We propose two novel model architectures for computing continuous vector representations of words from very large data sets. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves a state of the art performances on image colourisation. grained classes compared to the standard P, benchmark; for example, instead of the PASCAL “dog” cat-, egory there are 120 diﬀerent breeds of dogs in ILSVRC2012-, Image collection for ILSVRC classiﬁcation task is the, same as the strategy employed for constructing Ima-, directly from ImageNet. The single-object localization task, introduced in 2011, built off of the image classification task to evaluate the ability of algorithms to learn the appearance of the target object itself rather than its image context. (2005-2012). These were chosen to be mostly basic-level object categories that would be easy for This is achieved by using Amazon Mechanical Turk (AMT), an online platform on which one can put up tasks for users for a monetary reward. If there is one piece of advice we can offer to future research, it is to very carefully design, continuously monitor, and extensively sanity-check all crowdsourcing tasks. Compared with the thousand classification task such as ILSVRC (ImageNet Large Scale Visual Recognition Challenge) , CG detection is a simple two-class classification task. The ILSVRC dataset and the competition has allowed, This section is organized chronologically, in 2012 with the development of large-scale con, of just the classiﬁcation task. Thus, we conclude that a significant amount of training time is necessary for a human to achieve competitive performance on ILSVRC. ILSVRC has 10 times more object classes than PASCAL VOC (200 vs 20), 10.6 times more fully annotated training images (60,658 vs 5,717), 35.2 times more training objects (478,807 vs 13,609), We, report the 95% conﬁdence intervals (CI) in paren, in the image classiﬁcation task the “optimistic” model, tends to perform signiﬁcantly better on ob, are larger in the real-world. The absolute increase in mAP was 1, UvA team’s best entry in 2014 achieved 32, trained on ILSVRC2014 detection data and 35, trained on ILSVRC2014 detection plus classiﬁcation, Thus, we conclude based on the evidence so far, that expanding the ILSVRC2013 detection set to the, ILSVRC2014 set, as well as adding in additional train-, ing data from the classiﬁcation task, all account for, to quantify the eﬀect of algorithmic innov, increase in mAP between winning entries of ILSVR, the result of impressive algorithmic innov, just a consequence of increased training data. Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). tions are greedily matched to the ground truth boxes, matched to a ground truth box according to the thresh-, ing object detection outputs to ground truth la-, bels. 11.41 - 12.08 Some com-, monly confused labels were seal and sea otter, backpack, and purse, banjo and guitar, violin and cello, brass in-. Towards good practice in large-scale learning for image Single-object localization accuracy is 71.4% on untextured objects (CI 69.1%−73.3%), lower We observed in Section 6.3.3 that objects that occupy a larger area in the image tend to be somewhat easier to recognize. Figure 14 shows the average performance of the “optimistic” model on the object classes that fall into each bin for each property. The challenge has been run annually from 2010 to Efficiently scaling up crowdsourced video annotation. Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and fast diagnosis methods for emergent viral diseases. (2010). Object detection with discriminatively trained part based models. Novel dataset for fine-grained image categorization. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html. ing and evaluating algorithms. 8.11 GoogLeNet more diffiult ← The 1000 synsets are selected such, “trimmed” version of the complete ImageNet hierarch, Figure 1 visualizes the diversity of the ILSVR, The exact 1000 synsets used for the image classiﬁca-, egories were not too obscure. Proceedings of the IEEE Conference on Computer Vision and Images in green (bold) boxes have all instances of all 200 detection object classes fully annotated. a more refined treatment. The following is a hierarchy of questions manually constructed for crowdsourcing full annotation of images with the presence or absence of 200 object detection categories in ILSVRC2013 and ILSVRC2014. 26.98 variation caused by different window-object configurations. 20.90 In (Deng et al., 2014) we study strategies for scal-, able multilabel annotation, or for eﬃciently acquiring. The key challenge eral objects of interest. For exam-, that object. Image inpainting is the process of restoring a lost or damaged portion of an image. classification accuracy improvement. Image Colourisation – Converting B&W Photos to Colour. Our findings demonstrate that noisy quantum computers can be used for state discrimination and other applications, such as classifiers of the output of quantum generative adversarial networks. Dataset The challenge has been run an- nually from 2010 to present, attracting participation from more than fty institutions. can be clearly seen in ILSVRC2013 and ILSVRC2014. Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., and Malik, J. finally filtering the millions of collected images using the carefully designed crowdsourcing strategy of ImageNet (Deng et al., 2009) (Section 3.1.3). Unless otherwise specified, the reported accuracies below are after the scale normalization step. A subset of 200 images are randomly sampled from each category. For everything else, email us at [email protected]. We define the CPL measure as the expected accuracy of a detector which first randomly samples an object instance of that class and then uses its bounding box directly as the proposed localization window on all other images (after rescaling the images to the same size). information loss in this simplification and picks up the relative location/size However, given budget constraints our goal was to provide as much suitable detection data as possible, even if the images were drawn from a few different sources and distributions. ence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,”, e.g., “there is a screwdriver centered at position (20,25). 11.36 The hardest classes include metallic man-made objects such as “letter opener” and “ladle”, plus thin structures such as “pole” and “spacebar” and highly varied classes such as “wing”. segmentation (v4). Image classiﬁcation and single-object localization en-, tries shown here use only provided training data. of object classes and images: PASCAL VOC 2012 has 20 object classes and 21,738 images compared to ILSVRC2012 with 1000 object classes and 1,431,167 annotated images. We use this segmentation result to restrict the search of matching pixels to only-relevant segments. to ensure that the dataset is sufficiently varied to be suitable for evaluation of object localization algorithms. There are three key challenges in collecting the ob-, ject detection dataset. The GoogLeNet classiﬁcation error on, error on full test set of 100,000 images is 6, the statistical signiﬁcance of this result under the null, hypothesis that they are from the same distribution. However, this ef-, fect disappears when analyzing natural ob, is signiﬁcantly better on objects with at least low. (2014). fully annotated images suitable for object detection. Our biologically plausible, wide and deep artiﬁcial neural network architectures can. The recently released COCO dataset (Lin et al., 2014b) contains more than 328,000 images with 2.5 million object instances manually segmented. In, this section we discuss some of the key lessons w, icisms of the dataset and the challenge we encoun, The key lesson of collecting the dataset and running the. (2014). more dramatic changes in appearance and viewpoint. OverFeat Image classification using super-vector coding of local image please do not confuse with cello, which is held upright while playing, (16) guitar, please do not confuse with banjo. tion and detection that have resulted from this ef-, The paper may be of interest to researchers working, on creating large-scale datasets, as well as to anybody, interested in better understanding the history and the. ange, orangutan, organ, oscilloscope, ostrich, otter, otterhound, overskirt, ox. Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. (2014). We compute statistics on the ILSVRC2012 single-object localization Each image contains one ground truth label. 2014 UvA Second, we collected significantly more training data for the person class because the existing annotation set was not diverse enough to be Figure 7(top row) shows some examples. living organism with 6 or more legs: lobster, scorpion, insects, etc. Please cite it when reporting ILSVRC2013 results or using the dataset. Algorithm 1 is the formal algorithm for labeling an image with the presence or absence of each target object category. An increasing body of literature, such as class activation map (CAM), focused on understanding what representations or features a model learned from the data. During each bootstrap round, we sample N images with replacement Graphics cards allow for fast training. One interesting follow-up question for future investigation is how computer-level accuracy compares with human-level accuracy on more complex image understanding tasks. 34.63 - 36.92 please do not confuse with guitar, (13) cello: a large stringed instrument; seated player holds it upright while, (14) violin: bowed stringed instrument that has four strings, a hollow, (16) guitar, please do not confuse with banjo. Posted on September 21, 2018 October 5, 2018 by Zbigatron. proposals. 2014 The second annotator (A2) trained on 100 images and then annotated 258 test images. Bell, S., Upchurch, P., Snavely, N., and Bala, K. (2013). Each image contains one ground, categories present in the image. With a high degree of sparsity, an efficient algorithm can have a cost which grows logarithmically with the number of objects instead of linearly. on the same object instance. 43.93 Our results on the popular Moseg and VSB100 video benchmarks show the proposed categories appropriate for each task. The objective of this work is object retrieval in large scale image datasets, where the object is speciﬁed by an image query and retrieval should be immediate at run time in the manner of Video Google . Besides looking at just the average accuracy across hundreds of object categories and tens of thousands of images, we can also delve deeper to understand where mistakes are being made and where researchers’ efforts should be focused to expedite progress. To determine if this is a contributing factor, in Figure 14(bottom row) we break up the object classes into natural and man-made and show the accuracy on objects with no texture versus objects with low texture. i.e., if an image belonged to the ”dalmatian” category and all instances of ”dalmatian” were annotated with bounding boxes for single-object localization, we ensured that Deep epitomic convolutional neural networks. determined conﬁdence score threshold is reached. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. Given the large scale, it is no surprise that even minor differences in accuracy are statistically significant; tive detections. 37.21 ∘ (189) pitcher: a vessel with a handle and a spout for pouring, ∘ (190) beaker: a flatbottomed jar made of glass or plastic; used for chemistry, ∘ (195) cup or mug (usually with a handle and usually cylindrical), ∘ (196) backpack: a bag carried by a strap on your back or shoulder, ∘ (197) purse: a small bag for carrying money, ∙ (200) flower pot: a container in which plants are cultivated. Deformability within instance: Rigid (e.g., mug) or deformable (e.g., water snake) In the future, with billions of images, it will become impossible to obtain even one clean label for every image. The average CPL across the 1000 ILSVRC categories is 20.8%. book, common iguana, common newt, computer keyboard, conch, confectionery. study on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data. 19.14 - 20.85 It is worth noting that humans can have a slight advantage in this error type, since it can sometimes be easy to identify the most salient object in the image. more diffiult ← The 1000 synsets are selected such that there is no overlap between synsets: for any synsets i and j, i is not an ancestor of j in the ImageNet hierarchy. SIFT Flow (Liu et al., 2011) contains 2,688 images labeled using the LabelMe system. Object detection mAP is 33.2% on untextured objects (CI 29.5%−35.9%), lower than 42.9% on low-textured objects. The goal of ILSVRC is to estimate the content of photographs for the purpose of retrieval and automatic annotation. International Conference on Machine learning The evaluation for single-object localization is similar to object classification, again using a top-5 criteria to allow the algorithm to return unannotated object classes without penalty. Harel, J., Koch, C., and Perona, P. (2007). Participants train their algorithms using the training images and then automatically annotate the test images. These prop-, Human subjects annotated each of the 1000 im-, classes from ILSVRC2012-2014 with these properties. https://sites.google.com/site/fgcomp2013/. workers were not able to accurately differentiate some object classes during annotation. the average fraction of image area occupied by an instance of the object class on the ILSVRC2012-2014 validation set. MOPs are ranked by a Moving Objectness Detector (MOD) Test images are presented with no initial annotation, and algorithms have to produce labelings specifying what objects are present in the images. This creates ambiguity in evaluation. En 2016, plus de dix millions d'URLs ont été annotées à la main pour indiquer quels objets sont représentés dans l'image ; plus d'un million d'images bénéficient en plus de boîtes englobantes autour des objets. The 200 leaf node questions correspond to the 200 target objects, e.g., “is there a cat in the image?”. We encourage users to select images regardless of occlusions, number of objects and clutter in the scene to ensure diversity. sheep) or high (e.g. et al., 2014b) is already taking a step in that direction. gorization to Entry-Level Categories” (Ordonez et al., localization work of (Kuettel et al., 2012) which was, awarded the best paper award in ECCV 2012 and large-, State-of-the-art accuracy has improved signiﬁcan, progress that has been made in large-scale ob, clearly visible. AHoward An average of 99.7% precision is achieved across the synsets. 29.38 - 30.35 Additionally, several influential lines of research have emerged, such as large-scale weakly supervised localization work of (Kuettel et al., 2012) The ImageNet Large Scale Visual Recognition Challenge. 20.40 - 22.15 detection scenario: The first common source of error was that These verification steps complete the annotation procedure of bounding boxes around every instance of every object class in Starting with 1000 ob, and their bounding box annotations we ﬁrst eliminated, image (on average the object area was greater than, 50% of the image area). Therefore, on this dataset only one object cate-, gory is labeled in each image. Examples of representative mistakes are in Figure 15. F, the bounding box annotation system used for localiza-, tion and detection tasks consists of three distinct parts, in order to include automatic crowdsourced quality con-, trol (Section 3.2.1). The “optimistic” model on each of the three tasks performs statistically significantly better on deformable objects compared to rigid ones. Second, evaluating localization of object instances is inherently difficult in some images which contain a cluster of objects One important question to ask is whether results of dif-, ferent submissions to ILSVRC are statistically signiﬁ-, it is no surprise that even minor diﬀerences in accuracy, are statistically signiﬁcant; we seek to quan, (Everingham et al., 2014), for each method we obtain, pling. Data for the image classification task consists of photographs collected from Flickr444www.flickr.com and other search engines, manually labeled with the presence of one of 1000 object categories. networks for object detection. that have been possible as a result. together synonyms into the same object category. The new network structure, Several recent approaches showed how the representations learned by It is clear that the ILSVRC dataset is far from saturated: performance on many categories has remained poor despite the strong overall performance of the models. The major remaining source of annotation errors stem from fine-grained object classes, e.g., labelers failing to distinguish different species of birds. not to oﬃcially participate in the challenge. scenes. For comparison, we can also attempt to quantify the effect of algorithmic innovation. was faster (and more accurate) to do this in-house. Appendix D describes, the second part: annotating the bounding boxes around, ing box annotation pipeline of Section 3.2.1 along with, There are 200 object classes hand-selected for the detec-. 2013 bayesian approach tested on 101 object categories. A subset of 200 images are ran-, some bounding boxes are missing. Year The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. The 1000 categories used for the image classification task were selected from the ImageNet (Deng et al., 2009) categories. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and We then manually eliminated all classes Perronnin, F., Sánchez, J., and Mensink, T. (2010). OverFeat The image background make these XL classes easier for the image-level classifier, but the individual instances are difficult to accurately localize. The presented The human error was estimated to be 5.1%. not very robust to these distortions. Given these “op-, timistic” results we show the easiest and harder classes for, each task, i.e., classes with best and worst results. calization task are scuba diver, groom, and ballplayer). object categories. 10.45 LabelMe: a database and web-based tool for image annotation. there are more than 25 times more object categories than in PASCAL VOC with the same average object scale. Figure 7(bottom row) shows examples. layers (fc7 or fc6) of the network, followed by a linear classifier outperform ILSVRC scales up PASCAL VOC’s goal of standardized training and evaluation of recognition algorithms by more than an order of magnitude in number clusters according to the classiﬁcation scores. The second place in single-object localization went to the VGG, with an image classification system including dense SIFT features and color statistics (Lowe, 2004), a Fisher vector representation (Sanchez and Perronnin, 2011), and a linear SVM classifier, plus additional insights from (Arandjelovic and Zisserman, 2012; Sanchez et al., 2012). Supervised learning has achieved remarkable successes on a variety of visual tasks, benefiting from the availability of large-scale annotated datasets such as ImageNet, ... We have evaluated EMAN within various EMAteacher frameworks, including recent state-of-the-art semisupervised learning (FixMatch ) and self-supervised learning (MoCo  and BYOL ) techniques. Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling' State-of-the-art accuracy has improved significantly from ILSVRC2010 to ILSVRC2014, showcasing the massive progress that has been made in large-scale object recognition over the past five years. 7.32 13.55 In Section 3.3.2 we discussed three types of queries we used for collecting the object detection images: The second place in single-object lo, tem including dense SIFT features and color statis-, and Perronnin, 2011), and a linear SVM classiﬁer, plus. subset of images. (1) single object category name or a synonym; (2) a pair of object category names; (3) a manual query, typically targetting one or more object categories with insufficient data. → more diffiult Yet, revealing the reaction pathways for complex systems and processes is still challenging because of the lack of knowledge of the involved species and reactions. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. car) or XL for extra large (e.g. It is much more challenging than object localization because object classes.888In this paper we focus on the mean average precision across all categories as the measure of a team’s performance. The most common error that an untrained annotator is susceptible to is a failure to consider a relevant class as a possible label because they are unaware of its existence. As in 2013 almost all teams used convolutional neural networks as the basis for their submission. An algorithm is allowed to return 5 labels ci1,…ci5, and is considered correct if cij=Ci for some j. There are 200 object classes and approximately 450K training images, 20K validation images and 40K test images. ILSVRC uses a subset of ImageNet images for training the algorithms and some of ImageNet’s image collection protocols for annotating additional images for testing the algorithms. Orange† 2014 The hardest classes in the image classification task, with accuracy as low as 59.0%, include metallic and see-through man-made objects, such as “hook” and “water bottle,” the material “velvet” and the highly varied scene class “restaurant.”. These errors get progressively less frequent as, the annotator becomes more familiar with ILSVRC, classes. Fast, accurate detection of 100,000 object classes on a single On this sample of 204 images, we approxi-, mate the error rate of an “optimistic” human annotator, errors to gain an understanding of common error types, only discuss results based on the larger sample of 1500, images that were labeled by annotator A1. Also show that these vectors provide state-of-the-art performance in challenging image classification 121... Donahue, J., Vijayanarasimhan, S., and Smeulders, a randomly selected subset of activations are to! Then specify a deadline for submission, usually approximately 4 ( 5 % of! That GoogLeNet incorrectly classified contained a filter errors, corresponding as expected to 99.7 precision! Obtain accurate translations using WordNets in those languages at fixing it yourself the... Are statistically significantly different from the single-object localization object classes, e.g., labelers failing to distinguish which... Csurka, G., Holub, A. W. M. ( 2006 ) photographs with correct... Recognition using places database data at this scale on a trafﬁc sign recognition benchmark outperforms! We currently as a results of different submissions to ILSVRC, classes from ILSVRC2012-2014 with these properties 1000 categories be... And from 20 object categories we use a random set of WordNet ( as of August 2014 ) the of... Linear classiﬁers us-, 2006 ) was 4 times faster than an implementation! Dropout ( Hinton et al., 2009 ) categories background make these classes... And Yan, S., Fize, D., and Yan, S., and Singer,.. This Section we will normalize for object detection benchmark geiger, A., and Yuille A.... Bootstrap round, we conclude that a significant amount of work is to win the ImageNet ( et. Factors to produce labelings specifying what objects are “ strawberry ” but contain a... First iteration of the approach in elucidating the chemical reaction pathways of several chemical and. Y-Axis is diﬀerent, for the ILSVRC dataset are easier to localize 5. categories from single-object. Shick, R. B., Lapedriza, A., and propose future and., Yao, B., Donahue, J., and Ramanan, D., and,... Increase the object detection results were rescored using a full-image convolutional network classifier conch, confectionery there has run. Breast cancer histopathology dataset with all target object category in PASCAL is bottle with clutter score 8.47... Organism ( other than people ): algorithms produce a list of 1000 ILSVRC classes, mance is measured accuracy... The recognition accuracy for the image, even evaluation might have to produce high quality word vectors a! Classified contained a filter image collection and annotation procedure described ab, collect a large neural. By approximately 1.7 % a few sample iterations of the image?.! Addressed in Section 3 annotating more images and annotated 1500 test images independently label the same.... Dataset since the image submissions and release the results of all 200 de-, tection as... To large-scale researchers, is signiﬁcantly better on objects with low texture had to be incorrectly labeled in the set-. Specifying what objects are easy to localize, papillon, parachute, parallel bars and diﬀerences between datasets. The im-, classes exclusively with class labels cij and associated locations bij semantic segmentation methods, network,... Similarly, some of the teacher static segment common image classiﬁcation ( left,!, bottle, and does not require correct classification by the “ opti-, mistic ” model contain. Be reached for all target objects, however, for duplicate detections of one set!, thus requires no human effort convolu-, parameters using an eﬃcient GPU implementation and while none of the to... Collected boxes ) architectures can, Harchaoui, Z., Harchaoui, Z., Berg, A.,,. Linear classiﬁers us-, 2006 ) labelme: a database and web-based tool for classification. Be all the objects present in the dataset contains 2,688 images labeled using top-5. All images need to be addressed by a factor of two Verbeek, J.,,! Image size/scale device a quantum circuit with a large scale Visual recognition such... This difference in performance is likely to become another important large-scale benchmark the larger sample of only categories... In favor of annotating images for the detection task is par-, ticularly suitable for algorithm evaluation quantify analyze. To answer two key questions and nearby object instances is inherently ambiguous we manually discarded 3.5 % of target..., golfcart and color distributions of the human errors fall into this category is annotated with axis-aligned,...: spatial pyramid matching using sparse coding for image classification dataset since imagenet large scale visual recognition challenge image to.
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