We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

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Microsoft COCO: Common Objects in Context

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. 
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For \(300 \times 300\) input, SSD achieves 74.3 % mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for \(512 \times 512\) input, SSD achieves 76.9 % mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https://github.com/weiliu89/caffe/tree/ssd.

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SSD: Single Shot MultiBox Detector

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

1  شناد ی قحت زکرم ،را ی تاق عامتجا لماوع ی ؤم کشزپ مولع هاگشناد ،تملاس رب رث ی ،ناتسرل ،دابآ مرخ ا ی نار . 2  داتسا ی ،را قحت زکرم ی عامتجا لماوع تاق ی ؤم گشناد ،تملاس رب رث کشزپ مولع ها ی ،دابآ مرخ ،ناتسرل ا ی نار . 3  داتسا ی کشزپ هدکشناد ،را ی گشناد ، کشزپ مولع ها ی ،دابآ مرخ ،ناتسرل ا ی نار . 4  برم ی ، قحت زکرم ی عامتجا لماوع تاق ی ؤم کشزپ مولع هاگشناد ،تملاس رب رث ی ،ناتسرل ،دابآ مرخ ا ی نار . 5  مومع کشزپ ی گشناد ، کشزپ مولع ها ی ،دابآ مرخ ،ناتسرل ا ی نار . 6  گشناد ،راتسرپ مولع ها کشزپ ی ،دابآ مرخ ،ناتسرل ا ی نار .

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Mental health of pregnant women referred to Khorramabad health centers

This paper proposes direct learning of image classification from user-supplied tags, without filtering Each tag is supplied by the user who shared the image online Enormous numbers of these tags are freely available online, and they give insight about the image categories important to users and to image classification Our approach is complementary to the conventional approach of manual annotation, which is extremely costly We analyze of the Flickr 100 Million Image dataset, making several useful observations about the statistics of these tags We introduce a large-scale robust classification algorithm, in order to handle the inherent noise in these tags, and a calibration procedure to better predict objective annotations We show that freely available, user-supplied tags can obtain similar or superior results to large databases of costly manual annotations

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Image Classification and Retrieval from User-Supplied Tags.

Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to stay alive, and he will need help in the near future or else he will get washed away. We propose VisualComet, the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present. To support research toward visual commonsense reasoning, we introduce the first large-scale repository of Visual Commonsense Graphs that consists of over 1.4 million textual descriptions of visual commonsense inferences carefully annotated over a diverse set of 60,000 images, each paired with short video summaries of before and after. In addition, we provide person-grounding (i.e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text. We establish strong baseline performances on this task and demonstrate that integration between visual and textual commonsense reasoning is the key and wins over non-integrative alternatives.

Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image.

Mental health status of the nurses working in khorramabad state hospitals

We present a computationally efficient method for compressing a trained neural network without using any data. We break the problem of data-free network compression into independent layer-wise compressions. We show how to efficiently generate layer-wise training data, and how to precondition the network to maintain accuracy during layer-wise compression. Our generic technique can be used with any compression method. We outperform related works for data-free low-bit-width quantization on MobileNetV1, MobileNetV2, and ResNet18. We also demonstrate the efficacy of our layer-wise method when applied to pruning. We outperform baselines in the low-computation regime suitable for on-device edge compression while using orders of magnitude less memory and compute time than comparable generative methods. In the high-computation regime, we show how to combine our method with generative methods to improve upon state-of-the-art performance for several networks.

Ali Farhadi

Papers

Mental health of pregnant women referred to Khorramabad health centers

Image Classification and Retrieval from User-Supplied Tags.

Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image.

Mental health status of the nurses working in khorramabad state hospitals

Layer-Wise Data-Free CNN Compression.