table 2, showing the architecture robustness of LSTD. For consistency, we use conv for ROI pooling in all our experiments.
Regularized Transfer Learning for LSTD. We mainly evaluate if the proposed regularization can enhance transfer learning for LSTD, in order to boost low-shot detection. As shown in Table 3, our background-depression (BD) and transfer knowledge (TK) regularizations can significantly improve the baseline (i.e., fine-tuning), especially when the training set is scarce in the target domain (such as one-shot). Additionally, we show the architecture robustness of BD regularization in Table 4. Specifically, we perform LSTD for one-shot detection in the target domain, where BD regularization is implemented on different convolutional layers for fine-tuning. One can see that BD is generally robust to different convolutional layers. Hence, we apply BD on conv in all our experiments for consistency.
4.2 Comparison with the State-of-the-art
We compare our LSTD to the recent state-of-the-art detection approaches, according to mAP on the test set of target domain. The results are shown in Fig. 5. First, LSTD outperforms SSD [Liu et al.2016] and Faster RCNN [Ren et al.2016], when changing the number of training images in Task 1. It shows the architecture superiority of LSTD for low-shot detection. Second, LSTD outperforms other weakly-supervised [Wang et al.2014, Teh, Rochan, and Wang2016, Kantorov et al.2016, Bilen and Vedaldi2016, Li et al.2016, Diba et al.2016, Cinbis, Verbeek, and Schmid2017] and semi-supervised [Dong et al.2017] detectors, when the number of training shots is beyond two in Task 2 and 3. Note that, we pick the results of these weakly/semi-supervised detectors from the original papers, and compare them with our LSTD on the same test set. It shows that our LSTD is more effective and efficient, as our LSTD only requires a few fully-annotated training images in the target domain. On the contrary, both weakly-supervised and semi-supervised approaches require the full training set (i.e., weakly-supervised: all training images with only image-level labels, semi-supervised: a few fully-annotated training shots + other training images with only image-level labels). In fact, our LSTD outperforms these detectors on task 3 with only 0.4% training data, and can be competitive to fully-supervised detectors (LSTD:69.7, SSD:68.0, Faster RCNN:69.9) with only 11% of training set.
In this section, we qualitatively visualize our LSTD. First, we visualize the detection results in Fig. 6, based on various numbers of training shots in the target domain. As expected, 1-shot LSTD may localize but misclassify some objects (e.g., cows), due to the scarce training set. But this misclassification can be largely clarified with only 5-shot. It illustrates that, our LSTD is an effective and robust deep approach for low-shot detection. Second, we briefly analyze the error mode of LSTD on VOC 2007. For 2-shot LSTD, the error for Animal comes from (23%Loc,71%Sim, 4%BG), where the notations of (Loc, Sim, BG) follow [Girshick et al.2014]. As expected, the main error in low-shot detection may come from confusion with similar objects.
In this paper, we propose a novel low-shot transfer detector (LSTD) to address object detection with a few training images. First, we design a flexible deep architecture of LSTD to reduce transfer difficulties of low-shot detection. Second, we train LSTD within a regularized transfer learning framework, where we introduce a novel low-shot detection regularization (i.e., TK and BD terms) to generalize fine-tuning with a few target images. Finally, our LSTD outperforms other state-of-the-art approaches on a number of challenging experiments, demonstrating that LSTD is a preferable deep detector for low-shot scenarios.
Acknowledgments. This work was supported in part by National Key Research and Development Program of China (2016YFC1400704), National Natural Science Foundation of China (U1613211, 61633021, 61502470), and Shenzhen Research Program (JCYJ20160229193541167, JCYJ20150925163005055).
- [Bilen and Vedaldi2016] Bilen, H., and Vedaldi, A. 2016. Weakly supervised deep detection networks. In CVPR.
- [Cinbis, Verbeek, and Schmid2017] Cinbis, R. G.; Verbeek, J.; and Schmid, C. 2017. Weakly supervised object localization with multi-fold multiple instance learning. IEEE TPAMI.
- [Deng et al.2009] Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In CVPR.
- [Diba et al.2016] Diba, A.; Sharma, V.; Pazandeh, A.; Pirsiavash, H.; and Van Gool, L. 2016. Weakly Supervised Cascaded Convolutional Networks. arXiv:1611.08258.
- [Donahue et al.2014] Donahue, J.; Jia, Y.; Vinyals, O.; Hoffman, J.; Zhang, N.; Tzeng, E.; and Darrell, T. 2014. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML.
- [Dong et al.2017] Dong, X.; Zheng, L.; Ma, F.; Yang, Y.; and Meng, D. 2017. Few-shot Object Detection. arXiv:1706.08249.
- [Everingham et al.2010] Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; and Zisserman, A. 2010. The pascal visual object classes (voc) challenge. IJCV.
- [Fei-Fei, Fergus, and Perona2006] Fei-Fei, L.; Fergus, R.; and Perona, P. 2006. One-shot learning of object categories. IEEE TPAMI.
- [Girshick et al.2014] Girshick, R.; Donahue, J.; Darrell, T.; and Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.
- [Girshick2015] Girshick, R. 2015. Fast R-CNN. In ICCV.
- [Graves, Wayne, and Danihelka2014] Graves, A.; Wayne, G.; and Danihelka, I. 2014. Neural Turing Machines. In arXiv:1410.5401.
- [Hariharan and Girshick2017] Hariharan, B., and Girshick, R. 2017. Low-shot Visual Recognition by Shrinking and Hallucinating Features. In ICCV.
- [He et al.2017] He, K.; Gkioxari, G.; Dollar, P.; and Girshick, R. 2017. Mask R-CNN. arXiv:1703.06870v2.
- [Hinton, Vinyals, and Dean2015] Hinton, G.; Vinyals, O.; and Dean, J. 2015. Distilling the knowledge in a nueal network. In arXiv:1503.02531.
- [Hoffman et al.2014] Hoffman, J.; Guadarrama, S.; Tzeng, E.; Donahue, J.; Girshick, R.; Darrell, T.; and Saenko, K. 2014. LSDA: Large Scale Detection Through Adaptation. In NIPS.
- [Jia et al.2014] Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. In arXiv preprint arXiv:1408.5093.
- [Kantorov et al.2016] Kantorov, V.; Oquab, M.; Cho, M.; and Laptev, I. 2016. Contextlocnet: Context-aware deep network models for weakly supervised localization. In ECCV.
- [Kingma and Ba2015] Kingma, D. P., and Ba, J. L. 2015. Adam: a Method for Stochastic Optimization. In ICLR.
- [Lake, Salakhutdinov, and Tenenbaum2015] Lake, B. M.; Salakhutdinov, R.; and Tenenbaum, J. B. 2015. Human-level concept learning through probabilistic program induction. Science.
- [Li et al.2016] Li, D.; Huang, J.-B.; Li, Y.; Wang, S.; and Yang, M.-H. 2016. Weakly supervised object localization with progressive domain adaptation. In CVPR.
- [Liang et al.2016] Liang, X.; Liu, S.; Wei, Y.; Liu, L.; Lin, L.; and Yan, S. 2016. Towards computational baby learning: A weakly-supervised approach for object detection. In ICCV.
- [Lin et al.2014] Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014. Microsoft coco: Common objects in context. In ECCV.
- [Liu et al.2016] Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.; and Berg, A. C. 2016. Ssd: Single shot multibox detector. In ECCV.
- [Redmon et al.2016] Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016. You only look once: Unified, real-time object detection. In CVPR.
- [Ren et al.2015] Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS.
- [Ren et al.2016] Ren, S.; He, K.; Girshick, R.; and Sun, J. 2016. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE TPAMI.
[Santoro et al.2016]
Santoro, A.; Bartunov, S.; Botvinick, M.; Wierstra, D.; and Lillicrap, T.
One-shot Learning with Memory-Augmented Neural Networks.In ICML.
- [Sharif Razavian et al.2014] Sharif Razavian, A.; Azizpour, H.; Sullivan, J.; and Carlsson, S. 2014. Cnn features off-the-shelf: An astounding baseline for recognition. In CVPR Workshops.
- [Simonyan and Zisserman2014] Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
- [Singh, Xiao, and Lee2016] Singh, K. K.; Xiao, F.; and Lee, Y. J. 2016. Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection. In CVPR.
- [Tang et al.2016] Tang, Y.; Wang, J.; Gao, B.; Dellandréa, E.; Gaizauskas, R.; and Chen, L. 2016. Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer. In CVPR.
- [Tang et al.2017] Tang, P.; Wang, X.; Bai, X.; and Liu, W. 2017. Multiple instance detection network with online instance classifier refinement. In CVPR.
- [Teh, Rochan, and Wang2016] Teh, E. W.; Rochan, M.; and Wang, Y. 2016. Attention Networks for Weakly Supervised Object Localization. In BMVC.
- [Vinyals et al.2016] Vinyals, O.; Blundell, C.; Lillicrap, T.; Kavukcuoglu, K.; and Wierstra, D. 2016. Matching Networks for One Shot Learning. In NIPS.
- [Wang et al.2014] Wang, C.; Ren, W.; Huang, K.; and Tan, T. 2014. Weakly supervised object localization with latent category learning. In ECCV.
- [Xu, Zhu, and Yang2017] Xu, Z.; Zhu, L.; and Yang, Y. 2017. Few-Shot Object Recognition from Machine-Labeled Web Images. In CVPR.
- [Yosinski et al.2014] Yosinski, J.; Clune, J.; Bengio, Y.; and Lipson, H. 2014. How transferable are features in deep neural networks? In NIPS.