Paper Group NANR 278
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices. Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs. Simulating ASR errors for training SLU systems. HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision. Measuring Inno …
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
| Title | Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices |
| Authors | Zengfeng Huang |
| Abstract | Given a large matrix $A\in\real^{n\times d}$, we consider the problem of computing a sketch matrix $B\in\real^{\ell\times d}$ which is significantly smaller than but still well approximates $A$. We are interested in minimizing the covariance error $\norm{A^TA-B^TB}_2.$We consider the problems in the streaming model, where the algorithm can only make one pass over the input with limited working space. The popular Frequent Directions algorithm of Liberty (2013) and its variants achieve optimal space-error tradeoff. However, whether the running time can be improved remains an unanswered question.In this paper, we almost settle the time complexity of this problem. In particular, we provide new space-optimal algorithms with faster running times. Moreover, we also show that the running times of our algorithms are near-optimal unless the state-of-the-art running time of matrix multiplication can be improved significantly. |
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| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2125 |
| http://proceedings.mlr.press/v80/huang18a/huang18a.pdf | |
| PWC | https://paperswithcode.com/paper/near-optimal-frequent-directions-for |
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Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs
| Title | Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs |
| Authors | Eleri Aedmaa, Maximilian K{"o}per, Sabine Schulte im Walde |
| Abstract | This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with language-independent features of non-literal language (such as abstractness). |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-4002/ |
| https://www.aclweb.org/anthology/N18-4002 | |
| PWC | https://paperswithcode.com/paper/combining-abstractness-and-language-specific |
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Simulating ASR errors for training SLU systems
| Title | Simulating ASR errors for training SLU systems |
| Authors | Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Est{`e}ve |
| Abstract | |
| Tasks | Data Augmentation, Slot Filling, Speech Recognition, Spoken Language Understanding, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1499/ |
| https://www.aclweb.org/anthology/L18-1499 | |
| PWC | https://paperswithcode.com/paper/simulating-asr-errors-for-training-slu |
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HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision
| Title | HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision |
| Authors | Xiaokun Wu, Daniel Finnegan, Eamonn O’Neill, Yong-Liang Yang |
| Abstract | This work presents a novel hand pose estimation framework via intermediate dense guidance map supervision. By leveraging the advantage of predicting heat maps of hand joints in detection-based methods, we propose to use dense feature maps through intermediate supervision in a regression-based framework that is not limited to the resolution of the heat map. Our dense feature maps are delicately designed to encode the hand geometry and the spatial relation between local joint and global hand. The proposed framework significantly improves the state-of-the-art in both 2D and 3D on the recent benchmark datasets. |
| Tasks | Hand Pose Estimation, Pose Estimation |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/handmap-robust-hand-pose-estimation-via |
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Measuring Innovation in Speech and Language Processing Publications.
| Title | Measuring Innovation in Speech and Language Processing Publications. |
| Authors | Joseph Mariani, Gil Francopoulo, Patrick Paroubek |
| Abstract | |
| Tasks | Information Retrieval, Optical Character Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1297/ |
| https://www.aclweb.org/anthology/L18-1297 | |
| PWC | https://paperswithcode.com/paper/measuring-innovation-in-speech-and-language |
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Parameterized Algorithms for the Matrix Completion Problem
| Title | Parameterized Algorithms for the Matrix Completion Problem |
| Authors | Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider |
| Abstract | We consider two matrix completion problems, in which we are given a matrix with missing entries and the task is to complete the matrix in a way that (1) minimizes the rank, or (2) minimizes the number of distinct rows. We study the parameterized complexity of the two aforementioned problems with respect to several parameters of interest, including the minimum number of matrix rows, columns, and rows plus columns needed to cover all missing entries. We obtain new algorithmic results showing that, for the bounded domain case, both problems are fixed-parameter tractable with respect to all aforementioned parameters. We complement these results with a lower-bound result for the unbounded domain case that rules out fixed-parameter tractability w.r.t. some of the parameters under consideration. |
| Tasks | Matrix Completion |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2254 |
| http://proceedings.mlr.press/v80/ganian18a/ganian18a.pdf | |
| PWC | https://paperswithcode.com/paper/parameterized-algorithms-for-the-matrix |
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Graph Based Semi-Supervised Learning Approach for Tamil POS tagging
| Title | Graph Based Semi-Supervised Learning Approach for Tamil POS tagging |
| Authors | Mokanarangan Thayaparan, Surangika Ranathunga, Uthayasanker Thayasivam |
| Abstract | |
| Tasks | Graph Similarity, Information Retrieval, Metric Learning, Question Answering, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1624/ |
| https://www.aclweb.org/anthology/L18-1624 | |
| PWC | https://paperswithcode.com/paper/graph-based-semi-supervised-learning-approach |
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A Robust Method for Strong Rolling Shutter Effects Correction Using Lines With Automatic Feature Selection
| Title | A Robust Method for Strong Rolling Shutter Effects Correction Using Lines With Automatic Feature Selection |
| Authors | Yizhen Lao, Omar Ait-Aider |
| Abstract | We present a robust method which compensates RS distortions in a single image using a set of image curves, basing on the knowledge that they correspond to 3D straight lines. Unlike in existing work, no a priori knowledge about the line directions (e.g. Manhattan World assumption) is required. We first formulate a parametric equation for the projection of a 3D straight line viewed by a moving rolling shutter camera under a uniform motion model. Then we propose a method which efficiently estimates ego angular velocity separately from pose parameters, using at least 4 image curves. Moreover, we propose for the first time a RANSAC-like strategy to select image curves which really correspond to 3D straight lines and reject those corresponding to actual curves in 3D world. A comparative experimental study with both synthetic and real data from famous benchmarks shows that the proposed method outperforms all the existing techniques from the state-of-the-art. |
| Tasks | Feature Selection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Lao_A_Robust_Method_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Lao_A_Robust_Method_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-robust-method-for-strong-rolling-shutter |
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Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
| Title | Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer |
| Authors | Alexey Drutsa |
| Abstract | We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of $\Theta(\log\log T)$. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of non-decreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one. |
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| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=1911 |
| http://proceedings.mlr.press/v80/drutsa18a/drutsa18a.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-consistent-optimal-pricing-algorithms |
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Human Appearance Transfer
| Title | Human Appearance Transfer |
| Authors | Mihai Zanfir, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu |
| Abstract | We propose an automatic person-to-person appearance transfer model based on explicit parametric 3d human representations and learned, constrained deep translation network architectures for photographic image synthesis. Given a single source image and a single target image, each corresponding to different human subjects, wearing different clothing and in different poses, our goal is to photo-realistically transfer the appearance from the source image onto the target image while preserving the target shape and clothing segmentation layout. Our solution to this new problem is formulated in terms of a computational pipeline that combines (1) 3d human pose and body shape estimation from monocular images, (2) identifying 3d surface colors elements (mesh triangles) visible in both images, that can be transferred directly using barycentric procedures, and (3) predicting surface appearance missing in the first image but visible in the second one using deep learning-based image synthesis techniques. Our model achieves promising results as supported by a perceptual user study where the participants rated around 65% of our results as good, very good or perfect, as well in automated tests (Inception scores and a Faster-RCNN human detector responding very similarly to real and model generated images). We further show how the proposed architecture can be profiled to automatically generate images of a person dressed with different clothing transferred from a person in another image, opening paths for applications in entertainment and photo-editing (e.g. embodying and posing as friends or famous actors), the fashion industry, or affordable online shopping of clothing. |
| Tasks | Image Generation |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zanfir_Human_Appearance_Transfer_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zanfir_Human_Appearance_Transfer_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/human-appearance-transfer |
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M3: Multimodal Memory Modelling for Video Captioning
| Title | M3: Multimodal Memory Modelling for Video Captioning |
| Authors | Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan |
| Abstract | Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, video captioning has made great progress. However, learning an effective mapping from the visual sequence space to the language space is still a challenging problem due to the long-term multimodal dependency modelling and semantic misalignment. Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems [35] and working memory is the key factor of visual attention [33], we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments. Specifically, similar to [10], the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. To evaluate the proposed model, we perform experiments on two public datasets: MSVD and MSR-VTT. The experimental results demonstrate that our method outperforms most of the state-of-the-art methods in terms of BLEU and METEOR. |
| Tasks | Video Captioning |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_M3_Multimodal_Memory_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_M3_Multimodal_Memory_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/m3-multimodal-memory-modelling-for-video |
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Recurrent Tubelet Proposal and Recognition Networks for Action Detection
| Title | Recurrent Tubelet Proposal and Recognition Networks for Action Detection |
| Authors | Dong Li, Zhaofan Qiu, Qi Dai, Ting Yao, Tao Mei |
| Abstract | Detecting actions in videos is a challenging task as video is an information intensive media with complex variations. Existing approaches predominantly generate action proposals for each individual frame or fixed-length clip independently, while overlooking temporal context across them. Such temporal contextual relations are vital for action detection as an action is by nature a sequence of movements. This motivates us to leverage the localized action proposals in previous frames when determining action regions in the current one. Specifically, we present a novel deep architecture called Recurrent Tubelet Proposal and Recognition (RTPR) networks to incorporate temporal context for action detection. The proposed RTPR consists of two correlated networks, i.e., Recurrent Tubelet Proposal (RTP) networks and Recurrent Tubelet Recognition (RTR) networks. The RTP initializes action proposals of the start frame through a Region Proposal Network and then estimates the movements of proposals in next frame in a recurrent manner. The action proposals of different frames are linked to form the tubelet proposals. The RTR capitalizes on a multi-channel architecture, where in each channel, a tubelet proposal is fed into a CNN plus LSTM to recurrently recognize action in the tubelet. We conduct extensive experiments on four benchmark datasets and demonstrate superior results over state-of-the-art methods. More remarkably, we obtain mAP of 98.6%, 81.3%, 77.9% and 22.3% with gains of 2.9%, 4.3%, 0.7% and 3.9% over the best competitors on UCF-Sports, J-HMDB, UCF-101 and AVA, respectively. |
| Tasks | Action Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Dong_Li_Recurrent_Tubelet_Proposal_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Dong_Li_Recurrent_Tubelet_Proposal_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/recurrent-tubelet-proposal-and-recognition |
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Learning to Embed Semantic Correspondence for Natural Language Understanding
| Title | Learning to Embed Semantic Correspondence for Natural Language Understanding |
| Authors | Sangkeun Jung, Jinsik Lee, Jiwon Kim |
| Abstract | While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking. |
| Tasks | Slot Filling |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-1013/ |
| https://www.aclweb.org/anthology/K18-1013 | |
| PWC | https://paperswithcode.com/paper/learning-to-embed-semantic-correspondence-for |
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Assisted Nominalization for Academic English Writing
| Title | Assisted Nominalization for Academic English Writing |
| Authors | John Lee, Dariush Saberi, Marvin Lam, Jonathan Webster |
| Abstract | |
| Tasks | Lexical Simplification, Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6706/ |
| https://www.aclweb.org/anthology/W18-6706 | |
| PWC | https://paperswithcode.com/paper/assisted-nominalization-for-academic-english |
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Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes
| Title | Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes |
| Authors | Xin Yu, Basura Fernando, Richard Hartley, Fatih Porikli |
| Abstract | Given a tiny face image, conventional face hallucination methods aim to super-resolve its high-resolution (HR) counterpart by learning a mapping from an exemplar dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patches, this ambiguity may lead to erroneous HR facial details and thus distorts final results, such as gender reversal. An LR input contains low-frequency facial components of its HR version while its residual face image defined as the difference between the HR ground-truth and interpolated LR images contains the missing high-frequency facial details. We demonstrate that supplementing residual images or feature maps with facial attribute information can significantly reduce the ambiguity in face super-resolution. To explore this idea, we develop an attribute-embedded upsampling network, which consists of an upsampling network and a discriminative network. The upsampling network is composed of an autoencoder with skip-connections, which incorporates facial attribute vectors into the residual features of LR inputs at the bottleneck of the autoencoder and deconvolutional layers used for upsampling. The discriminative network is designed to examine whether super-resolved faces contain the desired attributes or not and then its loss is used for updating the upsampling network. In this manner, we can super-resolve tiny unaligned (16$ imes$16 pixels) face images with a large upscaling factor of 8$ imes$ while reducing the uncertainty of one-to-many mappings significantly. By conducting extensive evaluations on a large-scale dataset, we demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art. |
| Tasks | Face Hallucination, Super-Resolution |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Super-Resolving_Very_Low-Resolution_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Super-Resolving_Very_Low-Resolution_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/super-resolving-very-low-resolution-face |
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