Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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1,900 | 1,900 | ['Junhua Mao', 'Wei Xu', 'Yi Yang', 'Jiang Wang', 'Zhiheng Huang', 'Alan Yuille'] | 1412.6632v5 | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-networ... | Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | 2,014 | http://arxiv.org/pdf/1412.6632v5 | Title Deep Captioning Multimodal Recurrent Neural Networks mRNN Summary paper present multimodal Recurrent Neural Network mRNN model generating novel image caption directly model probability distribution generating word given previous word image Image caption generated sampling distribution model consists two subnetwor... | [0.0793909877538681, 0.04649634659290314, -0.0013939253985881805, 0.04682249575853348, -0.03486587852239609, -0.010868647135794163, 0.010027806274592876, -0.005283457692712545, -0.023180022835731506, -0.05472239479422569, -0.011653653346002102, -0.025220155715942383, -0.008407999761402607, 0.056870460510253906, 0.01624... |
1,901 | 1,901 | ['Angeliki Lazaridou', 'Nghia The Pham', 'Marco Baroni'] | 1501.02598v3 | We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed t... | Combining Language and Vision with a Multimodal Skip-gram Model | 2,015 | http://arxiv.org/pdf/1501.02598v3 | Title Combining Language Vision Multimodal Skipgram Model Summary extend SKIPGRAM model Mikolov et al 2013a taking visual information account Like SKIPGRAM multimodal model MMSKIPGRAM build vectorbased word representation learning predict linguistic context text corpus However restricted set word model also exposed vis... | [0.04908914119005203, 0.01721227541565895, -0.00902736559510231, 0.04834049940109253, -0.02523280307650566, 0.009042426943778992, 0.003951284568756819, 0.041498612612485886, -0.0044868928380310535, -0.07964462041854858, -0.0019926726818084717, -0.01477618794888258, 0.03014116734266281, 0.0454435870051384, 0.05133299902... |
1,902 | 1,902 | ['Junhua Mao', 'Wei Xu', 'Yi Yang', 'Jiang Wang', 'Zhiheng Huang', 'Alan Yuille'] | 1504.06692v2 | In this paper, we address the task of learning novel visual concepts, and
their interactions with other concepts, from a few images with sentence
descriptions. Using linguistic context and visual features, our method is able
to efficiently hypothesize the semantic meaning of new words and add them to
its word dictionar... | Learning like a Child: Fast Novel Visual Concept Learning from Sentence
Descriptions of Images | 2,015 | http://arxiv.org/pdf/1504.06692v2 | Title Learning like Child Fast Novel Visual Concept Learning Sentence Descriptions Images Summary paper address task learning novel visual concept interaction concept image sentence description Using linguistic context visual feature method able efficiently hypothesize semantic meaning new word add word dictionary used... | [0.05880361795425415, 0.012007403187453747, -0.004071745090186596, 0.05339255556464195, -0.028941739350557327, 0.03297742083668709, 0.006328505929559469, -0.022559313103556633, -0.03923584520816803, -0.05413109064102173, -0.009392209351062775, -0.001380954752676189, -0.002745207166299224, 0.05428895726799965, 0.0066782... |
1,903 | 1,903 | ['Samy Bengio', 'Oriol Vinyals', 'Navdeep Jaitly', 'Noam Shazeer'] | 1506.03099v3 | Recurrent Neural Networks can be trained to produce sequences of tokens given
some input, as exemplified by recent results in machine translation and image
captioning. The current approach to training them consists of maximizing the
likelihood of each token in the sequence given the current (recurrent) state
and the pr... | Scheduled Sampling for Sequence Prediction with Recurrent Neural
Networks | 2,015 | http://arxiv.org/pdf/1506.03099v3 | Title Scheduled Sampling Sequence Prediction Recurrent Neural Networks Summary Recurrent Neural Networks trained produce sequence token given input exemplified recent result machine translation image captioning current approach training consists maximizing likelihood token sequence given current recurrent state previou... | [0.04436810314655304, 0.022265905514359474, -0.010869869962334633, 0.017911946401000023, -0.014149378053843975, -0.017918044701218605, 0.01644875481724739, -0.0131453275680542, -0.024018095806241035, -0.03973142057657242, 0.007947895675897598, -0.0200678501278162, 0.013485431671142578, 0.06214265152812004, 0.0204907283... |
1,904 | 1,904 | ['Junhua Mao', 'Jonathan Huang', 'Alexander Toshev', 'Oana Camburu', 'Alan Yuille', 'Kevin Murphy'] | 1511.02283v3 | We propose a method that can generate an unambiguous description (known as a
referring expression) of a specific object or region in an image, and which can
also comprehend or interpret such an expression to infer which object is being
described. We show that our method outperforms previous methods that generate
descri... | Generation and Comprehension of Unambiguous Object Descriptions | 2,015 | http://arxiv.org/pdf/1511.02283v3 | Title Generation Comprehension Unambiguous Object Descriptions Summary propose method generate unambiguous description known referring expression specific object region image also comprehend interpret expression infer object described show method outperforms previous method generate description object without taking ac... | [0.05775875225663185, 0.05294516682624817, 0.018609188497066498, 0.06581497937440872, -0.021189767867326736, 0.005093289073556662, 0.022057348862290382, -0.0020759650506079197, -0.04518275335431099, -0.012719531543552876, 0.002339413622394204, 0.0415329784154892, 0.034309521317481995, 0.033394888043403625, 0.0027420429... |
1,905 | 1,905 | ['Anna Rohrbach', 'Marcus Rohrbach', 'Ronghang Hu', 'Trevor Darrell', 'Bernt Schiele'] | 1511.03745v4 | Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or... | Grounding of Textual Phrases in Images by Reconstruction | 2,015 | http://arxiv.org/pdf/1511.03745v4 | Title Grounding Textual Phrases Images Reconstruction Summary Grounding ie localizing arbitrary freeform textual phrase visual content challenging problem many application humancomputer interaction imagetext reference resolution datasets provide ground truth spatial localization phrase thus desirable learn data little ... | [0.05351989343762398, 0.04404808580875397, 0.03433031961321831, 0.04480961710214615, -0.011151671409606934, -0.012128446251153946, 0.0031569357961416245, 0.0028138908091932535, -0.018939824774861336, -0.056779421865940094, -0.012786462903022766, 0.01619798131287098, 0.04010212793946266, 0.011041603982448578, 0.04487297... |
1,906 | 1,906 | ['Mohamed Elhoseiny', 'Scott Cohen', 'Walter Chang', 'Brian Price', 'Ahmed Elgammal'] | 1511.04891v4 | We study scalable and uniform understanding of facts in images. Existing
visual recognition systems are typically modeled differently for each fact type
such as objects, actions, and interactions. We propose a setting where all
these facts can be modeled simultaneously with a capacity to understand
unbounded number of ... | Sherlock: Scalable Fact Learning in Images | 2,015 | http://arxiv.org/pdf/1511.04891v4 | Title Sherlock Scalable Fact Learning Images Summary study scalable uniform understanding fact image Existing visual recognition system typically modeled differently fact type object action interaction propose setting fact modeled simultaneously capacity understand unbounded number fact structured way training data com... | [-0.012998024001717567, -0.014170507900416851, 0.011001886799931526, 0.08346516638994217, -0.0033055003732442856, 0.03764963150024414, 0.021781960502266884, 0.015552295371890068, 0.002036727499216795, -0.04522998258471489, 0.009102778509259224, -0.001654736464843154, -0.027632640674710274, 0.07406899333000183, -0.01063... |
1,907 | 1,907 | ['Peng Zhang', 'Yash Goyal', 'Douglas Summers-Stay', 'Dhruv Batra', 'Devi Parikh'] | 1511.05099v5 | The complex compositional structure of language makes problems at the
intersection of vision and language challenging. But language also provides a
strong prior that can result in good superficial performance, without the
underlying models truly understanding the visual content. This can hinder
progress in pushing stat... | Yin and Yang: Balancing and Answering Binary Visual Questions | 2,015 | http://arxiv.org/pdf/1511.05099v5 | Title Yin Yang Balancing Answering Binary Visual Questions Summary complex compositional structure language make problem intersection vision language challenging language also provides strong prior result good superficial performance without underlying model truly understanding visual content hinder progress pushing st... | [0.04937983676791191, 0.044918257743120193, -0.027279075235128403, 0.04331352934241295, -0.040603213012218475, 0.005199109204113483, 0.002626160392537713, 0.017412172630429268, -0.011667820625007153, -0.054582539945840836, -0.0035920487716794014, 0.0022545072715729475, 0.010929903015494347, 0.11995977908372879, -0.0081... |
1,908 | 1,908 | ['Hyeonwoo Noh', 'Paul Hongsuck Seo', 'Bohyung Han'] | 1511.05756v1 | We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent uni... | Image Question Answering using Convolutional Neural Network with Dynamic
Parameter Prediction | 2,015 | http://arxiv.org/pdf/1511.05756v1 | Title Image Question Answering using Convolutional Neural Network Dynamic Parameter Prediction Summary tackle image question answering ImageQA problem learning convolutional neural network CNN dynamic parameter layer whose weight determined adaptively based question adaptive parameter prediction employ separate paramet... | [0.051230691373348236, 0.013715321198105812, -0.013175424188375473, 0.056065913289785385, 0.021962478756904602, 0.00792464055120945, 0.01949167624115944, 0.01350838877260685, -0.006171965040266514, -0.010045954957604408, 0.008784470148384571, -0.01721310056746006, -0.03806006908416748, 0.03215565159916878, 0.0292893089... |
1,909 | 1,909 | ['Liwei Wang', 'Yin Li', 'Svetlana Lazebnik'] | 1511.06078v2 | This paper proposes a method for learning joint embeddings of images and text
using a two-branch neural network with multiple layers of linear projections
followed by nonlinearities. The network is trained using a large margin
objective that combines cross-view ranking constraints with within-view
neighborhood structur... | Learning Deep Structure-Preserving Image-Text Embeddings | 2,015 | http://arxiv.org/pdf/1511.06078v2 | Title Learning Deep StructurePreserving ImageText Embeddings Summary paper proposes method learning joint embeddings image text using twobranch neural network multiple layer linear projection followed nonlinearities network trained using large margin objective combine crossview ranking constraint withinview neighborhoo... | [-0.00699905538931489, 0.06008131802082062, 0.019515084102749825, 0.059643302112817764, -0.014183498919010162, 0.011076982133090496, -0.02245064452290535, 0.03937374800443649, 0.03240153193473816, -0.0124816307798028, -0.020078305155038834, -0.02400781214237213, -0.028013093397021294, -0.0005240186583250761, 0.02696070... |
1,910 | 1,910 | ['Ivan Vendrov', 'Ryan Kiros', 'Sanja Fidler', 'Raquel Urtasun'] | 1511.06361v6 | Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning ordered... | Order-Embeddings of Images and Language | 2,015 | http://arxiv.org/pdf/1511.06361v6 | Title OrderEmbeddings Images Language Summary Hypernymy textual entailment image captioning seen special case single visualsemantic hierarchy word sentence image paper advocate explicitly modeling partial order structure hierarchy Towards goal introduce general method learning ordered representation show applied variet... | [0.0598696693778038, 0.050149306654930115, -0.02525370568037033, 0.07718096673488617, 0.009937568567693233, 0.033298347145318985, -0.009070387110114098, 0.017352713271975517, 0.011572662740945816, -0.051530979573726654, 0.011028998531401157, -0.02919090911746025, -0.0012201991630718112, 0.009193511679768562, 0.00341075... |
1,911 | 1,911 | ['Mohamed Elhoseiny', 'Jingen Liu', 'Hui Cheng', 'Harpreet Sawhney', 'Ahmed Elgammal'] | 1512.00818v2 | We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is bu... | Zero-Shot Event Detection by Multimodal Distributional Semantic
Embedding of Videos | 2,015 | http://arxiv.org/pdf/1512.00818v2 | Title ZeroShot Event Detection Multimodal Distributional Semantic Embedding Videos Summary propose new zeroshot Event Detection method Multimodal Distributional Semantic embedding video model embeds object action concept well available modality video distributional semantic space knowledge first ZeroShot event detectio... | [-0.031106218695640564, 0.016432391479611397, 0.01689792051911354, 0.05312442407011986, -0.011434118263423443, -0.0168935377150774, -0.001090610632672906, 0.05972377583384514, -0.04854621738195419, -0.11797219514846802, 0.03344038873910904, 0.03859531879425049, -0.03667730838060379, 0.06557496637105942, 0.0012652415316... |
1,912 | 1,912 | ['Arjun Chandrasekaran', 'Ashwin K. Vijayakumar', 'Stanislaw Antol', 'Mohit Bansal', 'Dhruv Batra', 'C. Lawrence Zitnick', 'Devi Parikh'] | 1512.04407v4 | Humor is an integral part of human lives. Despite being tremendously
impactful, it is perhaps surprising that we do not have a detailed
understanding of humor yet. As interactions between humans and AI systems
increase, it is imperative that these systems are taught to understand
subtleties of human expressions such as... | We Are Humor Beings: Understanding and Predicting Visual Humor | 2,015 | http://arxiv.org/pdf/1512.04407v4 | Title Humor Beings Understanding Predicting Visual Humor Summary Humor integral part human life Despite tremendously impactful perhaps surprising detailed understanding humor yet interaction human AI system increase imperative system taught understand subtlety human expression humor work interested question content sce... | [0.08771549165248871, 0.07180382311344147, -0.03546302393078804, -0.009996481239795685, 0.01573587954044342, 0.04774585738778114, 0.036728233098983765, 0.04824230819940567, 0.04828201234340668, -0.03717821091413498, 0.009566275402903557, 0.03356766700744629, 2.5227589503629133e-05, 0.04144928976893425, 0.02593978121876... |
1,913 | 1,913 | ['Mohamed Elhoseiny', 'Ahmed Elgammal', 'Babak Saleh'] | 1601.00025v2 | People typically learn through exposure to visual concepts associated with
linguistic descriptions. For instance, teaching visual object categories to
children is often accompanied by descriptions in text or speech. In a machine
learning context, these observations motivates us to ask whether this learning
process coul... | Write a Classifier: Predicting Visual Classifiers from Unstructured Text | 2,015 | http://arxiv.org/pdf/1601.00025v2 | Title Write Classifier Predicting Visual Classifiers Unstructured Text Summary People typically learn exposure visual concept associated linguistic description instance teaching visual object category child often accompanied description text speech machine learning context observation motivates u ask whether learning p... | [0.03654329106211662, 0.018298231065273285, -0.03063666634261608, 0.044718410819768906, -0.009789991192519665, 0.016778774559497833, -0.020594799891114235, 0.038557227700948715, 0.013630416244268417, -0.10864344984292984, 0.026198159903287888, 0.0425034761428833, 0.01608813740313053, 0.043444402515888214, 0.00070932914... |
1,914 | 1,914 | ['Afroze Ibrahim Baqapuri'] | 1601.03478v1 | The ability to describe images with natural language sentences is the
hallmark for image and language understanding. Such a system has wide ranging
applications such as annotating images and using natural sentences to search
for images.In this project we focus on the task of bidirectional image
retrieval: such asystem ... | Deep Learning Applied to Image and Text Matching | 2,015 | http://arxiv.org/pdf/1601.03478v1 | Title Deep Learning Applied Image Text Matching Summary ability describe image natural language sentence hallmark image language understanding system wide ranging application annotating image using natural sentence search imagesIn project focus task bidirectional image retrieval asystem capable retrieving image based s... | [0.06130256503820419, 0.03314635902643204, 0.007418754510581493, 0.10536203533411026, -0.0591852180659771, 0.01437277439981699, 0.027494970709085464, 0.021089883521199226, -0.009528565220534801, -0.038062889128923416, -0.020498190075159073, 0.004370720591396093, -0.037772659212350845, 0.05724097788333893, 0.02649604529... |
1,915 | 1,915 | ['Shijian Tang', 'Song Han'] | 1602.01895v1 | Generating natural language descriptions for images is a challenging task.
The traditional way is to use the convolutional neural network (CNN) to extract
image features, followed by recurrent neural network (RNN) to generate
sentences. In this paper, we present a new model that added memory cells to
gate the feeding o... | Generate Image Descriptions based on Deep RNN and Memory Cells for
Images Features | 2,016 | http://arxiv.org/pdf/1602.01895v1 | Title Generate Image Descriptions based Deep RNN Memory Cells Images Features Summary Generating natural language description image challenging task traditional way use convolutional neural network CNN extract image feature followed recurrent neural network RNN generate sentence paper present new model added memory cel... | [0.046134982258081436, 0.0635976567864418, 0.02595936506986618, 0.07111554592847824, -0.04028385132551193, 0.004777907859534025, 0.0203280970454216, -0.035092491656541824, -0.021020811051130295, -0.03534916415810585, -0.01842723973095417, -0.03319365531206131, 0.015593559481203556, 0.061000265181064606, 0.0088247591629... |
1,916 | 1,916 | ['Linlin Chao', 'Jianhua Tao', 'Minghao Yang', 'Ya Li', 'Zhengqi Wen'] | 1603.08321v1 | This paper focuses on two key problems for audio-visual emotion recognition
in the video. One is the audio and visual streams temporal alignment for
feature level fusion. The other one is locating and re-weighting the perception
attentions in the whole audio-visual stream for better recognition. The Long
Short Term Mem... | Audio Visual Emotion Recognition with Temporal Alignment and Perception
Attention | 2,016 | http://arxiv.org/pdf/1603.08321v1 | Title Audio Visual Emotion Recognition Temporal Alignment Perception Attention Summary paper focus two key problem audiovisual emotion recognition video One audio visual stream temporal alignment feature level fusion one locating reweighting perception attention whole audiovisual stream better recognition Long Short Te... | [-0.021154507994651794, -0.05716639384627342, 0.016844959929585457, 0.054869573563337326, 0.03188221529126167, -0.020258570089936256, -0.0015378228854387999, -0.050759173929691315, -0.02952943928539753, -0.0239292923361063, -0.060691773891448975, -0.009757154621183872, 0.008805305697023869, 0.027531523257493973, -0.023... |
1,917 | 1,917 | ['Angeliki Lazaridou', 'Nghia The Pham', 'Marco Baroni'] | 1605.07133v1 | We propose an interactive multimodal framework for language learning. Instead
of being passively exposed to large amounts of natural text, our learners
(implemented as feed-forward neural networks) engage in cooperative referential
games starting from a tabula rasa setup, and thus develop their own language
from the ne... | Towards Multi-Agent Communication-Based Language Learning | 2,016 | http://arxiv.org/pdf/1605.07133v1 | Title Towards MultiAgent CommunicationBased Language Learning Summary propose interactive multimodal framework language learning Instead passively exposed large amount natural text learner implemented feedforward neural network engage cooperative referential game starting tabula rasa setup thus develop language need co... | [0.05331356078386307, 0.010658401064574718, -0.03818010166287422, 0.05020883306860924, -0.017353946343064308, 0.003138753119856119, 0.01735346019268036, -0.02655145898461342, 0.022989239543676376, -0.06079627573490143, -0.037776995450258255, 0.007051250897347927, 0.008732425048947334, 0.041848354041576385, 0.0360692292... |
1,918 | 1,918 | ['Zhilin Yang', 'Ye Yuan', 'Yuexin Wu', 'Ruslan Salakhutdinov', 'William W. Cohen'] | 1605.07912v4 | We propose a novel extension of the encoder-decoder framework, called a
review network. The review network is generic and can enhance any existing
encoder- decoder model: in this paper, we consider RNN decoders with both CNN
and RNN encoders. The review network performs a number of review steps with
attention mechanism... | Review Networks for Caption Generation | 2,016 | http://arxiv.org/pdf/1605.07912v4 | Title Review Networks Caption Generation Summary propose novel extension encoderdecoder framework called review network review network generic enhance existing encoder decoder model paper consider RNN decoder CNN RNN encoders review network performs number review step attention mechanism encoder hidden state output tho... | [0.05868959054350853, 0.0289948508143425, 0.0014745807275176048, 0.03004823997616768, 0.008412117138504982, 0.01886371523141861, -0.0008221014868468046, 0.0062565552070736885, -0.05956875905394554, -0.027924291789531708, -0.011637681163847446, 0.018915768712759018, 0.016632039099931717, 0.08525408804416656, -0.00761369... |
1,919 | 1,919 | ['Nazneen Fatema Rajani', 'Raymond J. Mooney'] | 1605.08764v1 | Ensembling methods are well known for improving prediction accuracy. However,
they are limited in the sense that they cannot discriminate among component
models effectively. In this paper, we propose stacking with auxiliary features
that learns to fuse relevant information from multiple systems to improve
performance. ... | Stacking With Auxiliary Features | 2,016 | http://arxiv.org/pdf/1605.08764v1 | Title Stacking Auxiliary Features Summary Ensembling method well known improving prediction accuracy However limited sense cannot discriminate among component model effectively paper propose stacking auxiliary feature learns fuse relevant information multiple system improve performance Auxiliary feature enable stacker ... | [0.020187320187687874, 0.0305443424731493, 0.004901321139186621, 0.06804125756025314, 0.024295510724186897, 0.055930640548467636, 0.020448604598641396, 0.018606876954436302, -0.010276173241436481, -0.0244930237531662, -0.020152101293206215, -0.021450351923704147, 0.03347921743988991, 0.04576176777482033, 0.011288509704... |
1,920 | 1,920 | ['Chenxi Liu', 'Junhua Mao', 'Fei Sha', 'Alan Yuille'] | 1605.09553v2 | Attention mechanisms have recently been introduced in deep learning for
various tasks in natural language processing and computer vision. But despite
their popularity, the "correctness" of the implicitly-learned attention maps
has only been assessed qualitatively by visualization of several examples. In
this paper we f... | Attention Correctness in Neural Image Captioning | 2,016 | http://arxiv.org/pdf/1605.09553v2 | Title Attention Correctness Neural Image Captioning Summary Attention mechanism recently introduced deep learning various task natural language processing computer vision despite popularity correctness implicitlylearned attention map assessed qualitatively visualization several example paper focus evaluating improving ... | [0.07229209691286087, 0.0192995797842741, -0.009313875809311867, 0.027889646589756012, 0.016291910782456398, 0.008356530219316483, 0.012270179577171803, -0.019264928996562958, 0.019167259335517883, -0.05160246789455414, -0.01764633134007454, -0.011989313177764416, 0.020814115181565285, 0.035044509917497635, 0.034597128... |
1,921 | 1,921 | ['Arijit Ray', 'Gordon Christie', 'Mohit Bansal', 'Dhruv Batra', 'Devi Parikh'] | 1606.06622v3 | Visual Question Answering (VQA) is the task of answering natural-language
questions about images. We introduce the novel problem of determining the
relevance of questions to images in VQA. Current VQA models do not reason about
whether a question is even related to the given image (e.g. What is the capital
of Argentina... | Question Relevance in VQA: Identifying Non-Visual And False-Premise
Questions | 2,016 | http://arxiv.org/pdf/1606.06622v3 | Title Question Relevance VQA Identifying NonVisual FalsePremise Questions Summary Visual Question Answering VQA task answering naturallanguage question image introduce novel problem determining relevance question image VQA Current VQA model reason whether question even related given image eg capital Argentina requires ... | [0.0675911232829094, 0.006879306863993406, -0.032082945108413696, 0.018711306154727936, -0.03669046238064766, 0.022293323650956154, 0.020649926736950874, 0.028358710929751396, 0.018001053482294083, -0.032998085021972656, -0.0037338114343583584, -0.014373973943293095, -0.008558426052331924, 0.04846183955669403, 0.015763... |
1,922 | 1,922 | ['Hao Zhang', 'Zhiting Hu', 'Yuntian Deng', 'Mrinmaya Sachan', 'Zhicheng Yan', 'Eric P. Xing'] | 1606.09239v1 | We study the problem of automatically building hypernym taxonomies from
textual and visual data. Previous works in taxonomy induction generally ignore
the increasingly prominent visual data, which encode important perceptual
semantics. Instead, we propose a probabilistic model for taxonomy induction by
jointly leveragi... | Learning Concept Taxonomies from Multi-modal Data | 2,016 | http://arxiv.org/pdf/1606.09239v1 | Title Learning Concept Taxonomies Multimodal Data Summary study problem automatically building hypernym taxonomy textual visual data Previous work taxonomy induction generally ignore increasingly prominent visual data encode important perceptual semantics Instead propose probabilistic model taxonomy induction jointly l... | [0.029055966064333916, 0.03925388678908348, -0.007833646610379219, 0.07040741294622421, 0.005650457926094532, 0.0314328595995903, -0.014002575539052486, 0.017771225422620773, -0.038794081658124924, -0.09075689315795898, -0.009151303209364414, -0.0007647841703146696, 0.00769038125872612, 0.061802156269550323, 0.02718330... |
1,923 | 1,923 | ['Matthias Plappert', 'Christian Mandery', 'Tamim Asfour'] | 1607.03827v1 | Linking human motion and natural language is of great interest for the
generation of semantic representations of human activities as well as for the
generation of robot activities based on natural language input. However, while
there have been years of research in this area, no standardized and openly
available dataset... | The KIT Motion-Language Dataset | 2,016 | http://arxiv.org/pdf/1607.03827v1 | Title KIT MotionLanguage Dataset Summary Linking human motion natural language great interest generation semantic representation human activity well generation robot activity based natural language input However year research area standardized openly available dataset exists support development evaluation system theref... | [0.016659513115882874, 0.009078282862901688, 0.003103096503764391, 0.00870544370263815, 0.0012981063919141889, 0.03728282079100609, 0.01853986270725727, 0.00388489686883986, -0.018764259293675423, -0.029823871329426765, -0.012982301414012909, 0.0059290057979524136, 0.04428849741816521, 0.012053892016410828, 0.026149356... |
1,924 | 1,924 | ['Yannis M. Assael', 'Brendan Shillingford', 'Shimon Whiteson', 'Nando de Freitas'] | 1611.01599v2 | Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, exi... | LipNet: End-to-End Sentence-level Lipreading | 2,016 | http://arxiv.org/pdf/1611.01599v2 | Title LipNet EndtoEnd Sentencelevel Lipreading Summary Lipreading task decoding text movement speaker mouth Traditional approach separated problem two stage designing learning visual feature prediction recent deep lipreading approach endtoend trainable Wand et al 2016 Chung Zisserman 2016a However existing work model t... | [0.0153832221403718, 0.01479147095233202, 0.02952849678695202, 0.05985181778669357, 0.023813242092728615, 0.0009587219101376832, 0.040750958025455475, -0.009336501359939575, -0.037979017943143845, 0.012710895389318466, -0.043451547622680664, 0.002306808717548847, 0.041850510984659195, 0.03284887224435806, 0.03728131949... |
1,925 | 1,925 | ['Abhinav Thanda', 'Shankar M Venkatesan'] | 1611.02879v1 | In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are ... | Audio Visual Speech Recognition using Deep Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1611.02879v1 | Title Audio Visual Speech Recognition using Deep Recurrent Neural Networks Summary work propose training algorithm audiovisual automatic speech recognition AVASR system using deep recurrent neural network RNNFirst train deep RNN acoustic model Connectionist Temporal Classification CTC objective function frame label obt... | [-0.013347885571420193, -0.004991111345589161, 0.026523463428020477, 0.05694941058754921, 0.029855405911803246, -0.01577630080282688, 0.031258367002010345, -0.02945016697049141, -0.042567070573568344, 0.005845132749527693, -0.08016761392354965, -0.014463221654295921, 0.07093142718076706, 0.04534507915377617, 0.01084045... |
1,926 | 1,926 | ['Jie Mei', 'Aminul Islam', 'Yajing Wu', "Abidalrahman Moh'd", 'Evangelos E. Milios'] | 1611.06950v1 | The accuracy of Optical Character Recognition (OCR) is crucial to the success
of subsequent applications used in text analyzing pipeline. Recent models of
OCR post-processing significantly improve the quality of OCR-generated text,
but are still prone to suggest correction candidates from limited observations
while ins... | Statistical Learning for OCR Text Correction | 2,016 | http://arxiv.org/pdf/1611.06950v1 | Title Statistical Learning OCR Text Correction Summary accuracy Optical Character Recognition OCR crucial success subsequent application used text analyzing pipeline Recent model OCR postprocessing significantly improve quality OCRgenerated text still prone suggest correction candidate limited observation insufficientl... | [0.012530646286904812, 0.016086973249912262, 0.03476725146174431, 0.058697376400232315, -0.05679081752896309, -0.018245326355099678, 0.022005200386047363, 0.06314581632614136, -0.009433713741600513, -0.04421316459774971, 0.029805177822709084, 0.05064011365175247, 0.09690473973751068, 0.02296479046344757, -0.05355256050... |
1,927 | 1,927 | ['Zhe Gan', 'Chuang Gan', 'Xiaodong He', 'Yunchen Pu', 'Kenneth Tran', 'Jianfeng Gao', 'Lawrence Carin', 'Li Deng'] | 1611.08002v2 | A Semantic Compositional Network (SCN) is developed for image captioning, in
which semantic concepts (i.e., tags) are detected from the image, and the
probability of each tag is used to compose the parameters in a long short-term
memory (LSTM) network. The SCN extends each weight matrix of the LSTM to an
ensemble of ta... | Semantic Compositional Networks for Visual Captioning | 2,016 | http://arxiv.org/pdf/1611.08002v2 | Title Semantic Compositional Networks Visual Captioning Summary Semantic Compositional Network SCN developed image captioning semantic concept ie tag detected image probability tag used compose parameter long shortterm memory LSTM network SCN extends weight matrix LSTM ensemble tagdependent weight matrix degree member ... | [0.03757045790553093, 0.007851365022361279, 0.010910444892942905, 0.039530184119939804, -0.010543935000896454, 0.024578021839261055, 0.01089874655008316, -0.003615268040448427, -0.02385123446583748, -0.05445566400885582, 0.032427068799734116, 0.012191839516162872, 0.01608513481914997, 0.06343036890029907, -0.0097362790... |
1,928 | 1,928 | ['Junhua Mao', 'Jiajing Xu', 'Yushi Jing', 'Alan Yuille'] | 1611.08321v1 | In this paper, we focus on training and evaluating effective word embeddings
with both text and visual information. More specifically, we introduce a
large-scale dataset with 300 million sentences describing over 40 million
images crawled and downloaded from publicly available Pins (i.e. an image with
sentence descript... | Training and Evaluating Multimodal Word Embeddings with Large-scale Web
Annotated Images | 2,016 | http://arxiv.org/pdf/1611.08321v1 | Title Training Evaluating Multimodal Word Embeddings Largescale Web Annotated Images Summary paper focus training evaluating effective word embeddings text visual information specifically introduce largescale dataset 300 million sentence describing 40 million image crawled downloaded publicly available Pins ie image se... | [0.06229444220662117, 0.04569285362958908, 0.010145047679543495, 0.08391416817903519, -0.009813059121370316, -0.006584987975656986, 0.006694173440337181, -0.0004635954392142594, -0.022023813799023628, -0.05768460035324097, -0.026711495593190193, 0.014367236755788326, -0.009645265527069569, 0.02174447849392891, 0.027005... |
1,929 | 1,929 | ['Satish Palaniappan', 'Ronojoy Adhikari'] | 1702.00523v1 | Standardized corpora of undeciphered scripts, a necessary starting point for
computational epigraphy, requires laborious human effort for their preparation
from raw archaeological records. Automating this process through machine
learning algorithms can be of significant aid to epigraphical research. Here,
we take the f... | Deep Learning the Indus Script | 2,017 | http://arxiv.org/pdf/1702.00523v1 | Title Deep Learning Indus Script Summary Standardized corpus undeciphered script necessary starting point computational epigraphy requires laborious human effort preparation raw archaeological record Automating process machine learning algorithm significant aid epigraphical research take first step direction present de... | [0.009258639067411423, 0.038935285061597824, -0.03189752623438835, 0.055631041526794434, -0.032869454473257065, 0.025025783106684685, 0.02924126759171486, 0.06124240905046463, 0.032286226749420166, -0.007984030991792679, 0.06261150538921356, 0.014245666563510895, 0.05323003605008125, 0.04172979295253754, 0.020523518323... |
1,930 | 1,930 | ['Yao-Hung Hubert Tsai', 'Liang-Kang Huang', 'Ruslan Salakhutdinov'] | 1703.05908v2 | Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more ro... | Learning Robust Visual-Semantic Embeddings | 2,017 | http://arxiv.org/pdf/1703.05908v2 | Title Learning Robust VisualSemantic Embeddings Summary Many existing method learning joint embedding image text use supervised information paired image textual attribute Taking advantage recent success unsupervised learning deep neural network propose endtoend learning framework able extract robust multimodal represen... | [-0.014256333000957966, 0.029021963477134705, -0.012860733084380627, 0.04951856657862663, -0.00460374029353261, 0.009860038757324219, 0.004748955834656954, 0.0072862827219069, -0.005552264861762524, -0.0640937015414238, -0.0021317435894161463, 0.03527496010065079, 0.011859242804348469, 0.034903738647699356, 0.039595022... |
1,931 | 1,931 | ['Justin Johnson', 'Bharath Hariharan', 'Laurens van der Maaten', 'Judy Hoffman', 'Li Fei-Fei', 'C. Lawrence Zitnick', 'Ross Girshick'] | 1705.03633v1 | Existing methods for visual reasoning attempt to directly map inputs to
outputs using black-box architectures without explicitly modeling the
underlying reasoning processes. As a result, these black-box models often learn
to exploit biases in the data rather than learning to perform visual reasoning.
Inspired by module... | Inferring and Executing Programs for Visual Reasoning | 2,017 | http://arxiv.org/pdf/1705.03633v1 | Title Inferring Executing Programs Visual Reasoning Summary Existing method visual reasoning attempt directly map input output using blackbox architecture without explicitly modeling underlying reasoning process result blackbox model often learn exploit bias data rather learning perform visual reasoning Inspired module... | [-0.005308021791279316, 0.0016802240861579776, -0.042408134788274765, 0.029550572857260704, -0.02508360520005226, 0.0011384106473997235, -0.013676638714969158, 0.018334591761231422, -0.0004429493856150657, -0.028254078701138496, 0.03561275079846382, 0.09080764651298523, 0.010369697585701942, 0.07877660542726517, 0.0440... |
1,932 | 1,932 | ['Karol Kurach', 'Sylvain Gelly', 'Michal Jastrzebski', 'Philip Haeusser', 'Olivier Teytaud', 'Damien Vincent', 'Olivier Bousquet'] | 1705.08386v2 | Generic text embeddings are successfully used in a variety of tasks. However,
they are often learnt by capturing the co-occurrence structure from pure text
corpora, resulting in limitations of their ability to generalize. In this
paper, we explore models that incorporate visual information into the text
representation.... | Better Text Understanding Through Image-To-Text Transfer | 2,017 | http://arxiv.org/pdf/1705.08386v2 | Title Better Text Understanding ImageToText Transfer Summary Generic text embeddings successfully used variety task However often learnt capturing cooccurrence structure pure text corpus resulting limitation ability generalize paper explore model incorporate visual information text representation Based comprehensive ab... | [0.011716960929334164, 0.018181290477514267, -0.004495323169976473, 0.0658583790063858, -0.040386997163295746, 0.015920210629701614, 0.004607372917234898, 0.05799100175499916, 0.0020683908369392157, -0.05438396707177162, -0.023224495351314545, -0.016682453453540802, 0.022892441600561142, 0.07711713761091232, 0.01502747... |
1,933 | 1,933 | ['Serhii Havrylov', 'Ivan Titov'] | 1705.11192v2 | Learning to communicate through interaction, rather than relying on explicit
supervision, is often considered a prerequisite for developing a general AI. We
study a setting where two agents engage in playing a referential game and, from
scratch, develop a communication protocol necessary to succeed in this game.
Unlike... | Emergence of Language with Multi-agent Games: Learning to Communicate
with Sequences of Symbols | 2,017 | http://arxiv.org/pdf/1705.11192v2 | Title Emergence Language Multiagent Games Learning Communicate Sequences Symbols Summary Learning communicate interaction rather relying explicit supervision often considered prerequisite developing general AI study setting two agent engage playing referential game scratch develop communication protocol necessary succe... | [0.05379199609160423, 0.017209434881806374, -0.034755539149045944, 0.010731332935392857, -0.06160767003893852, -0.00632530776783824, -0.0028862354811280966, 0.01890427991747856, -0.013685098849236965, -0.016145281493663788, 0.024173203855752945, -0.003146185539662838, 0.04240715131163597, 0.07258296757936478, 0.0240452... |
1,934 | 1,934 | ['Harm de Vries', 'Florian Strub', 'Jérémie Mary', 'Hugo Larochelle', 'Olivier Pietquin', 'Aaron Courville'] | 1707.00683v3 | It is commonly assumed that language refers to high-level visual concepts
while leaving low-level visual processing unaffected. This view dominates the
current literature in computational models for language-vision tasks, where
visual and linguistic input are mostly processed independently before being
fused into a sin... | Modulating early visual processing by language | 2,017 | http://arxiv.org/pdf/1707.00683v3 | Title Modulating early visual processing language Summary commonly assumed language refers highlevel visual concept leaving lowlevel visual processing unaffected view dominates current literature computational model languagevision task visual linguistic input mostly processed independently fused single representation p... | [0.02513597160577774, 0.031430646777153015, -0.028609156608581543, 0.02403467521071434, 0.01029114332050085, -0.003399238456040621, -0.00980179850012064, 0.016650982201099396, -0.03707151114940643, -0.04293356463313103, -0.008771526627242565, 0.0042634685523808, 0.0008119286503642797, 0.04725205898284912, 0.03695069998... |
1,935 | 1,935 | ['Fartash Faghri', 'David J. Fleet', 'Jamie Ryan Kiros', 'Sanja Fidler'] | 1707.05612v2 | We present a new technique for learning visual-semantic embeddings for
cross-modal retrieval. Inspired by the use of hard negatives in structured
prediction, and ranking loss functions used in retrieval, we introduce a simple
change to common loss functions used to learn multi-modal embeddings. That,
combined with fine... | VSE++: Improving Visual-Semantic Embeddings with Hard Negatives | 2,017 | http://arxiv.org/pdf/1707.05612v2 | Title VSE Improving VisualSemantic Embeddings Hard Negatives Summary present new technique learning visualsemantic embeddings crossmodal retrieval Inspired use hard negative structured prediction ranking loss function used retrieval introduce simple change common loss function used learn multimodal embeddings combined ... | [0.0036112479865550995, 0.020161984488368034, -0.006348464637994766, 0.044735971838235855, 0.01292404718697071, 0.003998636733740568, -0.02723362296819687, 0.0354233980178833, 0.010819043032824993, -0.04138565808534622, -0.037703365087509155, 0.005576586816459894, -0.026687005534768105, 0.05209432542324066, 0.032528061... |
1,936 | 1,936 | ['Chuang Gan', 'Yandong Li', 'Haoxiang Li', 'Chen Sun', 'Boqing Gong'] | 1708.04686v1 | Rich and dense human labeled datasets are among the main enabling factors for
the recent advance on vision-language understanding. Many seemingly distant
annotations (e.g., semantic segmentation and visual question answering (VQA))
are inherently connected in that they reveal different levels and perspectives
of human ... | VQS: Linking Segmentations to Questions and Answers for Supervised
Attention in VQA and Question-Focused Semantic Segmentation | 2,017 | http://arxiv.org/pdf/1708.04686v1 | Title VQS Linking Segmentations Questions Answers Supervised Attention VQA QuestionFocused Semantic Segmentation Summary Rich dense human labeled datasets among main enabling factor recent advance visionlanguage understanding Many seemingly distant annotation eg semantic segmentation visual question answering VQA inher... | [0.046043314039707184, 0.009113709442317486, -0.013996644876897335, 0.07710210978984833, 0.004497261252254248, 0.019251450896263123, 0.032988935708999634, -0.013253811746835709, 0.0027865376323461533, -0.0444481261074543, -0.009124350734055042, 0.016219450160861015, -0.00499297771602869, 0.018901048228144646, 0.0361367... |
1,937 | 1,937 | ['Yang Xian', 'Yingli Tian'] | 1709.05038v1 | In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning
model is proposed to handle uncontrolled imbalanced real-world image-sentence
dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306,165
images and the original text descriptions uploaded by the users are utilized as
the ground ... | Self-Guiding Multimodal LSTM - when we do not have a perfect training
dataset for image captioning | 2,017 | http://arxiv.org/pdf/1709.05038v1 | Title SelfGuiding Multimodal LSTM perfect training dataset image captioning Summary paper selfguiding multimodal LSTM sgLSTM image captioning model proposed handle uncontrolled imbalanced realworld imagesentence dataset collect FlickrNYC dataset Flickr testbed 306165 image original text description uploaded user utiliz... | [0.0609104186296463, 0.017605571076273918, 0.020481960847973824, 0.05718410015106201, -0.022822491824626923, 0.01052162516862154, 0.027681224048137665, -0.011246905662119389, -0.018151406198740005, -0.08095657080411911, -0.043236251920461655, -0.014410714618861675, -0.007032691966742277, 0.04246456176042557, 0.00617270... |
1,938 | 1,938 | ['Xu Sun', 'Bingzhen Wei', 'Xuancheng Ren', 'Shuming Ma'] | 1710.10393v1 | We propose a method, called Label Embedding Network, which can learn label
representation (label embedding) during the training process of deep networks.
With the proposed method, the label embedding is adaptively and automatically
learned through back propagation. The original one-hot represented loss
function is conv... | Label Embedding Network: Learning Label Representation for Soft Training
of Deep Networks | 2,017 | http://arxiv.org/pdf/1710.10393v1 | Title Label Embedding Network Learning Label Representation Soft Training Deep Networks Summary propose method called Label Embedding Network learn label representation label embedding training process deep network proposed method label embedding adaptively automatically learned back propagation original onehot represe... | [-0.0041319807060062885, 0.013407545164227486, -0.005647705867886543, 0.05988280847668648, 0.018408987671136856, -0.006726698484271765, 0.03320416063070297, -0.015212559141218662, 0.007989793084561825, -0.012567042373120785, -0.010755392722785473, 0.04677826166152954, -0.032197754830121994, 0.05329598858952522, 0.02842... |
1,939 | 1,939 | ['Jianbo Chen', 'Yelong Shen', 'Jianfeng Gao', 'Jingjing Liu', 'Xiaodong Liu'] | 1711.06288v1 | We investigate the problem of Language-Based Image Editing (LBIE) in this
work. Given a source image and a natural language description, we want to
generate a target image by editing the source im- age based on the description.
We propose a generic modeling framework for two sub-tasks of LBIE:
language-based image segm... | Language-Based Image Editing with Recurrent Attentive Models | 2,017 | http://arxiv.org/pdf/1711.06288v1 | Title LanguageBased Image Editing Recurrent Attentive Models Summary investigate problem LanguageBased Image Editing LBIE work Given source image natural language description want generate target image editing source im age based description propose generic modeling framework two subtasks LBIE languagebased image segme... | [0.054590508341789246, 0.015464499592781067, 0.018978577107191086, 0.07335062325000763, -0.009570534341037273, -0.0005884341662749648, 0.037497133016586304, 0.007010838016867638, -0.06078018248081207, -0.05507772043347359, -0.02914297766983509, -0.05051392689347267, 0.019017856568098068, 0.05596606805920601, 0.01104517... |
1,940 | 1,940 | ['Ishan Misra', 'Ross Girshick', 'Rob Fergus', 'Martial Hebert', 'Abhinav Gupta', 'Laurens van der Maaten'] | 1712.01238v1 | We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and t... | Learning by Asking Questions | 2,017 | http://arxiv.org/pdf/1712.01238v1 | Title Learning Asking Questions Summary introduce interactive learning framework development testing intelligent visual system called learningbyasking LBA explore LBA context Visual Question Answering VQA task LBA differs standard VQA training question observed training time learner must ask question want answer Thus L... | [0.04647989198565483, -0.03832748532295227, -0.0320994071662426, 0.0533619225025177, -0.022607797756791115, 0.042575374245643616, -0.013398385606706142, 0.027958247810602188, 0.03898663446307182, -0.021292632445693016, 0.004242136143147945, 0.03137976676225662, -0.043840523809194565, 0.04898945614695549, 0.040218207985... |
1,941 | 1,941 | ['Kenneth Leidal', 'David Harwath', 'James Glass'] | 1712.03897v1 | In this paper, we explore the unsupervised learning of a semantic embedding
space for co-occurring sensory inputs. Specifically, we focus on the task of
learning a semantic vector space for both spoken and handwritten digits using
the TIDIGITs and MNIST datasets. Current techniques encode image and
audio/textual inputs... | Learning Modality-Invariant Representations for Speech and Images | 2,017 | http://arxiv.org/pdf/1712.03897v1 | Title Learning ModalityInvariant Representations Speech Images Summary paper explore unsupervised learning semantic embedding space cooccurring sensory input Specifically focus task learning semantic vector space spoken handwritten digit using TIDIGITs MNIST datasets Current technique encode image audiotextual input di... | [-0.03603636100888252, 0.047251325100660324, -0.011598740704357624, 0.05210009217262268, -0.013642596080899239, 0.008318580687046051, 0.03387545049190521, -0.002447296865284443, -0.06035871058702469, -0.04861799627542496, -0.07990363240242004, 0.02827667072415352, 0.004196727182716131, 0.044094718992710114, 0.032089594... |
1,942 | 1,942 | ['Jason Xie', 'Tingwen Bao'] | 1712.06682v1 | Generating novel pairs of image and text is a problem that combines computer
vision and natural language processing. In this paper, we present strategies
for generating novel image and caption pairs based on existing captioning
datasets. The model takes advantage of recent advances in generative
adversarial networks an... | Synthesizing Novel Pairs of Image and Text | 2,017 | http://arxiv.org/pdf/1712.06682v1 | Title Synthesizing Novel Pairs Image Text Summary Generating novel pair image text problem combine computer vision natural language processing paper present strategy generating novel image caption pair based existing captioning datasets model take advantage recent advance generative adversarial network sequencetosequen... | [0.049424827098846436, 0.059592943638563156, -0.008910011500120163, 0.05295021831989288, -0.02145352028310299, -0.007907837629318237, 0.025088030844926834, -0.00743473507463932, 0.029772890731692314, -0.02822738327085972, -0.010431253351271152, -0.039709482342004776, 0.006968350615352392, 0.09035183489322662, 0.0197790... |
1,943 | 1,943 | ['Pelin Dogan', 'Boyang Li', 'Leonid Sigal', 'Markus Gross'] | 1803.00057v1 | The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for such alignment,
including Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs),
suffer from inherent drawbacks. Mainly, the Markov assumption implies that,
given the immediate past,... | LSTM stack-based Neural Multi-sequence Alignment TeCHnique (NeuMATCH) | 2,018 | http://arxiv.org/pdf/1803.00057v1 | Title LSTM stackbased Neural Multisequence Alignment TeCHnique NeuMATCH Summary alignment heterogeneous sequential data video text important challenging problem Standard technique alignment including Dynamic Time Warping DTW Conditional Random Fields CRFs suffer inherent drawback Mainly Markov assumption implies given ... | [-0.003863204037770629, 0.06979136914014816, 0.019424989819526672, 0.017292406409978867, -0.026835227385163307, -0.0004638778336811811, 0.0285099558532238, 0.01350557804107666, -0.02321733348071575, 0.015686403959989548, -0.004273180849850178, -0.05907075107097626, 0.05682188272476196, -0.0010819692397490144, 0.0119762... |
1,944 | 1,944 | ['Amos Storkey'] | 1106.4509v1 | Prediction markets show considerable promise for developing flexible
mechanisms for machine learning. Here, machine learning markets for
multivariate systems are defined, and a utility-based framework is established
for their analysis. This differs from the usual approach of defining static
betting functions. It is sho... | Machine Learning Markets | 2,011 | http://arxiv.org/pdf/1106.4509v1 | Title Machine Learning Markets Summary Prediction market show considerable promise developing flexible mechanism machine learning machine learning market multivariate system defined utilitybased framework established analysis differs usual approach defining static betting function shown market implement model combinati... | [0.016158202663064003, 0.021132757887244225, -0.05616506189107895, -0.07620953768491745, -0.0015008344780653715, -0.016355184838175774, 0.08429225534200668, -0.005561568774282932, 0.0011157892877236009, -0.0017223723698407412, 0.04048127681016922, 0.04981696978211403, -0.013685200363397598, 0.13280357420444489, 0.04888... |
1,945 | 1,945 | ['M. Sanz', 'L. Lamata', 'E. Solano'] | 1709.07808v1 | We propose a method to build quantum memristors in quantum photonic
platforms. We firstly design an effective beam splitter, which is tunable in
real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam
splitters and a tunable retarder, which allows us to control its reflectivity.
Then, we show that th... | Quantum Memristors in Quantum Photonics | 2,017 | http://arxiv.org/pdf/1709.07808v1 | Title Quantum Memristors Quantum Photonics Summary propose method build quantum memristors quantum photonic platform firstly design effective beam splitter tunable realtime mean MachZehndertype array two equal 5050 beam splitter tunable retarder allows u control reflectivity show tunable beam splitter equipped weak mea... | [-0.04002555459737778, 0.0012817558599635959, -0.05555417016148567, 0.055808279663324356, -0.030166607350111008, -0.004903675988316536, -0.02860843949019909, -0.016976123675704002, 0.03496330976486206, -0.01829426921904087, -0.04458516836166382, -0.015128526836633682, -0.061451323330402374, 0.08596628904342651, 0.02585... |
1,946 | 1,946 | ['Lukas Cavigelli', 'Dominic Bernath', 'Michele Magno', 'Luca Benini'] | 1611.03130v1 | Detecting and classifying targets in video streams from surveillance cameras
is a cumbersome, error-prone and expensive task. Often, the incurred costs are
prohibitive for real-time monitoring. This leads to data being stored locally
or transmitted to a central storage site for post-incident examination. The
required c... | Computationally Efficient Target Classification in Multispectral Image
Data with Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1611.03130v1 | Title Computationally Efficient Target Classification Multispectral Image Data Deep Neural Networks Summary Detecting classifying target video stream surveillance camera cumbersome errorprone expensive task Often incurred cost prohibitive realtime monitoring lead data stored locally transmitted central storage site pos... | [0.006198809016495943, 0.03297511488199234, 0.012050468474626541, 0.03435709327459335, -0.017081232741475105, -0.03242902457714081, 0.07603206485509872, -0.04760618880391121, -0.05787106230854988, -0.0533931627869606, 0.02650459297001362, 0.030627934262156487, 0.026134224608540535, 0.06805604696273804, 0.01051885914057... |
1,947 | 1,947 | ['Arunkumar Byravan', 'Felix Leeb', 'Franziska Meier', 'Dieter Fox'] | 1710.00489v1 | In this work, we present an approach to deep visuomotor control using
structured deep dynamics models. Our deep dynamics model, a variant of
SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an
encoder-decoder structure. Unlike prior work, our dynamics model is structured:
given an input scen... | SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning
and Control | 2,017 | http://arxiv.org/pdf/1710.00489v1 | Title SE3PoseNets Structured Deep Dynamics Models Visuomotor Planning Control Summary work present approach deep visuomotor control using structured deep dynamic model deep dynamic model variant SE3Nets learns lowdimensional pose embedding visuomotor control via encoderdecoder structure Unlike prior work dynamic model ... | [-0.014479981735348701, -0.029330018907785416, 0.009508280083537102, 0.013032814487814903, 0.014198262244462967, 0.007458385545760393, -0.007270668633282185, -0.026687394827604294, 0.00043656857451424, -0.024139072746038437, -0.035000935196876526, 0.01515317615121603, -0.01362442597746849, 0.07919039577245712, 0.024073... |
1,948 | 1,948 | ['Wellington Pinheiro dos Santos', 'Francisco Marcos de Assis', 'Ricardo Emmanuel de Souza', 'Priscilla B. Mendes', 'Henrique S. S. Monteiro', 'Havana Diogo Alves'] | 1712.01694v1 | The materialist dialectical method is a philosophical investigative method to
analyze aspects of reality. These aspects are viewed as complex processes
composed by basic units named poles, which interact with each other. Dialectics
has experienced considerable progress in the 19th century, with Hegel's
dialectics and, ... | Fuzzy-Based Dialectical Non-Supervised Image Classification and
Clustering | 2,017 | http://arxiv.org/pdf/1712.01694v1 | Title FuzzyBased Dialectical NonSupervised Image Classification Clustering Summary materialist dialectical method philosophical investigative method analyze aspect reality aspect viewed complex process composed basic unit named pole interact Dialectics experienced considerable progress 19th century Hegels dialectic 20t... | [-0.009561926126480103, 0.06413190066814423, -0.03887389227747917, 0.023286066949367523, 0.024578867480158806, 0.028884541243314743, 0.02170419879257679, 0.07942850142717361, 0.008286935277283192, -0.025443218648433685, 0.03229217603802681, 0.012474185787141323, 0.008599602617323399, -0.03127027675509453, -0.0143457371... |
1,949 | 1,949 | ['Mohammadreza Zolfaghari', 'Gabriel L. Oliveira', 'Nima Sedaghat', 'Thomas Brox'] | 1704.00616v2 | General human action recognition requires understanding of various visual
cues. In this paper, we propose a network architecture that computes and
integrates the most important visual cues for action recognition: pose, motion,
and the raw images. For the integration, we introduce a Markov chain model
which adds cues su... | Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance
for Action Classification and Detection | 2,017 | http://arxiv.org/pdf/1704.00616v2 | Title Chained Multistream Networks Exploiting Pose Motion Appearance Action Classification Detection Summary General human action recognition requires understanding various visual cue paper propose network architecture computes integrates important visual cue action recognition pose motion raw image integration introdu... | [-0.028757261112332344, 0.012512513436377048, -0.01760033331811428, 0.03246408700942993, 0.002898276085034013, 0.020851563662290573, 0.04290839284658432, -0.006174824200570583, 0.013563087210059166, -0.0508565753698349, -0.01604834385216236, -0.04678159952163696, 0.0011554558295756578, 0.05488160252571106, 0.0293436199... |
1,950 | 1,950 | ['Filipe Rolim Cordeiro', 'Wellington Pinheiro dos Santos', 'Abel Guilhermino da Silva Filho'] | 1801.01443v1 | According to the World Health Organization, breast cancer is the most common
form of cancer in women. It is the second leading cause of death among women
round the world, becoming the most fatal form of cancer. Mammographic image
segmentation is a fundamental task to support image analysis and diagnosis,
taking into ac... | A semi-supervised fuzzy GrowCut algorithm to segment and classify
regions of interest of mammographic images | 2,017 | http://arxiv.org/pdf/1801.01443v1 | Title semisupervised fuzzy GrowCut algorithm segment classify region interest mammographic image Summary According World Health Organization breast cancer common form cancer woman second leading cause death among woman round world becoming fatal form cancer Mammographic image segmentation fundamental task support image... | [0.02392052672803402, 0.01936122216284275, -0.030704475939273834, -0.0041809831745922565, -0.013607854954898357, 0.031180711463093758, 0.01179508212953806, 0.014250610023736954, 0.0024159757886081934, 0.018662793561816216, 0.05406421795487404, -0.0059408457018435, 0.019317612051963806, 0.05267723277211189, 0.0009894749... |
1,951 | 1,951 | ['Peng Sun', 'Mark D. Reid', 'Jie Zhou'] | 1110.3907v3 | This paper presents an improvement to model learning when using multi-class
LogitBoost for classification. Motivated by the statistical view, LogitBoost
can be seen as additive tree regression. Two important factors in this setting
are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the
dense Hess... | AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem | 2,011 | http://arxiv.org/pdf/1110.3907v3 | Title AOSOLogitBoost Adaptive OneVsOne LogitBoost MultiClass Problem Summary paper present improvement model learning using multiclass LogitBoost classification Motivated statistical view LogitBoost seen additive tree regression Two important factor setting 1 coupled classifier output due sumtozero constraint 2 dense H... | [-0.03296606242656708, 0.05653146654367447, -0.006270922254770994, -0.019986161962151527, 0.03531356528401375, 0.009633042849600315, 0.007502211257815361, 0.024965625256299973, -0.013554667122662067, -0.028252027928829193, 0.011459332890808582, -0.01497524231672287, 0.020197397097945213, 0.014676141552627087, -0.036190... |
1,952 | 1,952 | ['Andreas C. Damianou', 'Michalis K. Titsias', 'Neil D. Lawrence'] | 1107.4985v1 | High dimensional time series are endemic in applications of machine learning
such as robotics (sensor data), computational biology (gene expression data),
vision (video sequences) and graphics (motion capture data). Practical
nonlinear probabilistic approaches to this data are required. In this paper we
introduce the v... | Variational Gaussian Process Dynamical Systems | 2,011 | http://arxiv.org/pdf/1107.4985v1 | Title Variational Gaussian Process Dynamical Systems Summary High dimensional time series endemic application machine learning robotics sensor data computational biology gene expression data vision video sequence graphic motion capture data Practical nonlinear probabilistic approach data required paper introduce variat... | [-0.03083920292556286, 0.06631612777709961, -0.031006187200546265, -0.0202065147459507, 0.0013108662096783519, 0.01850186660885811, 0.005673545878380537, -0.003497848054394126, -0.06549949198961258, 0.019826626405119896, 0.06181999295949936, -0.02682526223361492, 0.02342562936246395, 0.10817407071590424, 0.010855323635... |
1,953 | 1,953 | ['Khoat Than', 'Tu Bao Ho'] | 1210.7053v2 | Inference is an integral part of probabilistic topic models, but is often
non-trivial to derive an efficient algorithm for a specific model. It is even
much more challenging when we want to find a fast inference algorithm which
always yields sparse latent representations of documents. In this article, we
introduce a si... | Managing sparsity, time, and quality of inference in topic models | 2,012 | http://arxiv.org/pdf/1210.7053v2 | Title Managing sparsity time quality inference topic model Summary Inference integral part probabilistic topic model often nontrivial derive efficient algorithm specific model even much challenging want find fast inference algorithm always yield sparse latent representation document article introduce simple framework i... | [0.026217272505164146, 0.04010126367211342, -0.0006178987096063793, 0.023340871557593346, -0.03273701295256615, -0.02975769340991974, 0.011913889087736607, 0.0020416956394910812, -0.07666625082492828, -0.045265667140483856, 0.03134548291563988, 0.018887151032686234, 0.016947319731116295, 0.03883935511112213, 0.00902461... |
1,954 | 1,954 | ['Dinesh Jayaraman', 'Kristen Grauman'] | 1505.02206v2 | Understanding how images of objects and scenes behave in response to specific
ego-motions is a crucial aspect of proper visual development, yet existing
visual learning methods are conspicuously disconnected from the physical source
of their images. We propose to exploit proprioceptive motor signals to provide
unsuperv... | Learning image representations tied to ego-motion | 2,015 | http://arxiv.org/pdf/1505.02206v2 | Title Learning image representation tied egomotion Summary Understanding image object scene behave response specific egomotions crucial aspect proper visual development yet existing visual learning method conspicuously disconnected physical source image propose exploit proprioceptive motor signal provide unsupervised r... | [0.004861196503043175, 0.05312051624059677, -0.013334537856280804, 0.060094740241765976, -0.014360965229570866, -0.00657928129658103, 0.031503353267908096, -0.013995381072163582, -0.013675793074071407, -0.05009421706199646, -0.034869443625211716, 0.04285014793276787, 0.00045034135109744966, 0.018662504851818085, 0.0559... |
1,955 | 1,955 | ['Han Zhang', 'Tao Xu', 'Hongsheng Li', 'Shaoting Zhang', 'Xiaogang Wang', 'Xiaolei Huang', 'Dimitris Metaxas'] | 1612.03242v2 | Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In... | StackGAN: Text to Photo-realistic Image Synthesis with Stacked
Generative Adversarial Networks | 2,016 | http://arxiv.org/pdf/1612.03242v2 | Title StackGAN Text Photorealistic Image Synthesis Stacked Generative Adversarial Networks Summary Synthesizing highquality image text description challenging problem computer vision many practical application Samples generated existing texttoimage approach roughly reflect meaning given description fail contain necessa... | [0.004407680127769709, 0.0789143368601799, 0.018100561574101448, 0.03592776879668236, -0.0011214129626750946, -0.02349100261926651, 0.004202753771096468, 0.008034833706915379, -0.050943028181791306, 0.006091931369155645, 0.007521857973188162, -0.035865988582372665, -0.0016414282144978642, 0.04541533440351486, 0.0373324... |
1,956 | 1,956 | ['Mehdi S. M. Sajjadi', 'Bernhard Schölkopf', 'Michael Hirsch'] | 1612.07919v2 | Single image super-resolution is the task of inferring a high-resolution
image from a single low-resolution input. Traditionally, the performance of
algorithms for this task is measured using pixel-wise reconstruction measures
such as peak signal-to-noise ratio (PSNR) which have been shown to correlate
poorly with the ... | EnhanceNet: Single Image Super-Resolution Through Automated Texture
Synthesis | 2,016 | http://arxiv.org/pdf/1612.07919v2 | Title EnhanceNet Single Image SuperResolution Automated Texture Synthesis Summary Single image superresolution task inferring highresolution image single lowresolution input Traditionally performance algorithm task measured using pixelwise reconstruction measure peak signaltonoise ratio PSNR shown correlate poorly huma... | [0.017575522884726524, 0.07690668851137161, 0.0029536853544414043, 0.06364759802818298, 0.008040985092520714, -0.0515047088265419, -0.010118509642779827, 0.002637502271682024, -0.07499141991138458, 0.05689803510904312, 0.025371678173542023, 0.029098451137542725, -0.0034516314044594765, 0.013382057659327984, 0.049479138... |
1,957 | 1,957 | ['Vikash K. Mansinghka', 'Tejas D. Kulkarni', 'Yura N. Perov', 'Joshua B. Tenenbaum'] | 1307.0060v1 | The idea of computer vision as the Bayesian inverse problem to computer
graphics has a long history and an appealing elegance, but it has proved
difficult to directly implement. Instead, most vision tasks are approached via
complex bottom-up processing pipelines. Here we show that it is possible to
write short, simple ... | Approximate Bayesian Image Interpretation using Generative Probabilistic
Graphics Programs | 2,013 | http://arxiv.org/pdf/1307.0060v1 | Title Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Summary idea computer vision Bayesian inverse problem computer graphic long history appealing elegance proved difficult directly implement Instead vision task approached via complex bottomup processing pipeline show possibl... | [-0.011626800522208214, 0.05189124122262001, 0.007916132919490337, 0.04802766069769859, -0.04256373271346092, 0.015114221721887589, -0.0033600821625441313, 0.039497386664152145, -0.054562292993068695, 0.002229855628684163, 0.04348060488700867, 0.012416288256645203, 0.05728089064359665, 0.071538545191288, 0.005507067311... |
1,958 | 1,958 | ['Tejas D. Kulkarni', 'Vikash K. Mansinghka', 'Pushmeet Kohli', 'Joshua B. Tenenbaum'] | 1407.1339v1 | Recently, multiple formulations of vision problems as probabilistic
inversions of generative models based on computer graphics have been proposed.
However, applications to 3D perception from natural images have focused on
low-dimensional latent scenes, due to challenges in both modeling and
inference. Accounting for th... | Inverse Graphics with Probabilistic CAD Models | 2,014 | http://arxiv.org/pdf/1407.1339v1 | Title Inverse Graphics Probabilistic CAD Models Summary Recently multiple formulation vision problem probabilistic inversion generative model based computer graphic proposed However application 3D perception natural image focused lowdimensional latent scene due challenge modeling inference Accounting enormous variabili... | [0.012258650735020638, 0.01590992696583271, 0.0070972018875181675, 0.03177201375365257, -0.021831922233104706, -0.008755332790315151, -0.022285103797912598, -0.01631012000143528, -0.0594063438475132, 0.005801831372082233, 0.02545652538537979, 0.004951040260493755, 0.0337098129093647, 0.03812510892748833, 0.049214668571... |
1,959 | 1,959 | ['Harris V. Georgiou'] | 1410.7100v1 | Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool
for localizing and analyzing brain activity. This study focuses on one very
important aspect of the functional properties of human brain, specifically the
estimation of the level of parallelism when performing complex cognitive tasks.
Using fM... | Estimating the intrinsic dimension in fMRI space via dataset fractal
analysis - Counting the `cpu cores' of the human brain | 2,014 | http://arxiv.org/pdf/1410.7100v1 | Title Estimating intrinsic dimension fMRI space via dataset fractal analysis Counting cpu core human brain Summary Functional Magnetic Resonance Imaging fMRI powerful noninvasive tool localizing analyzing brain activity study focus one important aspect functional property human brain specifically estimation level paral... | [-0.04860258474946022, -0.005726220551878214, -0.052349288016557693, -0.005320407450199127, -0.011070162057876587, 0.04653465002775192, 0.05263819918036461, 0.04014687240123749, -0.011570101603865623, 0.0863010436296463, -0.0016188955632969737, -0.0972800999879837, 0.043253008276224136, 0.05158410593867302, 0.025566838... |
1,960 | 1,960 | ['Balint Antal', 'Andras Hajdu'] | 1410.8577v1 | Reliable microaneurysm detection in digital fundus images is still an open
issue in medical image processing. We propose an ensemble-based framework to
improve microaneurysm detection. Unlike the well-known approach of considering
the output of multiple classifiers, we propose a combination of internal
components of mi... | An Ensemble-based System for Microaneurysm Detection and Diabetic
Retinopathy Grading | 2,014 | http://arxiv.org/pdf/1410.8577v1 | Title Ensemblebased System Microaneurysm Detection Diabetic Retinopathy Grading Summary Reliable microaneurysm detection digital fundus image still open issue medical image processing propose ensemblebased framework improve microaneurysm detection Unlike wellknown approach considering output multiple classifier propose... | [0.018201958388090134, -0.02171334996819496, -0.007578928954899311, 0.008542180992662907, 0.0272004883736372, 0.03563505411148071, 0.054961901158094406, 0.04783674329519272, 0.03581336513161659, -0.01419894490391016, 0.08192003518342972, 0.01405976340174675, 0.020808303728699684, 0.04847802594304085, -0.000548334734048... |
1,961 | 1,961 | ['Liang Lin', 'Guangrun Wang', 'Wangmeng Zuo', 'Xiangchu Feng', 'Lei Zhang'] | 1605.04039v1 | Cross-domain visual data matching is one of the fundamental problems in many
real-world vision tasks, e.g., matching persons across ID photos and
surveillance videos. Conventional approaches to this problem usually involves
two steps: i) projecting samples from different domains into a common space,
and ii) computing (... | Cross-Domain Visual Matching via Generalized Similarity Measure and
Feature Learning | 2,016 | http://arxiv.org/pdf/1605.04039v1 | Title CrossDomain Visual Matching via Generalized Similarity Measure Feature Learning Summary Crossdomain visual data matching one fundamental problem many realworld vision task eg matching person across ID photo surveillance video Conventional approach problem usually involves two step projecting sample different doma... | [-0.03851548209786415, 0.057604432106018066, -0.004048462957143784, 0.01318285707384348, -0.025010228157043457, 0.023224834352731705, 0.047299742698669434, 0.00919927004724741, -0.028876138851046562, -0.003437710227444768, -0.052661728113889694, -0.060283757746219635, 0.024094179272651672, 0.03700126335024834, 0.034305... |
1,962 | 1,962 | ['Chong Peng', 'Zhao Kang', 'Qiang Chen'] | 1609.08677v1 | Robust principal component analysis (RPCA) has been widely used for
recovering low-rank matrices in many data mining and machine learning problems.
It separates a data matrix into a low-rank part and a sparse part. The convex
approach has been well studied in the literature. However, state-of-the-art
algorithms for the... | A Fast Factorization-based Approach to Robust PCA | 2,016 | http://arxiv.org/pdf/1609.08677v1 | Title Fast Factorizationbased Approach Robust PCA Summary Robust principal component analysis RPCA widely used recovering lowrank matrix many data mining machine learning problem separate data matrix lowrank part sparse part convex approach well studied literature However stateoftheart algorithm convex approach usually... | [-0.00940763857215643, 0.044874463230371475, -0.039731215685606, 0.07160425931215286, 0.048297218978405, 0.0026042794343084097, -0.0028453918639570475, 0.019354255869984627, 0.0026609431952238083, -0.006012165453284979, 0.018118582665920258, 0.01902424916625023, 0.011185460723936558, 0.05133288353681564, -0.01697207055... |
1,963 | 1,963 | ['Petar Veličković', 'Duo Wang', 'Nicholas D. Lane', 'Pietro Liò'] | 1610.00163v2 | In this paper we propose cross-modal convolutional neural networks (X-CNNs),
a novel biologically inspired type of CNN architectures, treating gradient
descent-specialised CNNs as individual units of processing in a larger-scale
network topology, while allowing for unconstrained information flow and/or
weight sharing b... | X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets | 2,016 | http://arxiv.org/pdf/1610.00163v2 | Title XCNN Crossmodal Convolutional Neural Networks Sparse Datasets Summary paper propose crossmodal convolutional neural network XCNNs novel biologically inspired type CNN architecture treating gradient descentspecialised CNNs individual unit processing largerscale network topology allowing unconstrained information f... | [-0.007940697483718395, 0.02541801892220974, -0.011006438173353672, 0.06740560382604599, 0.004110641311854124, -0.04911426082253456, 0.05959277227520943, 0.012296239845454693, -0.0336618646979332, -0.0035540557000786066, -0.09771452844142914, 0.0011273830896243453, 0.0027520328294485807, 0.039823733270168304, 0.0477296... |
1,964 | 1,964 | ['Rahaf Aljundi', 'Punarjay Chakravarty', 'Tinne Tuytelaars'] | 1611.06194v2 | In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new... | Expert Gate: Lifelong Learning with a Network of Experts | 2,016 | http://arxiv.org/pdf/1611.06194v2 | Title Expert Gate Lifelong Learning Network Experts Summary paper introduce model lifelong learning based Network Experts New task expert learned added model sequentially building learned ensure scalability processdata previous task cannot stored hence available learning new task critical issue context addressed litera... | [0.019863784313201904, 0.06267870217561722, -0.03421945124864578, 0.020915154367685318, -0.001748212962411344, -0.0005668086814694107, 0.06730546057224274, -0.013830186799168587, 0.00867999717593193, -0.05184248462319374, -0.015726320445537567, 0.026610907167196274, -0.0427088625729084, 0.1459474116563797, 0.0269659124... |
1,965 | 1,965 | ['Ehsan Jahangiri', 'Erdem Yoruk', 'Rene Vidal', 'Laurent Younes', 'Donald Geman'] | 1701.02343v1 | Despite enormous progress in object detection and classification, the problem
of incorporating expected contextual relationships among object instances into
modern recognition systems remains a key challenge. In this work we propose
Information Pursuit, a Bayesian framework for scene parsing that combines prior
models ... | Information Pursuit: A Bayesian Framework for Sequential Scene Parsing | 2,017 | http://arxiv.org/pdf/1701.02343v1 | Title Information Pursuit Bayesian Framework Sequential Scene Parsing Summary Despite enormous progress object detection classification problem incorporating expected contextual relationship among object instance modern recognition system remains key challenge work propose Information Pursuit Bayesian framework scene p... | [0.02754591405391693, 0.0341215506196022, 0.06118111312389374, 0.09648868441581726, -0.050924960523843765, 0.062030695378780365, 0.007713215425610542, 0.04618518054485321, -0.03541554510593414, -0.06001687049865723, -0.009126164950430393, 0.016158491373062134, 0.020053774118423462, 0.015286733396351337, -0.034150194376... |
1,966 | 1,966 | ['Ehsan Jahangiri', 'Alan L. Yuille'] | 1702.02258v2 | We propose a method to generate multiple diverse and valid human pose
hypotheses in 3D all consistent with the 2D detection of joints in a monocular
RGB image. We use a novel generative model uniform (unbiased) in the space of
anatomically plausible 3D poses. Our model is compositional (produces a pose by
combining par... | Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with
2D Joint Detections | 2,017 | http://arxiv.org/pdf/1702.02258v2 | Title Generating Multiple Diverse Hypotheses Human 3D Pose Consistent 2D Joint Detections Summary propose method generate multiple diverse valid human pose hypothesis 3D consistent 2D detection joint monocular RGB image use novel generative model uniform unbiased space anatomically plausible 3D pose model compositional... | [0.038782261312007904, 0.03645915910601616, -0.007229262497276068, 0.04087431728839874, -0.0036383606493473053, 0.028980283066630363, -0.00047003242070786655, 0.029281822964549065, 0.01399382296949625, -0.06658069789409637, -0.023100340738892555, -0.031910914927721024, 0.0664171576499939, 0.010348212905228138, 0.022117... |
1,967 | 1,967 | ['Ahmed Hussain Qureshi', 'Yutaka Nakamura', 'Yuichiro Yoshikawa', 'Hiroshi Ishiguro'] | 1702.07492v1 | For robots to coexist with humans in a social world like ours, it is crucial
that they possess human-like social interaction skills. Programming a robot to
possess such skills is a challenging task. In this paper, we propose a
Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like
interaction skills thr... | Robot gains Social Intelligence through Multimodal Deep Reinforcement
Learning | 2,017 | http://arxiv.org/pdf/1702.07492v1 | Title Robot gain Social Intelligence Multimodal Deep Reinforcement Learning Summary robot coexist human social world like crucial posse humanlike social interaction skill Programming robot posse skill challenging task paper propose Multimodal Deep QNetwork MDQN enable robot learn humanlike interaction skill trial error... | [0.02404920570552349, 0.03676782548427582, -0.005807043518871069, -0.0054890066385269165, 0.007047295104712248, 0.00698200473561883, 0.0013814045814797282, -0.044368576258420944, 0.0140475919470191, 0.003226795932278037, -0.060420677065849304, 0.03283054754137993, -0.0002511660859454423, 0.07016677409410477, 0.03045933... |
1,968 | 1,968 | ['Ahmed Hussain Qureshi', 'Yutaka Nakamura', 'Yuichiro Yoshikawa', 'Hiroshi Ishiguro'] | 1702.08626v1 | For a safe, natural and effective human-robot social interaction, it is
essential to develop a system that allows a robot to demonstrate the
perceivable responsive behaviors to complex human behaviors. We introduce the
Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
human-like social intera... | Show, Attend and Interact: Perceivable Human-Robot Social Interaction
through Neural Attention Q-Network | 2,017 | http://arxiv.org/pdf/1702.08626v1 | Title Show Attend Interact Perceivable HumanRobot Social Interaction Neural Attention QNetwork Summary safe natural effective humanrobot social interaction essential develop system allows robot demonstrate perceivable responsive behavior complex human behavior introduce Multimodal Deep Attention Recurrent QNetwork usin... | [0.036009062081575394, -0.010334296151995659, -0.0034927933011204004, -0.032354217022657394, 0.012577390298247337, 0.013225115835666656, 0.00267756637185812, -0.017654718831181526, 0.02671835757791996, -0.0011322794016450644, -0.021516337990760803, 0.01052994653582573, -0.01730543188750744, 0.0630308985710144, 0.027843... |
1,969 | 1,969 | ['Jose Luis Garcia-Arroyo', 'Begonya Garcia-Zapirain'] | 1703.03888v1 | This paper proposes an innovative method for segmentation of skin lesions in
dermoscopy images developed by the authors, based on fuzzy classification of
pixels and histogram thresholding. | Segmentation of skin lesions based on fuzzy classification of pixels and
histogram thresholding | 2,017 | http://arxiv.org/pdf/1703.03888v1 | Title Segmentation skin lesion based fuzzy classification pixel histogram thresholding Summary paper proposes innovative method segmentation skin lesion dermoscopy image developed author based fuzzy classification pixel histogram thresholding Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iuli... | [0.010086758062243462, -0.030734796077013016, -0.02581441029906273, -0.022781111299991608, 0.0185412410646677, 0.0018030513310804963, 0.029919806867837906, 0.029927093535661697, 0.004885641857981682, 0.0009628386469557881, 0.0597672201693058, 0.015730326995253563, 0.02837780863046646, 0.006459304131567478, -0.008551144... |
1,970 | 1,970 | ['Swami Sankaranarayanan', 'Arpit Jain', 'Ser Nam Lim'] | 1703.07928v2 | Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of re... | Self corrective Perturbations for Semantic Segmentation and
Classification | 2,017 | http://arxiv.org/pdf/1703.07928v2 | Title Self corrective Perturbations Semantic Segmentation Classification Summary Convolutional Neural Networks subject great importance past decade great stride made utility producing state art performance many computer vision problem However behavior deep network yet fully understood still active area research work pr... | [0.014037585817277431, 0.027607379481196404, 0.0064954375848174095, 0.0861433893442154, -0.0035159692633897066, -0.009506097994744778, 0.008485223166644573, -0.03128369152545929, -0.031467217952013016, -0.007371558807790279, -0.03004472702741623, 0.0765342116355896, -0.031030364334583282, 0.01316814310848713, -0.003328... |
1,971 | 1,971 | ['Hongyoon Choi', 'Kyong Hwan Jin'] | 1704.06033v1 | For effective treatment of Alzheimer disease (AD), it is important to
identify subjects who are most likely to exhibit rapid cognitive decline.
Herein, we developed a novel framework based on a deep convolutional neural
network which can predict future cognitive decline in mild cognitive impairment
(MCI) patients using... | Predicting Cognitive Decline with Deep Learning of Brain Metabolism and
Amyloid Imaging | 2,017 | http://arxiv.org/pdf/1704.06033v1 | Title Predicting Cognitive Decline Deep Learning Brain Metabolism Amyloid Imaging Summary effective treatment Alzheimer disease AD important identify subject likely exhibit rapid cognitive decline Herein developed novel framework based deep convolutional neural network predict future cognitive decline mild cognitive im... | [-0.022972162812948227, 0.10184957087039948, -0.020820150151848793, -0.007596414070576429, 0.057178959250450134, 0.008113421499729156, 0.03202507272362709, 0.06400543451309204, 0.06622902303934097, 0.04947930946946144, 0.013972852379083633, 0.029944688081741333, 0.022608673200011253, 0.09221390634775162, 0.023791447281... |
1,972 | 1,972 | ['Babak Toghiani-Rizi', 'Christofer Lind', 'Maria Svensson', 'Marcus Windmark'] | 1705.05884v1 | In this report, an automated bartender system was developed for making orders
in a bar using hand gestures. The gesture recognition of the system was
developed using Machine Learning techniques, where the model was trained to
classify gestures using collected data. The final model used in the system
reached an average ... | Static Gesture Recognition using Leap Motion | 2,017 | http://arxiv.org/pdf/1705.05884v1 | Title Static Gesture Recognition using Leap Motion Summary report automated bartender system developed making order bar using hand gesture gesture recognition system developed using Machine Learning technique model trained classify gesture using collected data final model used system reached average accuracy 95 system ... | [0.02472764626145363, 0.03799665346741676, -0.020114004611968994, -0.012377910315990448, -0.02267616055905819, 0.06293302774429321, 0.07161227613687515, 0.061751168221235275, 0.00851206574589014, -0.02691817842423916, 0.007224249187856913, -0.002497903537005186, 0.04215674102306366, 0.04655322805047035, -0.009609126485... |
1,973 | 1,973 | ['Nikolay Jetchev', 'Urs Bergmann'] | 1709.04695v1 | We present a novel method to solve image analogy problems : it allows to
learn the relation between paired images present in training data, and then
generalize and generate images that correspond to the relation, but were never
seen in the training set. Therefore, we call the method Conditional Analogy
Generative Adver... | The Conditional Analogy GAN: Swapping Fashion Articles on People Images | 2,017 | http://arxiv.org/pdf/1709.04695v1 | Title Conditional Analogy GAN Swapping Fashion Articles People Images Summary present novel method solve image analogy problem allows learn relation paired image present training data generalize generate image correspond relation never seen training set Therefore call method Conditional Analogy Generative Adversarial N... | [0.012495940551161766, 0.07017572969198227, 0.0011922339908778667, 0.0726662129163742, -0.006574581377208233, 0.004304699599742889, 0.021316833794116974, 0.004403966479003429, -0.02447667345404625, -0.008010302670300007, -0.0257248654961586, 0.017762543633580208, -0.03588474914431572, 0.015225215815007687, 0.0513887628... |
1,974 | 1,974 | ['Amit Mandelbaum', 'Daphna Weinshall'] | 1709.09844v1 | The reliable measurement of confidence in classifiers' predictions is very
important for many applications and is, therefore, an important part of
classifier design. Yet, although deep learning has received tremendous
attention in recent years, not much progress has been made in quantifying the
prediction confidence of... | Distance-based Confidence Score for Neural Network Classifiers | 2,017 | http://arxiv.org/pdf/1709.09844v1 | Title Distancebased Confidence Score Neural Network Classifiers Summary reliable measurement confidence classifier prediction important many application therefore important part classifier design Yet although deep learning received tremendous attention recent year much progress made quantifying prediction confidence ne... | [0.003964792937040329, 0.05895324423909187, -0.013620154932141304, 0.021847909316420555, -0.029156610369682312, -0.034118685871362686, 0.025903325527906418, -0.0137969134375453, -0.010856629349291325, 0.02672867476940155, 0.017470043152570724, -0.027551408857107162, 0.013855512253940105, 0.06485381722450256, 0.00253336... |
1,975 | 1,975 | ['Han Zhang', 'Tao Xu', 'Hongsheng Li', 'Shaoting Zhang', 'Xiaogang Wang', 'Xiaolei Huang', 'Dimitris Metaxas'] | 1710.10916v2 | Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-... | StackGAN++: Realistic Image Synthesis with Stacked Generative
Adversarial Networks | 2,017 | http://arxiv.org/pdf/1710.10916v2 | Title StackGAN Realistic Image Synthesis Stacked Generative Adversarial Networks Summary Although Generative Adversarial Networks GANs shown remarkable success various task still face challenge generating high quality image paper propose Stacked Generative Adversarial Networks StackGAN aiming generating highresolution ... | [-0.0015631030546501279, 0.07733391225337982, 0.01014532521367073, 0.026093093678355217, 0.014250113628804684, -0.022589268162846565, 0.018427535891532898, 0.009084799326956272, -0.06361372023820877, 0.022160882130265236, -0.02895629219710827, -0.014986860565841198, -0.010389686562120914, 0.020656755194067955, 0.046363... |
1,976 | 1,976 | ['Rahaf Aljundi', 'Francesca Babiloni', 'Mohamed Elhoseiny', 'Marcus Rohrbach', 'Tinne Tuytelaars'] | 1711.09601v1 | Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming... | Memory Aware Synapses: Learning what (not) to forget | 2,017 | http://arxiv.org/pdf/1711.09601v1 | Title Memory Aware Synapses Learning forget Summary Humans learn continuous manner Old rarely utilized knowledge overwritten new incoming information important frequently used knowledge prevented erased artificial learning system lifelong learning far focused mainly accumulating knowledge task overcoming catastrophic f... | [0.0028379769064486027, 0.034335386008024216, -0.020884960889816284, 0.03227798268198967, 0.020020270720124245, 0.012734457850456238, 0.014438924379646778, -0.02661140263080597, 0.0406244732439518, -0.011156192980706692, -0.008518180809915066, 0.0182108823210001, 0.0025903903879225254, 0.04846550151705742, 0.0282554700... |
1,977 | 1,977 | ['Hyunwoo Lee', 'Jooyoung Kim', 'Dojun Yang', 'Joon-Ho Kim'] | 1711.11200v1 | This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recogn... | Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care | 2,017 | http://arxiv.org/pdf/1711.11200v1 | Title Embedded RealTime Fall Detection Using Deep Learning Elderly Care Summary paper proposes realtime embedded fall detection system using DVSDynamic Vision Sensor never used traditional fall detection dataset fall detection using DVSTNDVSTemporal Network first contribution building DVS Falls Dataset made network rec... | [-0.05720779299736023, -0.0075706010684370995, -0.030688539147377014, 0.039439376443624496, 0.011567014269530773, -0.010779167525470257, 0.005299276206642389, -0.007035915274173021, 0.03483397141098976, -0.005770990625023842, 0.0805526077747345, 0.01762794516980648, 0.013498914428055286, 0.08066699653863907, 0.01740673... |
1,978 | 1,978 | ['Ruth Fong', 'Andrea Vedaldi'] | 1801.03454v1 | In an effort to understand the meaning of the intermediate representations
captured by deep networks, recent papers have tried to associate specific
semantic concepts to individual neural network filter responses, where
interesting correlations are often found, largely by focusing on extremal
filter responses. In this ... | Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters
in Deep Neural Networks | 2,018 | http://arxiv.org/pdf/1801.03454v1 | Title Net2Vec Quantifying Explaining Concepts Encoded Filters Deep Neural Networks Summary effort understand meaning intermediate representation captured deep network recent paper tried associate specific semantic concept individual neural network filter response interesting correlation often found largely focusing ext... | [-0.005346860270947218, 0.041252121329307556, -0.0288819819688797, 0.043204862624406815, 0.0025437537115067244, 0.0035371461417526007, -0.019467273727059364, 0.015932800248265266, -0.06381386518478394, 0.0017182852607220411, 0.044278811663389206, 0.0178231094032526, 0.017481604591012, 0.08827931433916092, 0.02625123225... |
1,979 | 1,979 | ['Deboleena Roy', 'Priyadarshini Panda', 'Kaushik Roy'] | 1802.05800v1 | In recent years, Convolutional Neural Networks (CNNs) have shown remarkable
performance in many computer vision tasks such as object recognition and
detection. However, complex training issues, such as "catastrophic forgetting"
and hyper-parameter tuning, make incremental learning in CNNs a difficult
challenge. In this... | Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning | 2,018 | http://arxiv.org/pdf/1802.05800v1 | Title TreeCNN Deep Convolutional Neural Network Lifelong Learning Summary recent year Convolutional Neural Networks CNNs shown remarkable performance many computer vision task object recognition detection However complex training issue catastrophic forgetting hyperparameter tuning make incremental learning CNNs difficu... | [0.023118233308196068, 0.09196504950523376, -0.0065264394506812096, 0.06721778213977814, 0.020465584471821785, 0.012314637191593647, -0.010618460364639759, 0.0011593463132157922, -0.06666744500398636, 0.013124784454703331, -0.0004200581170152873, 0.02488747425377369, -0.034330710768699646, 0.027935773134231567, -0.0297... |
1,980 | 1,980 | ['Cecilia S. Lee', 'Ariel J. Tyring', 'Yue Wu', 'Sa Xiao', 'Ariel S. Rokem', 'Nicolaas P. Deruyter', 'Qinqin Zhang', 'Adnan Tufail', 'Ruikang K. Wang', 'Aaron Y. Lee'] | 1802.08925v1 | Despite significant advances in artificial intelligence (AI) for computer
vision, its application in medical imaging has been limited by the burden and
limits of expert-generated labels. We used images from optical coherence
tomography angiography (OCTA), a relatively new imaging modality that measures
perfusion of the... | Generating retinal flow maps from structural optical coherence
tomography with artificial intelligence | 2,018 | http://arxiv.org/pdf/1802.08925v1 | Title Generating retinal flow map structural optical coherence tomography artificial intelligence Summary Despite significant advance artificial intelligence AI computer vision application medical imaging limited burden limit expertgenerated label used image optical coherence tomography angiography OCTA relatively new ... | [-0.015590861439704895, -0.008160259574651718, -0.0069184978492558, -0.03464813157916069, -0.03814835473895073, -0.014395536854863167, 0.03322206065058708, 0.016522180289030075, -0.0010247487807646394, 0.027023112401366234, 0.06176641955971718, 0.017349539324641228, 0.02651374414563179, 0.1433231085538864, -0.016031323... |
1,981 | 1,981 | ['Sathya N. Ravi', 'Ronak Mehta', 'Vikas Singh'] | 1803.08137v1 | We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in missi... | Robust Blind Deconvolution via Mirror Descent | 2,018 | http://arxiv.org/pdf/1803.08137v1 | Title Robust Blind Deconvolution via Mirror Descent Summary revisit Blind Deconvolution problem focus understanding robustness convergence property Provable robustness noise perturbation receiving recent interest vision obtaining immunity adversarial attack assessing describing failure mode algorithm mission critical a... | [-0.024873217567801476, 0.07485316693782806, -0.005499345250427723, 0.059864338487386703, 0.015645965933799744, -0.029218554496765137, 0.033223606646060944, 0.006737859919667244, 0.023283330723643303, 0.030895516276359558, -0.01648012362420559, 0.03235667198896408, 0.020969627425074577, 0.059272076934576035, 0.01201226... |
1,982 | 1,982 | ['Roland Memisevic'] | 1110.0107v2 | A fundamental operation in many vision tasks, including motion understanding,
stereopsis, visual odometry, or invariant recognition, is establishing
correspondences between images or between images and data from other
modalities. We present an analysis of the role that multiplicative interactions
play in learning such ... | Learning to relate images: Mapping units, complex cells and simultaneous
eigenspaces | 2,011 | http://arxiv.org/pdf/1110.0107v2 | Title Learning relate image Mapping unit complex cell simultaneous eigenspaces Summary fundamental operation many vision task including motion understanding stereopsis visual odometry invariant recognition establishing correspondence image image data modality present analysis role multiplicative interaction play learni... | [-0.004944073501974344, 0.018909916281700134, -0.0016458469908684492, 0.027415376156568527, -0.00268951547332108, 0.06175542622804642, 0.040185898542404175, 0.01638473942875862, -0.03559820353984833, -0.017726033926010132, -0.021726436913013458, -0.011056032963097095, 0.059657130390405655, 0.00103889056481421, 0.012414... |
1,983 | 1,983 | ['Shehroze Bhatti', 'Alban Desmaison', 'Ondrej Miksik', 'Nantas Nardelli', 'N. Siddharth', 'Philip H. S. Torr'] | 1612.00380v1 | A number of recent approaches to policy learning in 2D game domains have been
successful going directly from raw input images to actions. However when
employed in complex 3D environments, they typically suffer from challenges
related to partial observability, combinatorial exploration spaces, path
planning, and a scarc... | Playing Doom with SLAM-Augmented Deep Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1612.00380v1 | Title Playing Doom SLAMAugmented Deep Reinforcement Learning Summary number recent approach policy learning 2D game domain successful going directly raw input image action However employed complex 3D environment typically suffer challenge related partial observability combinatorial exploration space path planning scarc... | [0.03214658424258232, 0.005788092967122793, 0.0007911993889138103, -0.00891051534563303, 0.018258610740303993, -0.012062234804034233, -0.02829083800315857, -0.056368015706539154, -0.022725114598870277, 0.006848024670034647, -0.05227147787809372, 0.03324265405535698, -0.0011649943189695477, 0.08107288926839828, 0.027555... |
1,984 | 1,984 | ['Parker Koch', 'Jason J. Corso'] | 1612.04468v1 | Whereas CNNs have demonstrated immense progress in many vision problems, they
suffer from a dependence on monumental amounts of labeled training data. On the
other hand, dictionary learning does not scale to the size of problems that
CNNs can handle, despite being very effective at low-level vision tasks such as
denois... | Sparse Factorization Layers for Neural Networks with Limited Supervision | 2,016 | http://arxiv.org/pdf/1612.04468v1 | Title Sparse Factorization Layers Neural Networks Limited Supervision Summary Whereas CNNs demonstrated immense progress many vision problem suffer dependence monumental amount labeled training data hand dictionary learning scale size problem CNNs handle despite effective lowlevel vision task denoising inpainting Recen... | [0.00541137158870697, 0.046095382422208786, 0.02151351422071457, 0.06641347706317902, 0.031929466873407364, -0.007964573800563812, 0.04462100565433502, 0.024983523413538933, -0.05556724593043327, 0.01293788943439722, -0.02247467078268528, 0.003511303337290883, -0.018398568034172058, 0.060777559876441956, 0.000833452155... |
1,985 | 1,985 | ['Alberto N. Escalante-B.', 'Laurenz Wiskott'] | 1509.08329v1 | Slow feature analysis (SFA) is an unsupervised learning algorithm that
extracts slowly varying features from a time series. Graph-based SFA (GSFA) is
a supervised extension that can solve regression problems if followed by a
post-processing regression algorithm. A training graph specifies arbitrary
connections between ... | Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA
for the Design of Training Graphs | 2,015 | http://arxiv.org/pdf/1509.08329v1 | Title Theoretical Analysis Optimal Free Responses GraphBased SFA Design Training Graphs Summary Slow feature analysis SFA unsupervised learning algorithm extract slowly varying feature time series Graphbased SFA GSFA supervised extension solve regression problem followed postprocessing regression algorithm training gra... | [0.023135729134082794, 0.03272026777267456, -0.02626785822212696, 0.029738018289208412, 0.010589911602437496, 0.010526339523494244, 0.06106388568878174, 0.015787379816174507, 0.058025915175676346, -0.006170670501887798, 0.03669169917702675, 0.008816413581371307, 0.03022926114499569, 0.12708470225334167, 0.0152737349271... |
1,986 | 1,986 | ['Agne Grinciunaite', 'Amogh Gudi', 'Emrah Tasli', 'Marten den Uyl'] | 1609.00036v3 | This paper explores the capabilities of convolutional neural networks to deal
with a task that is easily manageable for humans: perceiving 3D pose of a human
body from varying angles. However, in our approach, we are restricted to using
a monocular vision system. For this purpose, we apply a convolutional neural
networ... | Human Pose Estimation in Space and Time using 3D CNN | 2,016 | http://arxiv.org/pdf/1609.00036v3 | Title Human Pose Estimation Space Time using 3D CNN Summary paper explores capability convolutional neural network deal task easily manageable human perceiving 3D pose human body varying angle However approach restricted using monocular vision system purpose apply convolutional neural network approach RGB video extend ... | [0.026248063892126083, 0.029549134895205498, 0.007911465130746365, 0.04756024852395058, 0.005172345787286758, 0.015959586948156357, 0.022854674607515335, 0.003499396611005068, -0.01535397581756115, -0.033727191388607025, -0.011850519105792046, -0.03264998644590378, 0.042170342057943344, 0.03846743330359459, 0.080222636... |
1,987 | 1,987 | ['Amal Rannen Triki', 'Rahaf Aljundi', 'Mathew B. Blaschko', 'Tinne Tuytelaars'] | 1704.01920v1 | This paper introduces a new lifelong learning solution where a single model
is trained for a sequence of tasks. The main challenge that vision systems face
in this context is catastrophic forgetting: as they tend to adapt to the most
recently seen task, they lose performance on the tasks that were learned
previously. O... | Encoder Based Lifelong Learning | 2,017 | http://arxiv.org/pdf/1704.01920v1 | Title Encoder Based Lifelong Learning Summary paper introduces new lifelong learning solution single model trained sequence task main challenge vision system face context catastrophic forgetting tend adapt recently seen task lose performance task learned previously method aim preserving knowledge previous task learning... | [-0.02078273333609104, 0.07169186323881149, -0.010402734391391277, 0.024157391861081123, -0.0023549431934952736, 0.033892564475536346, 0.03565932810306549, -0.00938281137496233, -0.006047660484910011, -0.0030452036298811436, 0.04142463952302933, 0.04197240248322487, -0.004235920961946249, 0.10904201865196228, 0.0030975... |
1,988 | 1,988 | ['Seyed A Sajjadi', 'Danial Moazen', 'Ani Nahapetian'] | 1705.02689v1 | Wearable computing is one of the fastest growing technologies today. Smart
watches are poised to take over at least of half the wearable devices market in
the near future. Smart watch screen size, however, is a limiting factor for
growth, as it restricts practical text input. On the other hand, wearable
devices have so... | AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer
Interactions | 2,017 | http://arxiv.org/pdf/1705.02689v1 | Title AirDraw Leveraging Smart Watch Motion Sensors Mobile Human Computer Interactions Summary Wearable computing one fastest growing technology today Smart watch poised take least half wearable device market near future Smart watch screen size however limiting factor growth restricts practical text input hand wearable... | [-0.007691414561122656, -0.05274724215269089, -0.007116193417459726, 0.039587561041116714, -0.00291139492765069, 0.03348410502076149, 0.017813069745898247, 0.04886007681488991, 0.036154769361019135, -0.04486796259880066, 0.034991879016160965, 0.045356422662734985, 0.0635870173573494, 0.060966283082962036, 0.00176254822... |
1,989 | 1,989 | ['David Rolnick', 'Yaron Meirovitch', 'Toufiq Parag', 'Hanspeter Pfister', 'Viren Jain', 'Jeff W. Lichtman', 'Edward S. Boyden', 'Nir Shavit'] | 1705.10882v1 | Deep learning algorithms for connectomics rely upon localized classification,
rather than overall morphology. This leads to a high incidence of erroneously
merged objects. Humans, by contrast, can easily detect such errors by acquiring
intuition for the correct morphology of objects. Biological neurons have
complicated... | Morphological Error Detection in 3D Segmentations | 2,017 | http://arxiv.org/pdf/1705.10882v1 | Title Morphological Error Detection 3D Segmentations Summary Deep learning algorithm connectomics rely upon localized classification rather overall morphology lead high incidence erroneously merged object Humans contrast easily detect error acquiring intuition correct morphology object Biological neuron complicated var... | [-0.01790752448141575, 0.020751740783452988, -0.009770575910806656, 0.037233684211969376, 0.027018265798687935, 0.0245415810495615, 0.02749733254313469, -0.02139386348426342, -0.023958662524819374, 0.054385874420404434, 0.02465571090579033, -0.030502479523420334, 0.09292641282081604, 0.047948263585567474, 0.03407514467... |
1,990 | 1,990 | ['Li Shen'] | 1708.09427v3 | We develop an end-to-end training algorithm for whole-image breast cancer
diagnosis based on mammograms. It requires lesion annotations only at the first
stage of training. After that, a whole image classifier can be trained using
only image level labels. This greatly reduced the reliance on lesion
annotations. Our app... | End-to-end Training for Whole Image Breast Cancer Diagnosis using An All
Convolutional Design | 2,017 | http://arxiv.org/pdf/1708.09427v3 | Title Endtoend Training Whole Image Breast Cancer Diagnosis using Convolutional Design Summary develop endtoend training algorithm wholeimage breast cancer diagnosis based mammogram requires lesion annotation first stage training whole image classifier trained using image level label greatly reduced reliance lesion ann... | [0.03249354660511017, 0.03197140619158745, -0.009393274784088135, 0.011779106222093105, 0.019147001206874847, 0.032474350184202194, 0.034797534346580505, 0.031608983874320984, -0.007411726750433445, 0.019252043217420578, 0.02239890955388546, -0.01690598390996456, -0.053833700716495514, 0.030426815152168274, -0.01224809... |
1,991 | 1,991 | ['Vijay Manikandan Janakiraman'] | 1710.04749v2 | Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations s... | Explaining Aviation Safety Incidents Using Deep Temporal Multiple
Instance Learning | 2,017 | http://arxiv.org/pdf/1710.04749v2 | Title Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning Summary Although aviation accident rare safety incident occur frequently require careful analysis detect mitigate risk timely manner Analyzing safety incident using operational data producing eventbased explanation invaluable airl... | [-0.03841226175427437, 0.032490313053131104, -0.0185301024466753, 0.03434028849005699, 0.013614344410598278, 0.03312031924724579, 0.02941741794347763, -0.011409520171582699, -0.06090288609266281, -0.00532841170206666, 0.06072196736931801, -0.01810302957892418, -0.0344916433095932, 0.09089121967554092, 0.018178563565015... |
1,992 | 1,992 | ['Housam Khalifa Bashier Babiker', 'Randy Goebel'] | 1711.06431v2 | We present a method for explaining the image classification predictions of
deep convolution neural networks, by highlighting the pixels in the image which
influence the final class prediction. Our method requires the identification of
a heuristic method to select parameters hypothesized to be most relevant in
this pred... | Using KL-divergence to focus Deep Visual Explanation | 2,017 | http://arxiv.org/pdf/1711.06431v2 | Title Using KLdivergence focus Deep Visual Explanation Summary present method explaining image classification prediction deep convolution neural network highlighting pixel image influence final class prediction method requires identification heuristic method select parameter hypothesized relevant prediction use Kullbac... | [0.007835954427719116, 0.00448676198720932, -0.02128908969461918, 0.04125114530324936, -0.00716746598482132, -0.023615358397364616, 0.019926849752664566, 0.007102197501808405, -0.06962113082408905, 0.006123377475887537, 0.013773586601018906, 0.03955304995179176, 0.007022903300821781, 0.043635331094264984, 0.04294789955... |
1,993 | 1,993 | ['Xuelin Qian', 'Yanwei Fu', 'Wenxuan Wang', 'Tao Xiang', 'Yang Wu', 'Yu-Gang Jiang', 'Xiangyang Xue'] | 1712.02225v5 | Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for... | Pose-Normalized Image Generation for Person Re-identification | 2,017 | http://arxiv.org/pdf/1712.02225v5 | Title PoseNormalized Image Generation Person Reidentification Summary Person Reidentification reid face two major challenge lack crossview paired training data learning discriminative identitysensitive viewinvariant feature presence large pose variation work address problem proposing novel deep person image generation ... | [-0.02247706800699234, 0.09542440623044968, -0.011474188417196274, 0.0008663114276714623, 0.014104396104812622, 0.03762594237923622, 0.02607634849846363, 0.0017286693910136819, -0.00544303935021162, 0.0431060865521431, 0.007860760204494, -0.033278223127126694, -0.01388423703610897, -0.0014908039011061192, 0.06006785482... |
1,994 | 1,994 | ['Mehdi S. M. Sajjadi', 'Raviteja Vemulapalli', 'Matthew Brown'] | 1801.04590v3 | Recent advances in video super-resolution have shown that convolutional
neural networks combined with motion compensation are able to merge information
from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a
single high-resolu... | Frame-Recurrent Video Super-Resolution | 2,018 | http://arxiv.org/pdf/1801.04590v3 | Title FrameRecurrent Video SuperResolution Summary Recent advance video superresolution shown convolutional neural network combined motion compensation able merge information multiple lowresolution LR frame generate highquality image Current stateoftheart method process batch LR frame generate single highresolution HR ... | [-0.019336925819516182, 0.02537389285862446, 0.049933332949876785, 0.02165704406797886, 0.01755663938820362, -0.00939319096505642, 0.015031899325549603, 0.059534259140491486, -0.06384757161140442, -0.009837331250309944, 0.04688708856701851, 0.00695439800620079, 0.00702296057716012, -0.008783289231359959, 0.034754484891... |
1,995 | 1,995 | ['Alexey Chaplygin', 'Joshua Chacksfield'] | 1802.01435v1 | Convolutional Neural Networks are a well-known staple of modern image
classification. However, it can be difficult to assess the quality and
robustness of such models. Deep models are known to perform well on a given
training and estimation set, but can easily be fooled by data that is
specifically generated for the pu... | A Method for Restoring the Training Set Distribution in an Image
Classifier | 2,018 | http://arxiv.org/pdf/1802.01435v1 | Title Method Restoring Training Set Distribution Image Classifier Summary Convolutional Neural Networks wellknown staple modern image classification However difficult ass quality robustness model Deep model known perform well given training estimation set easily fooled data specifically generated purpose shown one prod... | [-0.02852965146303177, 0.06851203739643097, -0.025401484221220016, 0.04940428584814072, -0.015637975186109543, -0.014230511151254177, 0.02036856859922409, 0.032664474099874496, -0.06536754965782166, 0.0053740390576422215, 0.020694907754659653, 0.044342491775751114, 0.00504992576315999, 0.041147295385599136, 0.027416577... |
1,996 | 1,996 | ['Richard J. Preen', 'Larry Bull'] | 1201.5604v2 | A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular,... | Discrete and fuzzy dynamical genetic programming in the XCSF learning
classifier system | 2,012 | http://arxiv.org/pdf/1201.5604v2 | Title Discrete fuzzy dynamical genetic programming XCSF learning classifier system Summary number representation scheme presented use within learning classifier system ranging binary encoding neural network paper present result investigation using discrete fuzzy dynamical system representation within XCSF learning clas... | [-0.0336943082511425, 0.03730117157101631, -0.06468050926923752, -0.02906373329460621, -0.007885068655014038, -0.02608029730618, -0.009982495568692684, 0.026202762499451637, -0.013336293399333954, -0.01947295106947422, 0.08659270405769348, 0.011125048622488976, -0.015593606047332287, 0.08251720666885376, -0.02254223264... |
1,997 | 1,997 | ['Zhen Li', 'Yizhou Yu'] | 1604.07176v1 | Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverage... | Protein Secondary Structure Prediction Using Cascaded Convolutional and
Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1604.07176v1 | Title Protein Secondary Structure Prediction Using Cascaded Convolutional Recurrent Neural Networks Summary Protein secondary structure prediction important problem bioinformatics Inspired recent success deep neural network paper propose endtoend deep network predicts protein secondary structure integrated local global... | [0.02827388234436512, 0.029116561636328697, 0.011595779098570347, 0.03169550374150276, 0.023811565712094307, -0.025512278079986572, -0.008289695717394352, -0.009067535400390625, 0.0033934114035218954, 0.01916162669658661, 0.016912853345274925, -0.04536593705415726, 0.03575683757662773, 0.06950857490301132, 0.0296377837... |
1,998 | 1,998 | ['Daniel Crawford', 'Anna Levit', 'Navid Ghadermarzy', 'Jaspreet S. Oberoi', 'Pooya Ronagh'] | 1612.05695v2 | We investigate whether quantum annealers with select chip layouts can
outperform classical computers in reinforcement learning tasks. We associate a
transverse field Ising spin Hamiltonian with a layout of qubits similar to that
of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to
numerically ... | Reinforcement Learning Using Quantum Boltzmann Machines | 2,016 | http://arxiv.org/pdf/1612.05695v2 | Title Reinforcement Learning Using Quantum Boltzmann Machines Summary investigate whether quantum annealers select chip layout outperform classical computer reinforcement learning task associate transverse field Ising spin Hamiltonian layout qubits similar deep Boltzmann machine DBM use simulated quantum annealing SQA ... | [-0.03310352563858032, -0.017029354348778725, -0.056926388293504715, 0.025781957432627678, -0.03580565005540848, -0.015372546389698982, 8.114010415738449e-05, 0.017400646582245827, -0.0033552302047610283, 0.02108965814113617, -0.018162701278924942, 0.01810094155371189, -0.0388457216322422, 0.02690531313419342, 0.025603... |
1,999 | 1,999 | ['Keki M. Burjorjee'] | 1307.3824v1 | This paper establishes theoretical bonafides for implicit concurrent
multivariate effect evaluation--implicit concurrency for short---a broad and
versatile computational learning efficiency thought to underlie
general-purpose, non-local, noise-tolerant optimization in genetic algorithms
with uniform crossover (UGAs). W... | The Fundamental Learning Problem that Genetic Algorithms with Uniform
Crossover Solve Efficiently and Repeatedly As Evolution Proceeds | 2,013 | http://arxiv.org/pdf/1307.3824v1 | Title Fundamental Learning Problem Genetic Algorithms Uniform Crossover Solve Efficiently Repeatedly Evolution Proceeds Summary paper establishes theoretical bonafides implicit concurrent multivariate effect evaluationimplicit concurrency shorta broad versatile computational learning efficiency thought underlie general... | [-0.03447984531521797, 0.060624007135629654, -0.029828805476427078, -0.0012971253599971533, -0.0243133082985878, 0.011968517675995827, 0.03353654965758324, 0.011371093802154064, 0.00044509785948321223, -0.035329144448041916, 0.015419398434460163, 0.05531609430909157, -0.01644636131823063, 0.043388281017541885, -0.02370... |
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