Weakly-supervised Discovery of Visual Pattern Configurations
The prominence of weakly labeled data gives rise to a growing demand for object detection methods that can cope with minimal supervision. We propose an approach that automatically identifies discriminative configurations of visual patterns that are characteristic of a given object class. We formulate the problem as a constrained submodular optimization problem and demonstrate the benefits of the discovered configurations in remedying mislocalizations and finding informative positive and negative training examples. Together, these lead to state-of-the-art weakly-supervised detection results on the challenging PASCAL VOC dataset.
The goal of this paper is to learn an object detector that produces tight bounding boxes around objects given training data that contains only weak annotations of presence/absence of objects without bounding box supervision. The paper develops an initialization for the latent SVM detector of Felzenszwalb et al. The paper first obtains many candidate part bounding boxes in all training images using selective search [28]. Then the method proceeds by selecting a set of distinctive object parts that occur (are matched) on the positive images and not on the negative data, but do not significantly overlap each other. This is formulated as a constrained discrete optimization problem and approximately solved using a greedy algorithm. Finally, to better localize the object in the image the paper selects pairs of discriminative parts that tend to co-occurr in a consistent mutual geometric configuration (position, scale and aspect ratio). Bounding boxes obtained from such consistent pairs are then used to train the LSVM object detector.Novelty:The work is somewhat incremental w.r.t. (Song et al., ICML'14 [27]).The main differences are:1.  finding multiple non-overlapping discriminative parts per image. This is in contrast to a single part per image found in [Song et al. ICML'14] and seems as a step in the right direction. 2. finding pairs of parts that occur in a certain configuration that is based on the algorithm of (Li et al., CVPR 2012 [17]) and involves voting in a 4D part-to-part transformation space.Pairs of co-occurring parts are used to better localize the object in the image.While the paper is incremental, the novelty seems sufficient for publication. Experiments:- Results are shown on the Pascal VOC 2007 detection dataset.Results on the test data slightly outperform the method of Song et al. (mAP 24.6 vs. mAP 22.7). The paper also experimentally demonstrates the (slight) improvement of the proposed hard-negative mining procedure that extracts (hard) negative bounding boxes from the positive images.  - The paper also shows qualitative (fig.5) demonstrating the output object localization for several object classes. The qualitative results seem reasonable. Impact:While this is a solid work, given the somewhat incremental nature w.r.t. Song et al. its impact is probably going to be limited. In general, the practical impact of weakly supervised learning methods is going to be determined by the scalabality of the method to large datasets with many images and many classes. What is the complexity of the algorithm? How does the algorithm scale to many images and many labels?  Detailed questions:- L296: It seems that each iteration adds only one additional positive example.How many iterations are performed? How is the algorithm stopped?- The paper claims that the algorithm easily extends beyond pairs of parts to three or more parts. This is not clear to me. The authors should elaborate on this in the rebuttal.- The authors should discuss the scalability of the proposed method to large datasets and many object categories.