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# Object detection system overview. https://i.imgur.com/vd2YUy3.png 1. takes an input image, 2. extracts around 2000 bottom-up region proposals, 3. computes features for each proposal using a large convolutional neural network (CNN), and then 4. classifies each region using class-specific linear SVMs. * R-CNN achieves a mean average precision (mAP) of 53.7% on PASCAL VOC 2010. * On the 200-class ILSVRC2013 detection dataset, R-CNN’s mAP is 31.4%, a large improvement over OverFeat , which had the previous best result at 24.3%. ## There is a 2 challenges faced in object detection 1. localization problem 2. labeling the data 1 localization problem : * One approach frames localization as a regression problem. they report a mAP of 30.5% on VOC 2007 compared to the 58.5% achieved by our method. * An alternative is to build a sliding-window detector. considered adopting a sliding-window approach increases the number of convolutional layers to 5, have very large receptive fields (195 x 195 pixels) and strides (32x32 pixels) in the input image, which makes precise localization within the sliding-window paradigm. 2 labeling the data: * The conventional solution to this problem is to use unsupervised pre-training, followed by supervise fine-tuning * supervised pre-training on a large auxiliary dataset (ILSVRC), followed by domain specific fine-tuning on a small dataset (PASCAL), * fine-tuning for detection improves mAP performance by 8 percentage points. * Stochastic gradient descent via back propagation was used to effective for training convolutional neural networks (CNNs) ## Object detection with R-CNN This system consists of three modules * The first generates category-independent region proposals. These proposals define the set of candidate detections available to our detector. * The second module is a large convolutional neural network that extracts a fixed-length feature vector from each region. * The third module is a set of class specific linear SVMs. Module design 1 Region proposals * which detect mitotic cells by applying a CNN to regularly-spaced square crops. * use selective search method in fast mode (Capture All Scales, Diversification, Fast to Compute). * the time spent computing region proposals and features (13s/image on a GPU or 53s/image on a CPU) 2 Feature extraction. * extract a 4096-dimensional feature vector from each region proposal using the Caffe implementation of the CNN * Features are computed by forward propagating a mean-subtracted 227x227 RGB image through five convolutional layers and two fully connected layers. * warp all pixels in a tight bounding box around it to the required size * The feature matrix is typically 2000x4096 3 Test time detection * At test time, run selective search on the test image to extract around 2000 region proposals (we use selective search’s “fast mode” in all experiments). * warp each proposal and forward propagate it through the CNN in order to compute features. Then, for each class, we score each extracted feature vector using the SVM trained for that class. * Given all scored regions in an image, we apply a greedy non-maximum suppression (for each class independently) that rejects a region if it has an intersection-over union (IoU) overlap with a higher scoring selected region larger than a learned threshold. ## Training 1 Supervised pre-training: * pre-trained the CNN on a large auxiliary dataset (ILSVRC2012 classification) using image-level annotations only (bounding box labels are not available for this data) 2 Domain-specific fine-tuning. * use the stochastic gradient descent (SGD) training of the CNN parameters using only warped region proposals with learning rate of 0.001. 3 Object category classifiers. * use intersection-over union (IoU) overlap threshold method to label a region with The overlap threshold of 0.3. * Once features are extracted and training labels are applied, we optimize one linear SVM per class. * adopt the standard hard negative mining method to fit large training data in memory. ### Results on PASCAL VOC 201012 1 VOC 2010 * compared against four strong baselines including SegDPM, DPM, UVA, Regionlets. * Achieve a large improvement in mAP, from 35.1% to 53.7% mAP, while also being much faster https://i.imgur.com/0dGX9b7.png 2 ILSVRC2013 detection. * ran R-CNN on the 200-class ILSVRC2013 detection dataset * R-CNN achieves a mAP of 31.4% https://i.imgur.com/GFbULx3.png #### Performance layer-by-layer, without fine-tuning 1 pool5 layer * which is the max pooled output of the network’s fifth and final convolutional layer. *The pool5 feature map is 6 x6 x 256 = 9216 dimensional * each pool5 unit has a receptive field of 195x195 pixels in the original 227x227 pixel input 2 Layer fc6 * fully connected to pool5 * it multiplies a 4096x9216 weight matrix by the pool5 feature map (reshaped as a 9216-dimensional vector) and then adds a vector of biases 3 Layer fc7 * It is implemented by multiplying the features computed by fc6 by a 4096 x 4096 weight matrix, and similarly adding a vector of biases and applying half-wave rectification #### Performance layer-by-layer, with fine-tuning * CNN’s parameters fine-tuned on PASCAL. * fine-tuning increases mAP by 8.0 % points to 54.2% ### Network architectures * 16-layer deep network, consisting of 13 layers of 3 _ 3 convolution kernels, with five max pooling layers interspersed, and topped with three fully-connected layers. We refer to this network as “O-Net” for OxfordNet and the baseline as “T-Net” for TorontoNet. * RCNN with O-Net substantially outperforms R-CNN with TNet, increasing mAP from 58.5% to 66.0% * drawback in terms of compute time, with in terms of compute time, with than T-Net. 1 The ILSVRC2013 detection dataset * dataset is split into three sets: train (395,918), val (20,121), and test (40,152) #### CNN features for segmentation. * full R-CNN: The first strategy (full) ignores the re region’s shape and computes CNN features directly on the warped window. Two regions might have very similar bounding boxes while having very little overlap. * fg R-CNN: the second strategy (fg) computes CNN features only on a region’s foreground mask. We replace the background with the mean input so that background regions are zero after mean subtraction. * full+fg R-CNN: The third strategy (full+fg) simply concatenates the full and fg features https://i.imgur.com/n1bhmKo.png
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[code](https://github.com/openai/improved-gan), [demo](http://infinite-chamber-35121.herokuapp.com/cifar-minibatch/1/?), [related](http://www.inference.vc/understanding-minibatch-discrimination-in-gans/) ### Feature matching problem: overtraining on the current discriminator solution: $||E_{x \sim p_{\text{data}}}f(x) - E_{z \sim p_{z}(z)}f(G(z))||_{2}^{2}$ were f(x) activations intermediate layer in discriminator ### Minibatch discrimination problem: generator to collapse to a single point solution: for each sample i, concatenate to $f(x_i)$ features $b$ measuring its distance to other samples j (i and j are both real or generated samples in same batch): $\sum_j \exp(-||M_{i, b} - M_{j, b}||_{L_1})$  this generates visually appealing samples very quickly ### Historical averaging problem: SGD fails by going into extended orbits solution: parameters revert to the mean $|| \theta - \frac{1}{t} \sum_{i=1}^t \theta[i] ||^2$  ### One-sided label smoothing problem: discriminator vulnerability to adversarial examples solution: discriminator target for positive samples is 0.9 instead of 1 ### Virtual batch normalization problem: using BN cause output of examples in batch to be dependent solution: use reference batch chosen once at start of training and each sample is normalized using itself and the reference. It's expensive so used only on generation ### Assessment of image quality problem: MTurk not reliable solution: use inception model p(y|x) to compute $\exp(\mathbb{E}_x \text{KL}(p(y | x) || p(y)))$ on 50K generated images x ### Semi-supervised learning use the discriminator to also classify on K labels when known and use all real samples (labels and unlabeled) in the discrimination task $D(x) = \frac{Z(x)}{Z(x) + 1}, \text{ where } Z(x) = \sum_{k=1}^{K} \exp[l_k(x)]$. In this case use feature matching but not minibatch discrimination. It also improves the quality of generated images.
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Mask RCNN takes off from where Faster RCNN left, with some augmentations aimed at bettering instance segmentation (which was out of scope for FRCNN). Instance segmentation was achieved remarkably well in *DeepMask* , *SharpMask* and later *Feature Pyramid Networks* (FPN). Faster RCNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is most evident in how RoIPool , the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. Mask RCNN fixes that by introducing RoIAlign in place of RoIPool. #### Methodology Mask RCNN retains most of the architecture of Faster RCNN. It adds the a third branch for segmentation. The third branch takes the output from RoIAlign layer and predicts binary class masks for each class. ##### Major Changes and intutions **Mask prediction** Mask prediction segmentation predicts a binary mask for each RoI using fully convolution - and the stark difference being usage of *sigmoid* activation for predicting final mask instead of *softmax*, implies masks don't compete with each other. This *decouples* segmentation from classification. The class prediction branch is used for class prediction and for calculating loss, the mask of predicted loss is used calculating Lmask. Also, they show that a single class agnostic mask prediction works almost as effective as separate mask for each class, thereby supporting their method of decoupling classification from segmentation **RoIAlign** RoIPool first quantizes a floating-number RoI to the discrete granularity of the feature map, this quantized RoI is then subdivided into spatial bins which are themselves quantized, and finally feature values covered by each bin are aggregated (usually by max pooling). Instead of quantization of the RoI boundaries or bin bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and aggregate the result (using max or average). **Backbone architecture** Faster RCNN uses a VGG like structure for extracting features from image, weights of which were shared among RPN and region detection layers. Herein, authors experiment with 2 backbone architectures - ResNet based VGG like in FRCNN and ResNet based [FPN](http://www.shortscience.org/paper?bibtexKey=journals/corr/LinDGHHB16) based. FPN uses convolution feature maps from previous layers and recombining them to produce pyramid of feature maps to be used for prediction instead of single-scale feature layer (final output of conv layer before connecting to fc layers was used in Faster RCNN) **Training Objective** The training objective looks like this ![](https://i.imgur.com/snUq73Q.png) Lmask is the addition from Faster RCNN. The method to calculate was mentioned above #### Observation Mask RCNN performs significantly better than COCO instance segmentation winners *without any bells and whiskers*. Detailed results are available in the paper |
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FaceNet directly maps face images to $\mathbb{R}^{128}$ where distances directly correspond to a measure of face similarity. They use a triplet loss function. The triplet is (face of person A, other face of person A, face of person which is not A). Later, this is called (anchor, positive, negative). The loss function is learned and inspired by LMNN. The idea is to minimize the distance between the two images of the same person and maximize the distance to the other persons image. ## LMNN Large Margin Nearest Neighbor (LMNN) is learning a pseudo-metric $$d(x, y) = (x -y) M (x -y)^T$$ where $M$ is a positive-definite matrix. The only difference between a pseudo-metric and a metric is that $d(x, y) = 0 \Leftrightarrow x = y$ does not hold. ## Curriculum Learning: Triplet selection Show simple examples first, then increase the difficulty. This is done by selecting the triplets. They use the triplets which are *hard*. For the positive example, this means the distance between the anchor and the positive example is high. For the negative example this means the distance between the anchor and the negative example is low. They want to have $$||f(x_i^a) - f(x_i^p)||_2^2 + \alpha < ||f(x_i^a) - f(x_i^n)||_2^2$$ where $\alpha$ is a margin and $x_i^a$ is the anchor, $x_i^p$ is the positive face example and $x_i^n$ is the negative example. They increase $\alpha$ over time. It is crucial that $f$ maps the images not in the complete $\mathbb{R}^{128}$, but on the unit sphere. Otherwise one could double $\alpha$ by simply making $f' = 2 \cdot f$. ## Tasks * **Face verification**: Is this the same person? * **Face recognition**: Who is this person? ## Datasets * 99.63% accuracy on Labeled FAces in the Wild (LFW) * 95.12% accuracy on YouTube Faces DB ## Network Two models are evaluated: The [Zeiler & Fergus model](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13) and an architecture based on the [Inception model](http://www.shortscience.org/paper?bibtexKey=journals/corr/SzegedyLJSRAEVR14). ## See also * [DeepFace](http://www.shortscience.org/paper?bibtexKey=conf/cvpr/TaigmanYRW14#martinthoma) |
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**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixel-wise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction (see [Deep Neural Networks for Object Detection](http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf)). The paper introduces RPNs (Region Proposal Networks). They are end-to-end trained to generate region proposals.They simoultaneously regress region bounds and bjectness scores at each location on a regular grid. RPNs are one type of fully convolutional networks. They take an image of any size as input and output a set of rectangular object proposals, each with an objectness score. ## See also * [R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen) * [Fast R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen) * [Faster R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/nips/RenHGS15#martinthoma) * [Mask R-CNN](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeGDG17) |