Paper:
Deep Neural Networks for Object Detection
Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.

Review:
This paper addresses the object detection problem 
      using deep neural networks (DNNs). Recently DNNs have been successfully 
      applied in image recognition and draw much attention from the vision 
      community. This paper, however, moves a step forward to an arguably more 
      challenging task: object detection. This work formulates detection as a 
      regression problem, and uses DNNs to predict binary object masks directly 
      from image pixels. A post-processing step of multi-scale refinement is 
      used to further improve the detection accuracy. The paper reports 
      comparable results to the state-of-the-art deformable part models (DPMs) 
      on the VOC 2007 benchmark. To my knowledge, this is the first paper that 
      report the detection performance of DNNs on VOC dataset. Pros: 
      1. A novel application of DNNs, and the paper gives an initial answer 
      to an often discussed question: how DNNs work on object detection? 2. 
      Comparable results to the existing state-of-the-arts. Cons: 
      1.Some ad-hoc choices without clear justifications/evaluations, such 
      as - the choices of splitting the boxes into 4 halves. How did that 
      affect the performance compared with using the full box only? Why is this 
      splitting better than other arbitrary splittings (e.g. 3x3 grids), any 
      insights? - the second step refinement. Fig4 shows that the refinement 
      dramatically improves the performance, but refinement step doesn't seem to 
      be fundamental different to the first step of detection. I'm not clear 
      where the boost is from. I don't see any new information is used. One 
      explanation in the paper is that the refinement step provides higher 
      resolution masks thus can detect smaller objects. Can't you just train a 
      single step DNNs with proper parameters so that the resolution is high 
      enough? - The way of producing bounding boxes from the predicted 
      binary mask sounds like a hack. 2. This work uses DNNs to make 
      separate predictions on if each small cell is in an object or on the 
      background. This is similar in spirit to the work of running classifiers 
      on image pixels/superpixels to decide segmentations. (e.g. section 5 of 
      "Semantic Texton Forests for Image Categorization and Segmentation" 
      CVPR08). what do you think will happen if we replace the DNNs with other 
      classifiers (e.g. random forest) on small image patches to generate the 
      binary masks? It would be nice to have comparison with such a baseline. 
      A few questions/suggestions (These are not criticisms): - I'd 
      been curious to see more diagnosis on the error cases. Are they the same 
      mistakes that DPMs make? (which I guess no). - L343 says that you 
      first trained the net for classification to start with good weights and 
      later tuned the higher level layers. In the pre-training step, did you 
      only use VOC2007 data, or extra data were used? - The poor results of 
      sliding window detector (second row in table 1) suggest that DNNs suffer 
      from false positives. Any thoughts on improving its ability to prune the 
      background patches? - For reported results in Table 1, were there any 
      filter/parameter sharing between classes? Typos: L21: extra 
      "few" L205: missing the closing parenthesis. L429: 
      "course-to-file" -> "coarse-to-fine"