paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
31bfd71d-b5d8-4706-aade-90158de90824 | make-an-animation-large-scale-text | 2305.09662 | null | https://arxiv.org/abs/2305.09662v1 | https://arxiv.org/pdf/2305.09662v1.pdf | Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation | Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their relian... | ['Sonal Gupta', 'Devi Parikh', 'Thomas Hayes', 'Akbar Shah', 'Samaneh Azadi'] | 2023-05-16 | null | null | null | null | ['video-generation', 'motion-synthesis', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.28082618e-01 6.74045458e-02 -4.65646833e-02 -5.77265471e-02
-8.38914037e-01 -4.15946543e-01 1.10360205e+00 -4.27133441e-01
-4.17012244e-01 5.67193329e-01 8.94969046e-01 1.15643302e-02
3.71776313e-01 -6.51602566e-01 -6.66487515e-01 -4.06402260e-01
2.77954619e-02 5.75004697e-01 4.66264963e-01 -4.12390679... | [7.319580078125, -0.13089828193187714] |
56029be0-f2c5-4ed7-b059-d8dfe8638644 | universal-and-independent-multilingual | 2210.13236 | null | https://arxiv.org/abs/2210.13236v1 | https://arxiv.org/pdf/2210.13236v1.pdf | Universal and Independent: Multilingual Probing Framework for Exhaustive Model Interpretation and Evaluation | Linguistic analysis of language models is one of the ways to explain and describe their reasoning, weaknesses, and limitations. In the probing part of the model interpretability research, studies concern individual languages as well as individual linguistic structures. The question arises: are the detected regularities... | ['Tatiana Shavrina', 'Viktoria Knyazkova', 'Ekaterina Voloshina', 'Vitaly Protasov', 'Oleg Serikov'] | 2022-10-24 | null | null | null | null | ['probing-language-models'] | ['natural-language-processing'] | [-4.25389349e-01 1.95700377e-01 -5.74975789e-01 -2.83618063e-01
-3.64693165e-01 -1.05200863e+00 6.18321240e-01 2.49962106e-01
-1.06885344e-01 4.31860149e-01 4.93849635e-01 -1.00192118e+00
-2.24192277e-01 -5.05096018e-01 -3.99832964e-01 -2.11375147e-01
2.34890774e-01 7.42093801e-01 7.77706802e-02 -7.07726181... | [10.493786811828613, 9.768186569213867] |
9adca49d-1033-4abf-875e-7852136e946a | a-bert-based-distractor-generation-scheme-1 | null | null | https://aclanthology.org/2020.findings-emnlp.393 | https://aclanthology.org/2020.findings-emnlp.393.pdf | A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies. | In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There are still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. H... | ['Yao-Chung Fan', 'Ying-Hong Chan', 'Ho-Lam Chung'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['distractor-generation'] | ['natural-language-processing'] | [-2.41410643e-01 -1.37101710e-01 -2.59188324e-01 1.32725835e-01
-1.39714992e+00 -7.07109749e-01 4.96762425e-01 -1.97949529e-01
-1.48442939e-01 1.27111018e+00 3.40235829e-01 -5.91267526e-01
1.16019905e-01 -4.07436132e-01 -3.28104377e-01 -6.13362312e-01
6.54375315e-01 6.01655722e-01 5.27142644e-01 -7.17460752... | [11.606990814208984, 8.355712890625] |
9a4b1349-13b6-4988-8e4f-dc5fd9a05146 | drone-based-rgbt-vehicle-detection-and | 2003.02437 | null | https://arxiv.org/abs/2003.02437v2 | https://arxiv.org/pdf/2003.02437v2.pdf | Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning | Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. It empowers smart city traffic management and disaster rescue. Researchers have made mount of efforts in this area and achieved considerable progress. Nevertheless, it is still a challenge when the objects are hard to... | ['QinGhua Hu', 'Pengfei Zhu', 'Bing Cao', 'Yiming Sun'] | 2020-03-05 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-9.94308107e-03 -6.29552722e-01 -9.18988138e-02 -1.37648165e-01
-8.59396398e-01 -5.97979605e-01 5.14176965e-01 -2.67803758e-01
-3.18952769e-01 5.29917538e-01 -2.01260865e-01 -1.80142418e-01
-2.93116540e-01 -1.22755384e+00 -3.43979955e-01 -1.09901452e+00
1.62905648e-01 1.55208245e-01 2.15940773e-01 -5.07438004... | [8.501371383666992, -1.7676247358322144] |
7d8ff90e-e8ca-43a0-9318-6b33e745ef66 | neural-shape-compiler-a-unified-framework-for | 2212.12952 | null | https://arxiv.org/abs/2212.12952v2 | https://arxiv.org/pdf/2212.12952v2.pdf | Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program | 3D shapes have complementary abstractions from low-level geometry to part-based hierarchies to languages, which convey different levels of information. This paper presents a unified framework to translate between pairs of shape abstractions: $\textit{Text}$ $\Longleftrightarrow$ $\textit{Point Cloud}$ $\Longleftrightar... | ['Justin Johnson', 'Honglak Lee', 'Tiange Luo'] | 2022-12-25 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 4.88565527e-02 1.62902832e-01 1.97743312e-01 -5.42163908e-01
-9.26625490e-01 -1.03229427e+00 6.01482213e-01 1.66704759e-01
2.29223311e-01 2.30613485e-01 -5.67992330e-01 -1.02297759e+00
-8.12425390e-02 -1.43084311e+00 -1.15726161e+00 -3.43443036e-01
-1.87224343e-01 7.53316879e-01 1.11838393e-01 -3.60811472... | [8.690433502197266, -3.636439561843872] |
6658c844-0626-4051-a31e-20b8a3da2597 | matching-the-blanks-distributional-similarity | 1906.03158 | null | https://arxiv.org/abs/1906.03158v1 | https://arxiv.org/pdf/1906.03158v1.pdf | Matching the Blanks: Distributional Similarity for Relation Learning | General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. H... | ['Tom Kwiatkowski', 'Livio Baldini Soares', 'Nicholas FitzGerald', 'Jeffrey Ling'] | 2019-06-07 | matching-the-blanks-distributional-similarity-1 | https://aclanthology.org/P19-1279 | https://aclanthology.org/P19-1279.pdf | acl-2019-7 | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 2.19153404e-01 1.01598108e+00 -4.50435609e-01 -3.62911463e-01
-6.62255168e-01 -5.91941953e-01 1.13585222e+00 6.40131235e-01
-9.59331691e-02 1.06518495e+00 5.77235818e-01 -6.45818889e-01
-4.31987911e-01 -1.17017138e+00 -4.96227115e-01 -5.77054471e-02
-2.46784776e-01 1.07169580e+00 2.03998387e-01 -7.22868085... | [9.343609809875488, 8.534507751464844] |
b99d5db7-3664-4a1c-a885-1fff13268856 | deep-parametric-indoor-lighting-estimation | 1910.08812 | null | https://arxiv.org/abs/1910.08812v1 | https://arxiv.org/pdf/1910.08812v1.pdf | Deep Parametric Indoor Lighting Estimation | We present a method to estimate lighting from a single image of an indoor scene. Previous work has used an environment map representation that does not account for the localized nature of indoor lighting. Instead, we represent lighting as a set of discrete 3D lights with geometric and photometric parameters. We train a... | ['Jean-François Lalonde', 'Christian Gagné', 'Yannick Hold-Geoffroy', 'Marc-André Gardner', 'Kalyan Sunkavalli'] | 2019-10-19 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.32580036e-01 -1.29155591e-01 5.54082930e-01 -9.12053943e-01
-6.25095785e-01 -7.21485794e-01 6.83518112e-01 -1.00970015e-01
-3.67441922e-01 6.53470457e-01 2.07376525e-01 -1.68429002e-01
3.87749791e-01 -8.40690792e-01 -1.04945779e+00 -3.52155834e-01
1.44458875e-01 2.88442165e-01 -1.11536965e-01 9.28084776... | [9.584202766418457, -2.9875102043151855] |
a1239ecb-cbde-4075-87af-f401ead07d45 | biodivtab-semantic-table-annotation-benchmark | null | null | https://ceur-ws.org/Vol-3324/om2022_LTpaper4.pdf | https://ceur-ws.org/Vol-3324/om2022_LTpaper4.pdf | BiodivTab: Semantic Table Annotation Benchmark Construction, Analysis, and New Additions | Systems that annotate tabular data semantically have witnessed increasing attention from the community in recent years; this process is commonly known as Semantic Table Annotation (STA). Its objective is to map individual table elements to their counterparts from a Knowledge Graph (KG). Individual cells and columns are... | ['Birgitta König-Ries', 'Sirko Schindler', 'Nora Abdelmageed'] | 2023-01-10 | null | null | null | ontology-matching-iswc-2022-2023-1 | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-4.40286584e-02 3.86825562e-01 -3.77087653e-01 -3.13761294e-01
-6.60888612e-01 -9.82753694e-01 6.87403142e-01 8.83995771e-01
-2.25948006e-01 1.14507329e+00 4.19208139e-01 2.03429982e-01
-2.03715153e-02 -9.97236371e-01 -8.52636635e-01 -1.21558048e-01
-1.22375600e-01 1.00551391e+00 3.35819364e-01 -4.35587138... | [9.364334106445312, 7.9934539794921875] |
7ef8d147-848f-4511-bc00-99ad7e34ceb6 | low-power-neuromorphic-emg-gesture | 2206.02061 | null | https://arxiv.org/abs/2206.02061v1 | https://arxiv.org/pdf/2206.02061v1.pdf | Low Power Neuromorphic EMG Gesture Classification | EMG (Electromyograph) signal based gesture recognition can prove vital for applications such as smart wearables and bio-medical neuro-prosthetic control. Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture recognition, owing to their inherent spike/event driven spatio-temporal dynamics. In... | ['Manan Suri', 'Ahmed Shaban', 'Sai Sukruth Bezugam'] | 2022-06-04 | null | null | null | null | ['gesture-recognition', 'emg-gesture-recognition'] | ['computer-vision', 'medical'] | [ 7.51001656e-01 -5.79134345e-01 1.74473599e-01 1.51270509e-01
-4.59882170e-01 -1.16927877e-01 -1.08192839e-01 -5.71794748e-01
-8.65216255e-01 8.35425794e-01 -1.27807930e-01 1.68313339e-01
-8.56544226e-02 -3.59765887e-01 -8.45416248e-01 -9.52621877e-01
-1.54845119e-01 -1.90296564e-02 4.22259331e-01 -1.99036032... | [8.386691093444824, 2.3188066482543945] |
b7329a88-d87e-4e33-b371-7fd135d371f4 | interpretability-and-explainability-a-machine | 2012.01805 | null | https://arxiv.org/abs/2012.01805v2 | https://arxiv.org/pdf/2012.01805v2.pdf | Interpretability and Explainability: A Machine Learning Zoo Mini-tour | In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have escape... | ['Julia E. Vogt', 'Ričards Marcinkevičs'] | 2020-12-03 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.52193403e-01 9.65974331e-01 -8.31753314e-01 -7.55929649e-01
-1.68042585e-01 -2.99908251e-01 4.09493357e-01 2.81489909e-01
4.15073521e-02 8.17691922e-01 3.32430989e-01 -6.85434759e-01
-6.53971255e-01 -4.37191367e-01 -3.59476715e-01 -5.20548582e-01
-1.18919052e-01 7.45472431e-01 -8.82474482e-01 1.82271842... | [8.761368751525879, 5.818778991699219] |
ff1395e3-8168-4538-b4ed-481748df27f6 | samplet-basis-pursuit | 2306.10180 | null | https://arxiv.org/abs/2306.10180v2 | https://arxiv.org/pdf/2306.10180v2.pdf | Samplet basis pursuit | We consider kernel-based learning in samplet coordinates with l1-regularization. The application of an l1-regularization term enforces sparsity of the coefficients with respect to the samplet basis. Therefore, we call this approach samplet basis pursuit. Samplets are wavelet-type signed measures, which are tailored to ... | ['Helmut Harbrecht', 'Michael Multerer', 'Davide Baroli'] | 2023-06-16 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 4.09076869e-01 -7.11437762e-02 8.56914669e-02 -4.41696167e-01
-1.09552205e+00 -9.18635502e-02 3.73596758e-01 1.48053035e-01
-2.78103143e-01 5.84746897e-01 5.48483692e-02 3.15658927e-01
-3.33472461e-01 -6.39411986e-01 -6.52003229e-01 -1.24426425e+00
-2.41462648e-01 3.58626902e-01 4.90920171e-02 -2.37102151... | [11.671968460083008, -2.310257911682129] |
07cace9d-8416-4377-8540-3da08366d29e | a-conditional-splitting-framework-for | 2106.15760 | null | https://arxiv.org/abs/2106.15760v1 | https://arxiv.org/pdf/2106.15760v1.pdf | A Conditional Splitting Framework for Efficient Constituency Parsing | We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-... | ['XiaoLi Li', 'Shafiq Joty', 'Xuan-Phi Nguyen', 'Thanh-Tung Nguyen'] | 2021-06-30 | null | https://aclanthology.org/2021.acl-long.450 | https://aclanthology.org/2021.acl-long.450.pdf | acl-2021-5 | ['discourse-segmentation', 'discourse-parsing', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.19394398e-01 8.75032306e-01 -2.72575676e-01 -5.23352742e-01
-1.39458251e+00 -9.17363226e-01 6.10286474e-01 3.45100611e-01
-3.10539216e-01 7.25990176e-01 8.20198357e-01 -8.33268166e-01
3.98972303e-01 -8.42375219e-01 -8.27590466e-01 -4.32470709e-01
6.74404427e-02 5.90267003e-01 3.51646453e-01 -2.44651556... | [10.724250793457031, 9.493741989135742] |
92eac626-6285-4796-803b-d00ef27016f0 | ibot-image-bert-pre-training-with-online | 2111.07832 | null | https://arxiv.org/abs/2111.07832v3 | https://arxiv.org/pdf/2111.07832v3.pdf | iBOT: Image BERT Pre-Training with Online Tokenizer | The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces. In this work, we study masked image modeling (MIM) and indicate the advantages and challenges of using a semantically meaningful visual... | ['Tao Kong', 'Alan Yuille', 'Cihang Xie', 'Wei Shen', 'Huiyu Wang', 'Chen Wei', 'Jinghao Zhou'] | 2021-11-15 | null | null | null | null | ['self-supervised-image-classification', 'semi-supervised-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.82621580e-01 7.29775071e-01 -3.79132926e-01 -5.07461667e-01
-9.41732228e-01 -3.28688204e-01 5.84149837e-01 8.75567943e-02
-6.67424262e-01 2.20343873e-01 7.10568279e-02 -3.47985119e-01
6.30617142e-01 -3.20754081e-01 -1.19444895e+00 -6.87616825e-01
8.85066837e-02 3.78179312e-01 2.78505325e-01 2.41247088... | [9.658617973327637, 0.851128876209259] |
b78d88a7-45b6-4e0f-8298-ef3f6d17295e | benchmarking-scene-text-recognition-in | 2104.04437 | null | https://arxiv.org/abs/2104.04437v1 | https://arxiv.org/pdf/2104.04437v1.pdf | Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam | Inspired by the success of Deep Learning based approaches to English scene text recognition, we pose and benchmark scene text recognition for three Indic scripts - Devanagari, Telugu and Malayalam. Synthetic word images rendered from Unicode fonts are used for training the recognition system. And the performance is ben... | ['CV Jawahar', 'Mohit Jain', 'Minesh Mathew'] | 2021-04-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.63143098e-01 -3.43084097e-01 1.37494177e-01 -4.57071215e-01
-7.66506195e-01 -8.23391140e-01 8.14994395e-01 -5.15118301e-01
-6.50795043e-01 5.60218573e-01 1.49793521e-01 -5.40951669e-01
4.99961436e-01 -5.94381154e-01 -7.05700159e-01 -7.35878348e-01
4.01274800e-01 7.09045053e-01 -1.61333695e-01 -1.96871564... | [11.881718635559082, 2.3476946353912354] |
81d22d8a-2b6f-4c30-9158-7b682dcb5331 | guided-generative-adversarial-neural-network | 2003.02836 | null | https://arxiv.org/abs/2003.02836v2 | https://arxiv.org/pdf/2003.02836v2.pdf | Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data | Recent improvements in Generative Adversarial Neural Networks (GANs) have shown their ability to generate higher quality samples as well as to learn good representations for transfer learning. Most of the representation learning methods based on GANs learn representations ignoring their post-use scenario, which can lea... | ['Björn Schuller', 'John H. L. Hansen', 'Rajib Rana', 'Kazi Nazmul Haque'] | 2020-03-05 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 8.51065218e-01 4.95573610e-01 1.37696594e-01 -4.37249362e-01
-8.22238445e-01 -3.87471795e-01 3.62449408e-01 -3.83984029e-01
1.83010861e-01 8.78407776e-01 2.64489651e-01 1.61924496e-01
3.28077525e-01 -1.12158012e+00 -7.07024217e-01 -9.06403124e-01
2.18694210e-01 4.41169500e-01 -4.19100016e-01 -3.48087043... | [11.784672737121582, 0.127290740609169] |
6dc9b0c2-2c35-41ca-83f9-049dd0a0085e | character-based-neural-networks-for-sentence | 1805.08297 | null | http://arxiv.org/abs/1805.08297v1 | http://arxiv.org/pdf/1805.08297v1.pdf | Character-based Neural Networks for Sentence Pair Modeling | Sentence pair modeling is critical for many NLP tasks, such as paraphrase
identification, semantic textual similarity, and natural language inference.
Most state-of-the-art neural models for these tasks rely on pretrained word
embedding and compose sentence-level semantics in varied ways; however, few
works have attemp... | ['Wuwei Lan', 'Wei Xu'] | 2018-05-21 | character-based-neural-networks-for-sentence-1 | https://aclanthology.org/N18-2025 | https://aclanthology.org/N18-2025.pdf | naacl-2018-6 | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 2.48354629e-01 -2.73036152e-01 -4.33532357e-01 -5.46240509e-01
-5.59545577e-01 -3.98121864e-01 7.07481086e-01 8.75586689e-01
-5.91839790e-01 2.08906174e-01 7.41711557e-01 -5.75724781e-01
1.23255871e-01 -8.00313473e-01 -6.44388318e-01 -8.97730589e-02
3.09364229e-01 3.84403825e-01 1.50719538e-01 -6.61168396... | [11.089353561401367, 8.724143028259277] |
82b88843-119d-4d91-9610-2c25825b9e3e | improving-knowledge-aware-dialogue-generation | 1912.07491 | null | https://arxiv.org/abs/1912.07491v1 | https://arxiv.org/pdf/1912.07491v1.pdf | Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering | Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base... | ['Xiaojiang Liu', 'Jian Wang', 'Junhao Liu', 'Ruifeng Xu', 'Min Yang', 'Kejing He', 'Wei Bi'] | 2019-12-16 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 1.88013881e-01 7.65546739e-01 7.28142709e-02 -5.41679144e-01
-1.02861595e+00 -4.40699458e-01 9.07896876e-01 -2.78733134e-01
-1.99877203e-01 1.36629081e+00 9.01868045e-01 -2.62636274e-01
2.05490306e-01 -9.95195270e-01 -2.53947586e-01 -2.08995283e-01
5.51531434e-01 7.03941822e-01 3.36184772e-03 -9.76550341... | [12.444563865661621, 8.143044471740723] |
03ea203d-0cf5-4e17-b205-6ebf5f0d2471 | hiddensinger-high-quality-singing-voice | 2306.06814 | null | https://arxiv.org/abs/2306.06814v1 | https://arxiv.org/pdf/2306.06814v1.pdf | HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models | Recently, denoising diffusion models have demonstrated remarkable performance among generative models in various domains. However, in the speech domain, the application of diffusion models for synthesizing time-varying audio faces limitations in terms of complexity and controllability, as speech synthesis requires very... | ['Seong-Whan Lee', 'Sang-Hoon Lee', 'Ji-Sang Hwang'] | 2023-06-12 | null | null | null | null | ['speech-synthesis', 'singing-voice-synthesis'] | ['speech', 'speech'] | [ 7.39067867e-02 1.01103351e-01 3.73342521e-02 1.66335046e-01
-1.15141976e+00 -4.36870605e-01 1.65903822e-01 -8.60845625e-01
4.30271626e-01 3.91438127e-01 6.09268129e-01 1.26206696e-01
-1.73163131e-01 -7.66640365e-01 -6.11031294e-01 -9.68248487e-01
8.78614709e-02 2.69953042e-01 -1.46459833e-01 3.32963541... | [15.500258445739746, 6.177810192108154] |
4dd72201-08e4-4b90-9ee8-fcbe2a3e1138 | neural-coreference-resolution-based-on | 2212.09028 | null | https://arxiv.org/abs/2212.09028v1 | https://arxiv.org/pdf/2212.09028v1.pdf | Neural Coreference Resolution based on Reinforcement Learning | The target of a coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to solve two subtasks; one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention.... | ['Hongxia Jin', 'Yu Wang'] | 2022-12-18 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-1.93360418e-01 7.49524176e-01 -3.52070540e-01 -4.30604607e-01
-1.39456630e+00 -3.74381244e-01 3.49112749e-01 1.37795001e-01
-4.69526947e-01 8.89557660e-01 5.95257223e-01 -1.56903908e-01
-1.47473395e-01 -7.00321198e-01 -7.26537406e-01 -5.27427793e-01
-9.08300057e-02 1.31761420e+00 1.39246270e-01 -4.41692442... | [9.299397468566895, 9.533286094665527] |
d5d63d24-ddf6-46a9-97d2-ab533cd194f2 | a-method-for-detection-of-small-moving | null | null | https://www.mdpi.com/2072-4292/13/4/653 | https://www.mdpi.com/2072-4292/13/4/653/pdf | A Method for Detection of Small Moving Objects in UAV Videos | Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the l... | ['and Zdenka Babić', 'Nikola Kezić', 'Janja Filipi', 'Vedran Jovanović', 'Mario Muštra', 'Vladimir Risojević', 'Vladan Stojnić'] | 2021-02-11 | null | null | null | null | ['video-stabilization', 'small-object-detection', 'segmentation-of-remote-sensing-imagery'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.65771163e-01 -2.91842490e-01 2.20063299e-01 -8.15240294e-02
3.02218914e-01 -6.39391303e-01 4.00001585e-01 1.11935131e-01
-8.23079407e-01 6.86403871e-01 -7.21507013e-01 -2.57765979e-01
1.44230556e-02 -9.12128270e-01 -5.67717433e-01 -7.80286729e-01
-3.29667956e-01 1.39737949e-01 8.46009195e-01 -7.99359456... | [8.702872276306152, -0.8157316446304321] |
e156f9de-68a9-4ea5-97e3-ae25dd249fa6 | context-based-roman-urdu-to-urdu-script | 2109.14197 | null | https://arxiv.org/abs/2109.14197v1 | https://arxiv.org/pdf/2109.14197v1.pdf | Context based Roman-Urdu to Urdu Script Transliteration System | Now a day computer is necessary for human being and it is very useful in many fields like search engine, text processing, short messaging services, voice chatting and text recognition. Since last many years there are many tools and techniques that have been developed to support the writing of language script. Most of t... | ['Muhammad Waheed', 'Rashid Khan', 'H Muhammad Shakeel'] | 2021-09-29 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 7.43068457e-02 -5.42905509e-01 -9.92730334e-02 -2.88815707e-01
9.27238837e-02 -1.11585367e+00 4.00207192e-01 -2.87665427e-02
-3.32176536e-01 8.63438368e-01 2.13327840e-01 -8.79514515e-01
7.72877187e-02 -8.23554754e-01 -1.61606036e-02 -1.93314552e-01
6.56568468e-01 7.25506723e-01 3.96425426e-01 -6.25355482... | [10.65872859954834, 10.498636245727539] |
938df728-440a-45f6-8dc4-67a9ea1877b2 | the-effect-of-the-loss-on-generalization | 2108.04815 | null | https://arxiv.org/abs/2108.04815v1 | https://arxiv.org/pdf/2108.04815v1.pdf | The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data | Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss function in such tasks, a growing number of approaches have turned to a family of contrastive learning-based losses. Even though performanc... | ['Julia A. Schnabel', 'Ben Glocker', 'Sujal Desai', 'Arjun Nair', 'Kyriaki-Margarita Bintsi', 'Octavio E. Martinez Manzanera', 'Sam Ellis', 'Loic Le Folgoc', 'Vasileios Baltatzis'] | 2021-08-10 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.78046906e-01 -1.83760509e-04 -4.26122844e-01 -6.37946188e-01
-6.51334226e-01 -3.72970730e-01 3.28617245e-01 3.34335059e-01
-6.83134675e-01 6.98729992e-01 -2.25079626e-01 -3.58368635e-01
-2.57407188e-01 -6.54447556e-01 -5.52129209e-01 -9.34699297e-01
-5.97720817e-02 2.52849102e-01 2.49611527e-01 2.18538821... | [14.966662406921387, -2.4610869884490967] |
36d2bcc3-02e2-4d4b-ae42-53f0883cd127 | partial-matrix-completion | 2208.12063 | null | https://arxiv.org/abs/2208.12063v1 | https://arxiv.org/pdf/2208.12063v1.pdf | Partial Matrix Completion | In the matrix completion problem, one wishes to reconstruct a low-rank matrix based on a revealed set of (possibly noisy) entries. Prior work considers completing the entire matrix, which may be highly inaccurate in the common case where the distribution over entries is non-uniform. We formalize the problem of Partial ... | ['Adam Tauman Kalai', 'Elad Hazan', 'Varun Kanade'] | 2022-08-25 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.37234974e-01 2.06259429e-01 -6.59678951e-02 8.38509873e-02
-1.30306852e+00 -1.18750191e+00 2.11944625e-01 -4.61191051e-02
-1.23375133e-01 8.81328464e-01 5.18784225e-01 -2.02005535e-01
-3.88242036e-01 -4.21104133e-01 -9.82636333e-01 -8.67984474e-01
-2.46513322e-01 1.01961899e+00 -4.10971671e-01 -1.09141298... | [6.935223579406738, 4.7225518226623535] |
26e49297-4c42-48fc-a2a3-f31cc371e4d2 | transferring-hierarchical-structure-with-dual | null | null | https://openreview.net/forum?id=t3E10H8UNz | https://openreview.net/pdf?id=t3E10H8UNz | Transferring Hierarchical Structure with Dual Meta Imitation Learning | Hierarchical Imitation learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations. However, the learned hierarchical structure lacks the mechanism to transfer across multi-tasks or to new tasks, which makes them have to learn from scratch when facing a new situation. ... | ['Feng Chen', 'Yizhou Jiang', 'Chongkai Gao'] | 2021-09-29 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [-8.76857638e-02 1.43419221e-01 -8.05082768e-02 -1.04618460e-01
-4.40088451e-01 -1.83462992e-01 5.94266474e-01 -3.85139972e-01
-6.00938320e-01 8.94608736e-01 -7.16721406e-03 2.10387364e-01
-1.52902797e-01 -5.65961063e-01 -1.14484739e+00 -7.67155111e-01
-2.38134131e-01 5.41123629e-01 6.63962603e-01 -2.57235855... | [4.366684913635254, 1.166095495223999] |
a018e0a2-e8f1-47be-ac04-3ed00988b4c7 | csvideonet-a-real-time-end-to-end-learning | 1612.05203 | null | http://arxiv.org/abs/1612.05203v5 | http://arxiv.org/pdf/1612.05203v5.pdf | CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing | This paper addresses the real-time encoding-decoding problem for
high-frame-rate video compressive sensing (CS). Unlike prior works that perform
reconstruction using iterative optimization-based approaches, we propose a
non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse
mapping of CS and re... | ['Kai Xu', 'Fengbo Ren'] | 2016-12-15 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 2.94730932e-01 -2.61330783e-01 2.95703225e-02 -1.44359946e-01
-7.41993725e-01 -1.91709548e-01 2.64565915e-01 -4.45597053e-01
-4.43593502e-01 3.51029992e-01 4.00663525e-01 -5.05314410e-01
1.36560932e-01 -4.73161399e-01 -1.01874089e+00 -4.55279261e-01
-2.27892026e-01 -2.80562967e-01 8.28457549e-02 -1.26393944... | [11.143889427185059, -2.035737991333008] |
5e37c71e-6484-4e35-94df-9c97abd2880f | probabilistic-metamodels-for-an-efficient | 2110.02892 | null | https://arxiv.org/abs/2110.02892v3 | https://arxiv.org/pdf/2110.02892v3.pdf | Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios | To validate the safety of automated vehicles (AV), scenario-based testing aims to systematically describe driving scenarios an AV might encounter. In this process, continuous inputs such as velocities result in an infinite number of possible variations of a scenario. Thus, metamodels are used to perform analyses or to ... | ['Steffen Müller', 'Hadj Hamma Tadjine', 'Mike Kohlhoff', 'Max Winkelmann'] | 2021-10-06 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.59828249e-02 -4.02414501e-02 1.76454857e-02 -2.50747025e-01
-2.73475498e-01 -4.76280451e-01 7.72432983e-01 2.50599474e-01
-1.48435324e-01 8.31939697e-01 -5.83055198e-01 -7.02114344e-01
-6.95922315e-01 -1.14165843e+00 -5.65385938e-01 -7.95197785e-01
-1.13270506e-01 7.99827635e-01 6.57783091e-01 -4.83076572... | [5.7231597900390625, 1.4301674365997314] |
8856739d-a7ce-4e0c-b140-b1da8d55aa4d | screen-content-image-segmentation-using-least | 1501.03755 | null | http://arxiv.org/abs/1501.03755v2 | http://arxiv.org/pdf/1501.03755v2.pdf | Screen Content Image Segmentation Using Least Absolute Deviation Fitting | We propose an algorithm for separating the foreground (mainly text and line
graphics) from the smoothly varying background in screen content images. The
proposed method is designed based on the assumption that the background part of
the image is smoothly varying and can be represented by a linear combination of
a few s... | ['Yao Wang', 'Shervin Minaee'] | 2015-01-15 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.37317860e-01 -3.75006348e-01 -1.67270988e-01 -7.45600015e-02
-2.70894796e-01 -6.52154088e-01 4.83324438e-01 -2.30023399e-01
7.68373534e-02 4.75339323e-01 -2.08039716e-01 -4.83766258e-01
2.34511480e-01 -5.75662136e-01 -2.40511462e-01 -9.33684886e-01
3.97155851e-01 4.61804956e-01 1.02513528e+00 3.84544954... | [8.974955558776855, -0.8172535300254822] |
af0740b4-11e3-45ad-b7f9-33c47d6aea70 | conveying-the-predicted-future-to-users-a | 2302.09122 | null | https://arxiv.org/abs/2302.09122v1 | https://arxiv.org/pdf/2302.09122v1.pdf | Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction | Creative writing is hard: Novelists struggle with writer's block daily. While automatic story generation has advanced recently, it is treated as a "toy task" for advancing artificial intelligence rather than helping people. In this paper, we create a system that produces a short description that narrates a predicted pl... | ["Ting-Hao 'Kenneth' Huang", 'Kavya Laalasa Karanam', 'Saniya Naphade', 'Chieh-Yang Huang'] | 2023-02-17 | null | null | null | null | ['story-continuation', 'story-generation'] | ['computer-vision', 'natural-language-processing'] | [ 1.10346138e-01 5.06849647e-01 -1.48101896e-03 -3.36327642e-01
-8.65100145e-01 -8.58653069e-01 1.05933809e+00 -1.65658891e-01
8.68456215e-02 8.57224822e-01 8.44784617e-01 -1.14768185e-01
1.62773624e-01 -5.42920649e-01 -4.36048031e-01 1.66391637e-02
6.25571668e-01 6.89763188e-01 -6.31467327e-02 -4.88250285... | [11.750041961669922, 8.773791313171387] |
96aa59ec-a3e0-4d2b-aaaf-c75e1d954a78 | cardiac-mr-image-segmentation-techniques-an | 1502.04252 | null | http://arxiv.org/abs/1502.04252v1 | http://arxiv.org/pdf/1502.04252v1.pdf | Cardiac MR Image Segmentation Techniques: an overview | Broadly speaking, the objective in cardiac image segmentation is to delineate
the outer and inner walls of the heart to segment out either the entire or
parts of the organ boundaries. This paper will focus on MR images as they are
the most widely used in cardiac segmentation -- as a result of the accurate
morphological... | ['Tizita Nesibu Shewaye'] | 2015-02-14 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.89427948e-02 5.07007986e-02 -3.27187926e-02 -3.12265046e-02
4.26870845e-02 -6.46111965e-01 -6.70163473e-03 3.46790791e-01
-1.84971124e-01 7.99819648e-01 -4.27695699e-02 -2.93307453e-01
4.00246643e-02 -4.55439806e-01 2.34932810e-01 -8.82366776e-01
-1.30459264e-01 7.12421000e-01 3.49742949e-01 2.74489492... | [14.21518611907959, -2.5337109565734863] |
d464de57-b3f5-4dc7-ba4f-c8c013c3a8ff | memex-detecting-explanatory-evidence-for | 2305.15913 | null | https://arxiv.org/abs/2305.15913v2 | https://arxiv.org/pdf/2305.15913v2.pdf | MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization | Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication vehicle. To comprehend the subtle message conveyed within a meme, one must understand the background that facilitates its holistic assimilati... | ['Ramaneswaran S', 'Tanmoy Chakraborty', 'Md. Shad Akhtar', 'Udit Arora', 'Shivam Sharma'] | 2023-05-25 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-7.80301094e-02 -5.12411594e-02 -2.96129491e-02 -1.41891256e-01
-6.05361462e-01 -6.01414621e-01 1.15425754e+00 5.93390405e-01
-4.97040480e-01 6.47305727e-01 8.00391614e-01 -2.42367193e-01
4.36868519e-02 -6.23617411e-01 -6.31151736e-01 -2.31999218e-01
4.54880565e-01 1.53386280e-01 5.48903793e-02 -7.81926751... | [8.516389846801758, 10.69361686706543] |
5b48f174-bfb9-4d9a-8fc5-9247227e9473 | task-specific-fine-tuning-via-variational | 2303.08446 | null | https://arxiv.org/abs/2303.08446v1 | https://arxiv.org/pdf/2303.08446v1.pdf | Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification | While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification, such a paradigm still faces performance and generalization problems due to challenges in high computational costs on Gigapixel WSIs and limited sample size for model training. To deal with the... | ['Lin Yang', 'Sunyi Zheng', 'Wenwei Kuang', 'Zhongyi Shui', 'Yuxuan Sun', 'Yunlong Zhang', 'Chenglu Zhu', 'Honglin Li'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-instance-learning'] | ['methodology'] | [ 6.42942011e-01 1.97702259e-01 -5.48825443e-01 -4.83230412e-01
-1.28799379e+00 -2.42565930e-01 2.58876622e-01 2.26809829e-01
-5.52774549e-01 8.70077193e-01 1.27557993e-01 -2.52686381e-01
-5.36554873e-01 -6.52870119e-01 -5.44710696e-01 -1.09927297e+00
1.54543340e-01 4.99687970e-01 2.06324607e-01 -1.45806015... | [15.097735404968262, -2.7876036167144775] |
ff7f31dd-c7da-45d7-8251-d16659019924 | decomposed-cross-modal-distillation-for-rgb | 2303.17285 | null | https://arxiv.org/abs/2303.17285v1 | https://arxiv.org/pdf/2303.17285v1.pdf | Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection | Temporal action detection aims to predict the time intervals and the classes of action instances in the video. Despite the promising performance, existing two-stream models exhibit slow inference speed due to their reliance on computationally expensive optical flow. In this paper, we introduce a decomposed cross-modal ... | ['Hyeran Byun', 'Dongyoon Wee', 'Minho Shim', 'Taeoh Kim', 'Pilhyeon Lee'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lee_Decomposed_Cross-Modal_Distillation_for_RGB-Based_Temporal_Action_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lee_Decomposed_Cross-Modal_Distillation_for_RGB-Based_Temporal_Action_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-localization'] | ['computer-vision'] | [ 2.61139154e-01 -1.63008779e-01 -6.26699328e-01 -1.51936084e-01
-6.53961420e-01 -5.81309021e-01 7.56856203e-01 -2.18702987e-01
-3.84025156e-01 5.29335856e-01 5.27298927e-01 3.27399187e-02
-1.31197974e-01 -5.35945117e-01 -5.88218987e-01 -9.54515636e-01
-6.62236139e-02 -2.12975502e-01 3.18780363e-01 -3.10528018... | [8.33189582824707, 0.435358464717865] |
868d211a-ac43-41b5-abbc-d5a6fc641e6c | a2dele-adaptive-and-attentive-depth-distiller | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.pdf | A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection | Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data. This inevitably incurs extra computational costs and memory consumption, and using depth data during testing may hinder the prac... | [' Huchuan Lu', ' Weisong Ren', ' Miao Zhang', ' Zhengkun Rong', 'Yongri Piao'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.36942017e-01 3.73244464e-01 -4.72811945e-02 -2.07611576e-01
-3.87391537e-01 -1.73724473e-01 2.74619550e-01 -5.29399179e-02
-3.60147417e-01 3.11804920e-01 4.89643076e-03 -4.19938266e-01
2.78945833e-01 -8.95497322e-01 -8.19670260e-01 -6.29864872e-01
1.69830233e-01 -9.75932628e-02 9.71533239e-01 -2.09556252... | [9.648853302001953, -0.8406720757484436] |
dd630f06-4ed9-421b-b8a9-d29b65bcd843 | visual-chirality-1 | 2006.09512 | null | https://arxiv.org/abs/2006.09512v1 | https://arxiv.org/pdf/2006.09512v1.pdf | Visual Chirality | How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual dat... | ['Abe Davis', 'Zhiqiu Lin', 'Jin Sun', 'Noah Snavely'] | 2020-06-16 | visual-chirality | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Visual_Chirality_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Visual_Chirality_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-forensics'] | ['computer-vision'] | [ 8.09553266e-01 1.42454207e-01 3.86700071e-02 -4.09980625e-01
1.74763575e-01 -6.67520702e-01 1.21029353e+00 -1.67858839e-01
-2.69103140e-01 2.30581731e-01 7.67556012e-01 -3.66204083e-01
6.07878938e-02 -5.28674841e-01 -7.66983986e-01 -6.89882398e-01
-1.77414745e-01 2.24548906e-01 -1.70262128e-01 -2.25473836... | [11.500053405761719, 0.7838843464851379] |
0033e3d6-f9d8-4726-b7c9-0e8e400594e8 | 191111236 | 1911.11236 | null | https://arxiv.org/abs/1911.11236v3 | https://arxiv.org/pdf/1911.11236v3.pdf | RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds | We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net,... | ['Andrew Markham', 'Zhihua Wang', 'Niki Trigoni', 'Bo Yang', 'Stefano Rosa', 'Yulan Guo', 'Qingyong Hu', 'Linhai Xie'] | 2019-11-25 | randla-net-efficient-semantic-segmentation-of | http://openaccess.thecvf.com/content_CVPR_2020/html/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.pdf | cvpr-2020-6 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 1.19688243e-01 -2.78986245e-02 7.62692988e-02 -4.60734546e-01
-9.07447577e-01 -6.09905958e-01 4.45321560e-01 4.00039673e-01
-5.84926426e-01 2.10730523e-01 -3.33429486e-01 -3.37403804e-01
1.54244587e-01 -1.13062024e+00 -1.15266788e+00 -3.01440686e-01
5.91978319e-02 8.99713814e-01 8.62618327e-01 -8.59337598... | [7.965831279754639, -3.3206868171691895] |
d73458a4-fca9-4345-947b-bcca14ed6f6e | gnn3dmot-graph-neural-network-for-3d-multi | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.pdf | GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning | 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses a standard tracking-by-detection pipeline, where feature extraction is first performed independently for each object in order to compute an affinity matrix. Then the affinity matrix is passed to the Hungarian algorithm for data associatio... | [' Kris M. Kitani', ' Yunze Man', ' Yongxin Wang', 'Xinshuo Weng'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 3.83705385e-02 -4.43338424e-01 -5.60053475e-02 -2.35904768e-01
-4.94209796e-01 -5.29311299e-01 5.31911314e-01 -6.00099415e-02
-4.04720098e-01 4.30908978e-01 -1.96901575e-01 5.46445660e-02
-7.65400305e-02 -7.26410747e-01 -7.11769879e-01 -9.70710397e-01
1.09140880e-01 6.73344493e-01 7.94969440e-01 1.18341066... | [6.439352035522461, -2.2244467735290527] |
19863a09-b6db-4579-986b-01bc20f300bd | grammatical-error-annotation-for-korean | null | null | https://aclanthology.org/L12-1035 | https://aclanthology.org/L12-1035.pdf | Grammatical Error Annotation for Korean Learners of Spoken English | The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus. We collected raw story-telling speech from Korean university students. Transcription and annotation using the Cambridge Learner Corpus tagset were performed by six Korean annotator... | ['Hae-Ri Kim', 'Soo-Ok Kweon', 'Kyusong Lee', 'Gary Geunbae Lee', 'Hongsuck Seo'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.05050534e-01 3.00823718e-01 1.47736877e-01 -5.76591432e-01
-1.43851388e+00 -7.42070019e-01 1.37579501e-01 4.52546328e-01
-8.34715366e-01 1.16680741e+00 6.27821565e-01 -2.82298565e-01
3.13236922e-01 -4.12096500e-01 -6.57540262e-01 -2.43617594e-02
2.50542015e-01 4.57668155e-01 5.04129231e-01 -1.71610624... | [10.930191993713379, 10.484824180603027] |
068c10d0-b26e-49a5-9e27-05bdf8795a69 | learning-to-infer-counterfactuals-meta | 2208.06748 | null | https://arxiv.org/abs/2208.06748v1 | https://arxiv.org/pdf/2208.06748v1.pdf | Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects | We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observational studies are growing in significance in answering such questions due to their widespread accumulation and comparatively easier acquisiti... | ['Liming Zhu', 'Chen Wang', 'Xiwei Xu', 'Lina Yao', 'Guanglin Zhou'] | 2022-08-13 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.08273184e-01 3.50248590e-02 -1.36946273e+00 -5.72099447e-01
-9.81167853e-01 -2.87537184e-02 4.67750400e-01 5.90884462e-02
-4.78970826e-01 1.50225198e+00 6.21963859e-01 -4.59043622e-01
-4.65128392e-01 -8.07264030e-01 -9.00292575e-01 -6.62841558e-01
-3.39515544e-02 4.53512520e-01 -6.56951010e-01 1.42759219... | [8.081954002380371, 5.454188823699951] |
f4da690f-9679-4a9d-b82a-ffa1f9009e98 | a-benchmark-of-pdf-information-extraction | 2303.09957 | null | https://arxiv.org/abs/2303.09957v1 | https://arxiv.org/pdf/2303.09957v1.pdf | A Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents | Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements is difficult because many, technically diverse tools are available, but recent performance benchmarks are rare. Moreover, such benchmarks ty... | ['Bela Gipp', 'Jelena Mitrović', 'Timo Spinde', 'Apurva Jagdale', 'Norman Meuschke'] | 2023-03-17 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [-3.48856360e-01 -6.53312877e-02 -5.16439855e-01 3.48412544e-02
-1.32499707e+00 -1.26153612e+00 8.50706041e-01 4.85091865e-01
-3.88444930e-01 1.00144911e+00 4.05934721e-01 -5.44831455e-01
-5.84483564e-01 -7.90319860e-01 -7.57717490e-01 -9.96426120e-02
3.84545535e-01 5.87347567e-01 4.15231496e-01 2.38813281... | [9.587217330932617, 8.165563583374023] |
3d33e5a3-78f1-4f2f-b58d-bd46d1adc9b7 | tell-me-why-you-feel-that-way-processing | 2103.05815 | null | https://arxiv.org/abs/2103.05815v1 | https://arxiv.org/pdf/2103.05815v1.pdf | Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE) | Sentiment analysis has transitioned from classifying the sentiment of an entire sentence to providing the contextual information of what targets exist in a sentence, what sentiment the individual targets have, and what the causal words responsible for that sentiment are. However, this has led to elaborate requirements ... | ['S. Wermter', 'S. Magg', 'T. Hellström', 'S. Bensch', 'A. Sutherland'] | 2021-03-10 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 6.46643519e-01 4.10089076e-01 6.64220154e-02 -9.71890330e-01
-7.81241059e-01 -8.00079226e-01 8.52185488e-01 5.18294811e-01
-2.56509483e-01 8.31279814e-01 5.56012213e-01 -5.20633817e-01
-5.93112521e-02 -5.33630013e-01 -6.77478373e-01 -4.59405690e-01
1.23624668e-01 4.11508232e-01 6.55468106e-02 -4.07420307... | [11.223186492919922, 7.065085411071777] |
0f0500d1-ed3d-43ea-a69a-b10df5af20c4 | boundarycam-a-boundary-based-refinement | 2303.07853 | null | https://arxiv.org/abs/2303.07853v1 | https://arxiv.org/pdf/2303.07853v1.pdf | BoundaryCAM: A Boundary-based Refinement Framework for Weakly Supervised Semantic Segmentation of Medical Images | Weakly Supervised Semantic Segmentation (WSSS) with only image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of t... | ['Muhammad Shafique', 'Erik Ostrowski', 'Bharath Srinivas Prabakaran'] | 2023-03-14 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 6.47762477e-01 5.46611071e-01 -5.15823811e-02 -5.14578342e-01
-8.69457662e-01 -3.86846870e-01 3.77239048e-01 2.63338327e-01
-4.30977076e-01 2.97226071e-01 -2.94183522e-01 -2.37238079e-01
1.17073722e-01 -8.04780126e-01 -7.98511803e-01 -6.46326900e-01
1.00233369e-01 6.64276421e-01 1.03494477e+00 -2.67262489... | [9.596844673156738, 0.2791537940502167] |
a4569bbc-28f4-41f2-93cf-883215facbda | lg-hand-advancing-3d-hand-pose-estimation | 2211.03151 | null | https://arxiv.org/abs/2211.03151v1 | https://arxiv.org/pdf/2211.03151v1.pdf | LG-Hand: Advancing 3D Hand Pose Estimation with Locally and Globally Kinematic Knowledge | 3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand... | ['Thanh-Hai Tran', 'Thi Ngoc Hien Doan', 'Trung Tran-Quang', 'Tu Le-Xuan'] | 2022-11-06 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-3.65620792e-01 -1.74582094e-01 -4.37432766e-01 -7.00525865e-02
-5.03344178e-01 -4.78627831e-01 3.75150204e-01 -4.45461810e-01
-6.87585413e-01 6.18847251e-01 2.76499182e-01 -7.64099881e-02
-7.45978355e-02 -4.07290697e-01 -5.55903018e-01 -6.42535627e-01
-1.28458794e-02 4.61333543e-01 9.63556916e-02 -1.40897140... | [6.611468315124512, -0.7106606364250183] |
71fd30ef-3663-4882-86a2-ea2b01896198 | analysis-of-overfitting-in-the-regularized | 1904.06632 | null | https://arxiv.org/abs/1904.06632v2 | https://arxiv.org/pdf/1904.06632v2.pdf | Analysis of overfitting in the regularized Cox model | The Cox proportional hazards model is ubiquitous in the analysis of time-to-event data. However, when the data dimension p is comparable to the sample size $N$, maximum likelihood estimates for its regression parameters are known to be biased or break down entirely due to overfitting. This prompted the introduction of ... | ['M Sheikh', 'A. C. C. Coolen'] | 2019-04-14 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.47136658e-01 2.22390257e-02 -3.64460081e-01 -3.49983811e-01
-1.06894672e+00 3.29252961e-03 4.75419134e-01 5.22205055e-01
-5.93284369e-01 1.02822840e+00 -2.52934173e-02 -3.38129252e-01
-4.05622065e-01 -8.34839582e-01 -6.30951405e-01 -8.96360338e-01
-5.98643005e-01 4.73890156e-01 2.17214704e-01 8.89424235... | [7.722892761230469, 4.821357727050781] |
45bc3e66-5f6e-4e14-94d0-d7c0a4692979 | instance-segmentation-of-fibers-from-low | 1901.01034 | null | http://arxiv.org/abs/1901.01034v1 | http://arxiv.org/pdf/1901.01034v1.pdf | Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning | We propose a novel approach for automatic extraction (instance segmentation)
of fibers from low resolution 3D X-ray computed tomography scans of short glass
fiber reinforced polymers. We have designed a 3D instance segmentation
architecture built upon a deep fully convolutional network for semantic
segmentation with an... | ['Jürgen Hesser', 'Thorben Kröger', 'Tomasz Konopczyński', 'Lei Zheng'] | 2019-01-04 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.83389413e-01 5.11587083e-01 1.98693901e-01 -4.15812969e-01
-4.89166081e-01 -3.12751710e-01 3.10472429e-01 4.80735362e-01
-4.04295772e-01 2.62337059e-01 -1.96531221e-01 -2.21361995e-01
-5.10336876e-01 -1.27988183e+00 -9.86507714e-01 -7.37568736e-01
-5.63989043e-01 1.17705131e+00 4.72362161e-01 -3.51692252... | [14.163503646850586, -2.739170551300049] |
ff9b9ae7-85e5-4032-a317-dfbfd9b5522b | modular-visual-question-answering-via-code | 2306.05392 | null | https://arxiv.org/abs/2306.05392v1 | https://arxiv.org/pdf/2306.05392v1.pdf | Modular Visual Question Answering via Code Generation | We present a framework that formulates visual question answering as modular code generation. In contrast to prior work on modular approaches to VQA, our approach requires no additional training and relies on pre-trained language models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA examples used... | ['Dan Klein', 'Trevor Darrell', 'Andy Zeng', 'Cordelia Schmid', 'Arsha Nagrani', 'Kevin Yang', 'Kushal Khangaonkar', 'Medhini Narasimhan', 'Sanjay Subramanian'] | 2023-06-08 | null | null | null | null | ['code-generation', 'visual-question-answering-1'] | ['computer-code', 'computer-vision'] | [ 1.37240112e-01 4.54725891e-01 2.29250982e-01 -4.18351620e-01
-1.41013694e+00 -8.43247533e-01 8.74715030e-01 1.35762304e-01
-1.44256577e-01 4.46035922e-01 1.59456253e-01 -7.37359226e-01
6.45042181e-01 -8.45730722e-01 -1.17515707e+00 2.81477664e-02
4.23134059e-01 5.19880891e-01 4.55895782e-01 -4.08342987... | [10.864350318908691, 1.8184727430343628] |
5c0bcef3-3c1a-45e8-8538-2693fc3ce48e | when-newer-is-not-better-does-deep-learning | 2305.01801 | null | https://arxiv.org/abs/2305.01801v1 | https://arxiv.org/pdf/2305.01801v1.pdf | When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback? | In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the reported state-of-the-art results of many neural recommendation models cannot be reliably replicated. A primary reason is that existing evalu... | ['Tobias Schnabel', 'Jundong Li', 'Yushun Dong'] | 2023-05-02 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-3.81733738e-02 -5.05328059e-01 -6.03517830e-01 -4.27047968e-01
-3.56685400e-01 -6.09179854e-01 5.18348396e-01 -9.49475318e-02
-4.71395731e-01 6.68326139e-01 6.16924345e-01 -6.07929111e-01
-6.96102381e-01 -7.61787891e-01 -6.98095739e-01 -5.11129200e-01
-1.17731623e-01 3.97662252e-01 2.67963838e-02 -5.85353434... | [10.096728324890137, 5.6659016609191895] |
72f619d6-30c0-4687-b958-e17da33e8c8d | a-gromov-wasserstein-geometric-view-of | 2306.08854 | null | https://arxiv.org/abs/2306.08854v1 | https://arxiv.org/pdf/2306.08854v1.pdf | A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening | Graph coarsening is a technique for solving large-scale graph problems by working on a smaller version of the original graph, and possibly interpolating the results back to the original graph. It has a long history in scientific computing and has recently gained popularity in machine learning, particularly in methods t... | ['Jie Chen', 'Yun Yang', 'Rentian Yao', 'Yifan Chen'] | 2023-06-15 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 1.44940332e-01 2.50811100e-01 -9.05348957e-02 -1.55599609e-01
-1.67502418e-01 -3.72009754e-01 2.21857920e-01 4.59189832e-01
-2.42227808e-01 4.81941491e-01 -3.62919234e-02 -1.75154090e-01
-3.90195012e-01 -1.17560959e+00 -6.28793776e-01 -7.84215569e-01
-4.57738131e-01 2.50382662e-01 1.55810192e-01 -1.92851260... | [7.134591579437256, 5.50976037979126] |
22ad219a-c50c-4f40-b9dd-e4a652316733 | more-robust-schema-guided-dialogue-state | 2303.09905 | null | https://arxiv.org/abs/2303.09905v1 | https://arxiv.org/pdf/2303.09905v1.pdf | More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking | The schema-guided paradigm overcomes scalability issues inherent in building task-oriented dialogue (TOD) agents with static ontologies. Instead of operating on dialogue context alone, agents have access to hierarchical schemas containing task-relevant natural language descriptions. Fine-tuned language models excel at ... | ['B. Byrne', 'W. Lin', 'B. H. Tseng', 'A. Coca'] | 2023-03-17 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.92423844e-01 8.94119620e-01 -1.60276070e-01 -6.29397392e-01
-9.39507544e-01 -8.39438677e-01 1.42309868e+00 1.64791420e-01
-5.30795753e-01 8.93376529e-01 9.02166963e-01 -8.46624821e-02
-6.03504293e-02 -7.15872765e-01 -2.51907766e-01 -2.49855164e-02
1.83483973e-01 1.07954001e+00 3.69367748e-01 -1.17098773... | [12.770879745483398, 7.975526332855225] |
17949131-1012-443f-86e7-2108bc42c6f4 | targeted-adversarial-attacks-against-neural-1 | 2303.01068 | null | https://arxiv.org/abs/2303.01068v1 | https://arxiv.org/pdf/2303.01068v1.pdf | Targeted Adversarial Attacks against Neural Machine Translation | Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as adversarial attacks. In this paper, we propose a new targeted adversarial attack against NMT models. In particular, our goal is to insert a... | ['Pascal Frossard', 'Ljiljana Dolamic', 'AmirHossein Dabiri Aghdam', 'Sahar Sadrizadeh'] | 2023-03-02 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 6.59215927e-01 7.28831589e-02 2.46583432e-01 -1.16569676e-01
-9.75098550e-01 -1.19264555e+00 7.32977450e-01 -1.27043784e-01
-4.08307374e-01 8.38806093e-01 -2.06520576e-02 -5.17767966e-01
4.66782123e-01 -6.83727264e-01 -1.22641540e+00 -7.74754941e-01
3.49293530e-01 3.37951422e-01 2.89067142e-02 -5.14174879... | [6.052786827087402, 8.17674732208252] |
c85551bd-e3d3-48e3-9464-eb1b61f35e22 | second-order-democratic-aggregation | 1808.07503 | null | http://arxiv.org/abs/1808.07503v1 | http://arxiv.org/pdf/1808.07503v1.pdf | Second-order Democratic Aggregation | Aggregated second-order features extracted from deep convolutional networks
have been shown to be effective for texture generation, fine-grained
recognition, material classification, and scene understanding. In this paper,
we study a class of orderless aggregation functions designed to minimize
interference or equalize... | ['Tsung-Yu Lin', 'Subhransu Maji', 'Piotr Koniusz'] | 2018-08-22 | second-order-democratic-aggregation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Tsung-Yu_Lin_Second-order_Democratic_Aggregation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Tsung-Yu_Lin_Second-order_Democratic_Aggregation_ECCV_2018_paper.pdf | eccv-2018-9 | ['material-classification'] | ['computer-vision'] | [ 5.65498710e-01 1.52254596e-01 3.65418792e-02 -3.56787562e-01
-6.21431708e-01 -3.47178042e-01 1.05083585e+00 4.00606096e-01
-5.94298661e-01 6.42407060e-01 2.85095930e-01 -1.80261835e-01
-5.61125457e-01 -1.09242857e+00 -6.91330135e-01 -9.25399899e-01
-2.94060171e-01 8.80095735e-02 1.33374259e-01 -3.83483857... | [9.011884689331055, 2.4234886169433594] |
9714c1ca-0df8-49ae-b248-250b557ea2da | learning-a-pose-lexicon-for-semantic-action | 1604.00147 | null | http://arxiv.org/abs/1604.00147v1 | http://arxiv.org/pdf/1604.00147v1.pdf | Learning a Pose Lexicon for Semantic Action Recognition | This paper presents a novel method for learning a pose lexicon comprising
semantic poses defined by textual instructions and their associated visual
poses defined by visual features. The proposed method simultaneously takes two
input streams, semantic poses and visual pose candidates, and statistically
learns a mapping... | ['Lijuan Zhou', 'Philip Ogunbona', 'Wanqing Li'] | 2016-04-01 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 5.38231492e-01 -2.54708111e-01 -4.22292948e-01 -5.63321352e-01
-8.63857627e-01 -4.87163097e-01 6.89749122e-01 -3.30346942e-01
-6.09571815e-01 4.06259418e-01 5.32670438e-01 3.55123490e-01
-3.09454888e-01 -2.81015635e-01 -7.40172267e-01 -5.49615443e-01
-2.78829336e-01 6.15047455e-01 3.56047213e-01 1.49819553... | [8.32082462310791, 0.6803924441337585] |
56b69c3b-3f7c-4a7b-ac95-8e6c40100e4a | uncertainty-aware-lidar-panoptic-segmentation | 2210.04472 | null | https://arxiv.org/abs/2210.04472v1 | https://arxiv.org/pdf/2210.04472v1.pdf | Uncertainty-aware LiDAR Panoptic Segmentation | Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncert... | ['Wolfram Burgard', 'Daniel Büscher', 'Sajad Marvi', 'Kshitij Sirohi'] | 2022-10-10 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-1.37363598e-01 -1.28528491e-01 -2.16869533e-01 -7.70406961e-01
-1.21227109e+00 -4.88265753e-01 5.39096177e-01 6.20153919e-02
-4.21308398e-01 9.13020372e-01 -5.09021103e-01 -4.03290778e-01
-2.75082648e-01 -1.11203218e+00 -9.27601039e-01 -6.08312249e-01
1.15558974e-01 1.05576491e+00 4.95080978e-01 -1.41519669... | [8.114096641540527, -2.5367250442504883] |
750f390a-7f53-4d03-8a7f-bc9d150b647b | sar-image-despeckling-using-a-convolutional | 1706.00552 | null | http://arxiv.org/abs/1706.00552v2 | http://arxiv.org/pdf/1706.00552v2.pdf | SAR Image Despeckling Using a Convolutional Neural Network | Synthetic Aperture Radar (SAR) images are often contaminated by a
multiplicative noise known as speckle. Speckle makes the processing and
interpretation of SAR images difficult. We propose a deep learning-based
approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for
automatically removing speckle ... | ['He Zhang', 'Vishal M. Patel', 'Puyang Wang'] | 2017-06-02 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 7.34205246e-01 -2.99911439e-01 7.38259792e-01 -7.32685983e-01
-9.15957093e-01 -2.48211369e-01 3.29028517e-01 -7.84046412e-01
-5.49310923e-01 6.13090098e-01 2.12790221e-01 1.25800807e-03
-3.13113779e-01 -6.47291183e-01 -6.52603030e-01 -1.13066387e+00
1.51535235e-02 -5.91677204e-02 -9.75498408e-02 -9.99374762... | [10.488554954528809, -2.266045570373535] |
713eca9b-7ba6-4df1-9693-f9a27d924532 | beyond-co2-emissions-the-overlooked-impact-of | 2306.16668 | null | https://arxiv.org/abs/2306.16668v1 | https://arxiv.org/pdf/2306.16668v1.pdf | Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models | As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search. This interest has become particularly relevant as the energy consumption of information retrieval models has risen ... | ['Shengyao Zhuang', 'Harrisen Scells', 'Guido Zuccon'] | 2023-06-29 | null | null | null | null | ['retrieval', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [ 1.76260293e-01 5.50569668e-02 -5.01707971e-01 1.43959597e-01
-3.56505632e-01 -4.57305640e-01 9.22895730e-01 6.90699637e-01
-7.89382577e-01 4.85807538e-01 3.04394979e-02 -5.35616577e-01
-5.08983076e-01 -9.02771115e-01 -3.76537442e-01 -4.29911375e-01
-3.00374806e-01 2.90460527e-01 -7.73513690e-02 7.27799833... | [11.034448623657227, 7.672529697418213] |
4ab956e4-e4ca-450b-9594-0f583d1aa75f | self-interpretable-time-series-prediction | 2306.06024 | null | https://arxiv.org/abs/2306.06024v3 | https://arxiv.org/pdf/2306.06024v3.pdf | Self-Interpretable Time Series Prediction with Counterfactual Explanations | Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving. Most existing methods focus on interpreting predictions by assigning important scores to segments of time series. In this paper, we take a different and more challenging route and aim at developing a sel... | ['Hao Wang', 'Jingquan Yan'] | 2023-06-09 | null | null | null | null | ['counterfactual-inference', 'time-series-prediction'] | ['miscellaneous', 'time-series'] | [ 5.96061826e-01 8.64182353e-01 -5.92203856e-01 -7.34335482e-01
-6.29761219e-01 -2.07043350e-01 9.96359646e-01 5.49703697e-03
6.49607033e-02 1.13625479e+00 8.93970191e-01 -8.73108625e-01
-3.26434970e-01 -6.70665920e-01 -9.01474476e-01 -2.58310109e-01
-2.98106492e-01 6.27622724e-01 -3.49287838e-01 -3.81074362... | [8.704639434814453, 5.614113807678223] |
af2eb9d1-47fe-4e12-966f-4df054d27142 | 3d-guided-weakly-supervised-semantic | 2012.00242 | null | https://arxiv.org/abs/2012.00242v1 | https://arxiv.org/pdf/2012.00242v1.pdf | 3D Guided Weakly Supervised Semantic Segmentation | Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain. In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with adv... | ['Nick Barnes', 'Jing Zhang', 'Weixuan Sun'] | 2020-12-01 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.68237668e-01 5.25942981e-01 -3.14342290e-01 -5.74432254e-01
-8.00657511e-01 -6.24309301e-01 3.26829374e-01 9.32579488e-02
-2.52307028e-01 2.97156632e-01 -3.61526668e-01 -1.00995056e-01
2.92982817e-01 -8.10130239e-01 -8.35128844e-01 -4.57552671e-01
2.10253417e-01 9.08916116e-01 9.71254587e-01 8.24553519... | [8.030277252197266, -3.147217273712158] |
ce254f58-7651-4495-b5d0-4bc06bf6eecb | omnivl-one-foundation-model-for-image | 2209.07526 | null | https://arxiv.org/abs/2209.07526v2 | https://arxiv.org/pdf/2209.07526v2.pdf | OmniVL:One Foundation Model for Image-Language and Video-Language Tasks | This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. It adopts a unified transformer-based visual encoder for both image and video inputs, and thus can perform joint image-language and video-language pretraining. We demonstrate, for... | ['Lu Yuan', 'Yu-Gang Jiang', 'Ce Liu', 'Yujia Xie', 'Yucheng Zhao', 'Luowei Zhou', 'Chong Luo', 'Zuxuan Wu', 'Dongdong Chen', 'Junke Wang'] | 2022-09-15 | null | null | null | null | ['video-text-retrieval', 'action-classification', 'video-question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.79042959e-01 -1.31364614e-01 -4.61565912e-01 -3.85482788e-01
-1.05569613e+00 -7.46622086e-01 7.75197029e-01 -2.79556066e-01
-7.14403570e-01 3.76200140e-01 3.34189236e-02 -4.53778923e-01
5.21136403e-01 -4.25367087e-01 -1.27909315e+00 -5.03286958e-01
5.19543767e-01 2.56298333e-01 1.35455757e-01 1.00088388... | [10.349349021911621, 0.9884293079376221] |
0fc2a840-1ee0-4c13-b052-4dd29722aba8 | spatial-econometrics-for-misaligned-data | 2207.04082 | null | https://arxiv.org/abs/2207.04082v1 | https://arxiv.org/pdf/2207.04082v1.pdf | Spatial Econometrics for Misaligned Data | We produce methodology for regression analysis when the geographic locations of the independent and dependent variables do not coincide, in which case we speak of misaligned data. We develop and investigate two complementary methods for regression analysis with misaligned data that circumvent the need to estimate or sp... | ['Guillaume Allaire Pouliot'] | 2022-07-08 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.29561859e-01 -1.78404614e-01 -6.14618897e-01 -3.54485989e-01
-4.69666362e-01 -7.58230925e-01 8.63517165e-01 -4.98143658e-02
-5.97540140e-01 9.74071085e-01 4.45303053e-01 -9.07844901e-01
-5.16886115e-01 -5.03888011e-01 -5.03402829e-01 -5.15866756e-01
5.01209535e-02 -5.41592166e-02 -4.44130361e-01 -7.51504526... | [7.878166675567627, 5.125662326812744] |
7bb35561-a6e5-4d77-8fec-c037a4f3c937 | machine-translation-from-signed-to-spoken | 2202.03086 | null | https://arxiv.org/abs/2202.03086v4 | https://arxiv.org/pdf/2202.03086v4.pdf | Machine Translation from Signed to Spoken Languages: State of the Art and Challenges | Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics. Nevertheless, research in this domain is performed mostly by computer scientists in isolation. As the domain is becoming increasingly popular ... | ['Joni Dambre', 'Mieke Van Herreweghe', 'Dimitar Shterionov', 'Mathieu De Coster'] | 2022-02-07 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 2.77578294e-01 -8.22449327e-02 -4.87199724e-01 -4.41402525e-01
-8.06130648e-01 -6.58867776e-01 5.87941766e-01 -4.49666917e-01
-4.40890282e-01 3.43102306e-01 7.55866826e-01 -5.50666988e-01
-8.76436755e-02 -1.81053922e-01 -2.09211200e-01 -2.03404903e-01
4.60059822e-01 5.13469219e-01 4.95164916e-02 -4.70046759... | [9.101709365844727, -6.406668663024902] |
d8074122-2dc2-4663-ae31-d7f84e53116b | environmental-sound-classification-on-the | 2103.03483 | null | https://arxiv.org/abs/2103.03483v4 | https://arxiv.org/pdf/2103.03483v4.pdf | Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices | Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support). Here, we demonstrate the first deep network for acoustic recognition that is small, flexible and compression-friendly yet achieves s... | ['Bernd Meyer', 'Ian Thomas West', 'Christoph Bergmeir', 'Md Mohaimenuzzaman'] | 2021-03-05 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 4.74017747e-02 -8.41354951e-02 8.71095136e-02 -2.96781301e-01
-9.29910660e-01 -2.71716297e-01 -1.31049184e-02 -9.54051390e-02
-4.07175899e-01 3.14974040e-01 -1.36666983e-01 -4.62960362e-01
6.21235259e-02 -6.66765153e-01 -5.82867265e-01 -3.81668210e-01
-3.97219688e-01 8.12817551e-03 1.28517225e-01 7.06663653... | [14.583333969116211, 5.513881683349609] |
62193e35-3427-4974-85a3-2c802dc736d9 | textir-a-simple-framework-for-text-based | 2302.14736 | null | https://arxiv.org/abs/2302.14736v1 | https://arxiv.org/pdf/2302.14736v1.pdf | TextIR: A Simple Framework for Text-based Editable Image Restoration | Most existing image restoration methods use neural networks to learn strong image-level priors from huge data to estimate the lost information. However, these works still struggle in cases when images have severe information deficits. Introducing external priors or using reference images to provide information also hav... | ['Zhi Wang', 'Chun Yuan', 'Chao Dong', 'Shuzhao Xie', 'Cairong Wang', 'Yunpeng Bai'] | 2023-02-28 | null | null | null | null | ['colorization', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 5.11227608e-01 -3.98501217e-01 -2.02751309e-01 -3.56954575e-01
-4.77951974e-01 -1.31215677e-01 1.31143823e-01 -4.52939779e-01
-2.30343372e-01 6.85339749e-01 4.02943075e-01 1.56090841e-01
9.23124328e-02 -7.55108237e-01 -5.84484577e-01 -8.00756454e-01
6.65825605e-01 -2.51903802e-01 1.86467007e-01 -3.15763593... | [11.215758323669434, -1.9128596782684326] |
83b39ae2-ec79-4ebe-877e-ad7b4278119d | convgru-in-fine-grained-pitching-action | 2008.07819 | null | https://arxiv.org/abs/2008.07819v1 | https://arxiv.org/pdf/2008.07819v1.pdf | ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction | Prediction of the action outcome is a new challenge for a robot collaboratively working with humans. With the impressive progress in video action recognition in recent years, fine-grained action recognition from video data turns into a new concern. Fine-grained action recognition detects subtle differences of actions i... | ['Ou Ma', 'Lin Zhang', 'Xiumin Diao', 'Tianqi Ma'] | 2020-08-18 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 5.21808386e-01 -3.42656404e-01 -1.20052293e-01 -3.91272038e-01
-5.34241974e-01 7.88246766e-02 6.87017858e-01 -1.57945946e-01
-4.69329745e-01 8.96106184e-01 6.54270589e-01 2.95056850e-01
-3.96330237e-01 -6.85827136e-01 -5.54753959e-01 -8.67300272e-01
-1.40759245e-01 3.24766338e-01 5.65799534e-01 -2.07792744... | [8.171290397644043, 0.5872477293014526] |
515494df-165c-475d-b65b-d1ee25a101b0 | temporal-cue-guided-video-highlight-detection | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ye_Temporal_Cue_Guided_Video_Highlight_Detection_With_Low-Rank_Audio-Visual_Fusion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ye_Temporal_Cue_Guided_Video_Highlight_Detection_With_Low-Rank_Audio-Visual_Fusion_ICCV_2021_paper.pdf | Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion | Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i.e., where the highlight moments are in long videos) for supervised learning. ... | ['Guang Yang', 'Ping Li', 'Qi Bi', 'ZiRui Wang', 'Yuan Gao', 'Xiyue Shen', 'Qinghao Ye'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 1.85100853e-01 -4.30536568e-01 -3.05254519e-01 -9.89029258e-02
-1.02596700e+00 -4.71725047e-01 3.49808365e-01 3.72096837e-01
-3.43351126e-01 4.31760460e-01 4.86550540e-01 2.37942189e-01
-1.26673743e-01 -1.99638650e-01 -7.89068997e-01 -7.15891004e-01
-4.99056786e-01 -6.02964103e-01 4.80654567e-01 9.22617018... | [10.11257266998291, 0.47619274258613586] |
559a2f8a-a6dc-4b4a-b701-89625489bb2b | pingan-vcgroup-s-solution-for-icdar-2021 | 2105.01848 | null | https://arxiv.org/abs/2105.01848v1 | https://arxiv.org/pdf/2105.01848v1.pdf | PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML | This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure r... | ['Rong Xiao', 'Peng Gao', 'Dengyi Gu', 'Yihao Chen', 'Yelin He', 'Xianbiao Qi', 'Jiaquan Ye'] | 2021-05-05 | null | null | null | null | ['table-recognition', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 3.42719346e-01 1.38027176e-01 -6.25078976e-01 -2.72471875e-01
-1.24009144e+00 -8.30218375e-01 2.54002482e-01 4.40319866e-01
-2.43721917e-01 5.19253671e-01 -2.50382423e-02 -2.96958894e-01
3.07588816e-01 -6.36997700e-01 -9.31180954e-01 -1.90673679e-01
6.01840138e-01 7.14353204e-01 3.47181827e-01 3.83987576... | [11.697680473327637, 3.0489230155944824] |
86e2f62a-7681-498d-a632-47dcc58f762d | train-short-test-long-attention-with-linear | 2108.12409 | null | https://arxiv.org/abs/2108.12409v2 | https://arxiv.org/pdf/2108.12409v2.pdf | Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation | Since the introduction of the transformer model by Vaswani et al. (2017), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing the position rep... | ['Mike Lewis', 'Noah A. Smith', 'Ofir Press'] | 2021-08-27 | train-short-test-long-attention-with-linear-1 | https://openreview.net/forum?id=R8sQPpGCv0 | https://openreview.net/pdf?id=R8sQPpGCv0 | iclr-2022-4 | ['2048'] | ['playing-games'] | [ 2.66915619e-01 2.92762309e-01 -2.76777983e-01 -2.51790941e-01
-7.70481825e-01 -7.43288219e-01 7.88598120e-01 2.60067701e-01
-1.08359015e+00 7.32891440e-01 5.88974893e-01 -9.41816032e-01
1.96012571e-01 -7.86417902e-01 -1.02513289e+00 -3.85576606e-01
-1.14398919e-01 5.96315086e-01 4.56191212e-01 -3.00947368... | [10.750683784484863, 8.581208229064941] |
6b76968f-e8ca-4f1a-9f67-0f6e341f2a3e | higher-order-recurrent-space-time-transformer | 2104.08665 | null | https://arxiv.org/abs/2104.08665v3 | https://arxiv.org/pdf/2104.08665v3.pdf | Higher Order Recurrent Space-Time Transformer for Video Action Prediction | Endowing visual agents with predictive capability is a key step towards video intelligence at scale. The predominant modeling paradigm for this is sequence learning, mostly implemented through LSTMs. Feed-forward Transformer architectures have replaced recurrent model designs in ML applications of language processing a... | ['Oswald Lanz', 'Cheng-Kuang Lee', 'Giuseppe Fiameni', 'Tsung-Ming Tai'] | 2021-04-17 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.31149039e-01 2.06484288e-01 -3.46932501e-01 -1.22961193e-01
-1.73907980e-01 -1.00869879e-01 1.22863555e+00 -4.03375626e-01
-2.17951015e-01 1.56784460e-01 9.18247640e-01 -2.99689323e-01
3.91599424e-02 -3.58216763e-01 -8.90145957e-01 -7.63645768e-01
-2.25207001e-01 4.84222025e-01 2.26508319e-01 -4.73438263... | [8.649149894714355, 0.4187415540218353] |
e9008755-1044-4bcc-b373-3b65c09cfba7 | an-energy-management-system-model-with-power | 2212.01910 | null | https://arxiv.org/abs/2212.01910v1 | https://arxiv.org/pdf/2212.01910v1.pdf | An energy management system model with power quality constraints for unbalanced multi-microgrids interacting in a local energy market | As multi-microgrids become readily available, some limited models have been proposed that study operational and power quality constraints with local energy markets independently. This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interaction... | ['Diego Patino', 'César A. Uribe', 'Gabriel Ordóñez-Plata', 'Alejandro Garcés', 'Carlos Adrian Correa-Florez', 'Johanna Castellanos'] | 2022-12-04 | null | null | null | null | ['energy-management'] | ['time-series'] | [-6.10754669e-01 6.12333827e-02 -2.60668635e-01 1.92749277e-01
-8.39938894e-02 -8.55993450e-01 2.27001473e-01 1.99707136e-01
1.24781281e-01 9.68610346e-01 -2.21398398e-01 -6.02253415e-02
-3.60904366e-01 -1.06294966e+00 -1.86866403e-01 -1.17731464e+00
-4.22261329e-03 2.08234221e-01 -5.52505404e-02 -1.77750081... | [5.643560409545898, 2.530181884765625] |
8bc2241f-1661-4c3c-99e8-8a2a0c64799f | owl-observe-watch-listen-localizing-actions | 2202.04947 | null | https://arxiv.org/abs/2202.04947v3 | https://arxiv.org/pdf/2202.04947v3.pdf | OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos | Egocentric videos capture sequences of human activities from a first-person perspective and can provide rich multimodal signals. However, most current localization methods use third-person videos and only incorporate visual information. In this work, we take a deep look into the effectiveness of audiovisual context in ... | ['Bernard Ghanem', 'Chen Zhao', 'Fabian Caba Heilbron', 'Victor Escorcia', 'Merey Ramazanova'] | 2022-02-10 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [-5.31795919e-02 -4.44554627e-01 -3.06925565e-01 -2.56974310e-01
-9.53650057e-01 -6.64291084e-01 7.89430559e-01 -1.74174190e-01
-5.82355559e-01 5.59732854e-01 8.81753325e-01 5.49070060e-01
2.80978262e-01 -2.46715873e-01 -7.78705478e-01 -4.82372522e-01
-3.88391525e-01 -3.89019847e-01 3.21639508e-01 8.90819952... | [8.321980476379395, 0.6305134892463684] |
749688e5-35ef-467c-9ad5-d516971bd7ee | selective-eye-gaze-augmentation-to-enhance | 2012.03145 | null | https://arxiv.org/abs/2012.03145v1 | https://arxiv.org/pdf/2012.03145v1.pdf | Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games | This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Vast evidence suggests that our eye movement convey a wealth of information about the direction of our attention and mental states and encode the information necessary to complete a task. Based on this evidence, we h... | ['Ehsan T. Esfahani', 'Hemanth Manjunatha', 'Chaitanya Thammineni'] | 2020-12-05 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 1.26259491e-01 9.18425769e-02 -6.87491149e-02 -4.62887660e-02
1.74684957e-01 -7.58289844e-02 5.63957393e-01 -6.21370852e-01
-8.73481810e-01 9.36801076e-01 1.63741753e-01 -3.63187529e-02
1.22237176e-01 -3.44205379e-01 -8.41561854e-01 -9.02830958e-01
9.77055654e-02 2.04637721e-01 4.05244499e-01 -4.31548208... | [13.97118854522705, 0.05053410306572914] |
605f95fa-c09f-419e-8136-4b17702c7ec1 | chinese-native-language-identification | null | null | https://aclanthology.org/E14-4019 | https://aclanthology.org/E14-4019.pdf | Chinese Native Language Identification | null | ['Mark Dras', 'Shervin Malmasi'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.364411354064941, 3.527883529663086] |
1edcb0da-2edf-40be-b642-b0b95ac550fd | improving-long-tailed-document-level-relation | 2205.10511 | null | https://arxiv.org/abs/2205.10511v1 | https://arxiv.org/pdf/2205.10511v1.pdf | Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning | Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE). Existing approaches for DocRE aim to extract relation by encoding various information sources in the long context by novel model architectures. However, the inherent long-tailed... | ['Shouling Ji', 'Bo Long', 'Xuhong Zhang', 'Yiming Wu', 'Lingfei Wu', 'Tengfei Ma', 'Yangkai Du'] | 2022-05-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.91459620e-01 6.33452237e-01 -5.51279783e-01 -1.21867105e-01
-7.47093797e-01 -4.36562479e-01 7.50501633e-01 1.82616979e-01
-2.07328841e-01 9.17090595e-01 4.53255475e-01 -5.43993413e-01
-4.09467459e-01 -8.99760067e-01 -4.73737359e-01 -1.64028376e-01
-2.35594437e-01 8.06365609e-01 1.32905975e-01 -3.84752899... | [9.291796684265137, 8.626953125] |
34b609ba-02f6-45c5-ab2f-37939f07a993 | dense-prediction-transformer-for-scale | 2210.01723 | null | https://arxiv.org/abs/2210.01723v1 | https://arxiv.org/pdf/2210.01723v1.pdf | Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry | Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality. However, monocular systems suffer from the scale ambiguity problem due to the lack of depth information in 2D frames. This ... | ['Marcos R. O. A. Maximo', 'André O. Françani'] | 2022-10-04 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-3.29760462e-01 1.75575629e-01 -2.63430655e-01 -1.48228392e-01
1.26624495e-01 -2.92410910e-01 7.65155852e-01 -3.02187979e-01
-4.87596184e-01 7.39262402e-01 -1.97591454e-01 8.80747437e-02
4.38711047e-01 -3.93105537e-01 -6.84186280e-01 -6.11876070e-01
2.70075709e-01 9.60815787e-01 4.93641436e-01 -2.62348711... | [7.937764644622803, -2.203077554702759] |
9cba8817-77ec-48ae-a8fc-956cff03073d | depthwise-convolution-for-multi-agent | 2203.02896 | null | https://arxiv.org/abs/2203.02896v2 | https://arxiv.org/pdf/2203.02896v2.pdf | Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation | Multi-agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within ... | ['Daoyi Dong', 'Chunlin Chen', 'Zhi Wang', 'Donghan Xie'] | 2022-03-06 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.70042837e-01 4.06192839e-02 -1.34826452e-02 -2.90431362e-02
-8.29141319e-01 -5.29755712e-01 8.71107697e-01 6.62892417e-04
-7.05225825e-01 1.10568285e+00 2.90507942e-01 -7.83717334e-02
-4.28375721e-01 -6.26488090e-01 -9.05660570e-01 -8.73406529e-01
-4.16877717e-01 6.73192084e-01 3.40176821e-01 -6.51542425... | [3.769620656967163, 1.9734346866607666] |
4b1ca1d3-61f3-4f0a-b772-29742d3f420b | sms-spam-detection-through-skip-gram | null | null | https://aclanthology.org/2021.findings-acl.367 | https://aclanthology.org/2021.findings-acl.367.pdf | SMS Spam Detection Through Skip-gram Embeddings and Shallow Networks | null | ['Ivan Rizzo Guilherme', 'João Paulo Papa', 'Daniel Carlos Guimarães Pedronette', 'Gustavo Sousa'] | null | null | null | null | findings-acl-2021-8 | ['spam-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.40623664855957, 3.6529669761657715] |
59a44fce-6501-4a7f-b922-dd468816a827 | pod-positional-dependency-based-word | 1911.03785 | null | https://arxiv.org/abs/1911.03785v2 | https://arxiv.org/pdf/1911.03785v2.pdf | PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction | Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term... | ['Chenguang Wang', 'Ming Zhang', 'Yichun Yin'] | 2019-11-09 | null | https://aclanthology.org/2020.coling-main.150 | https://aclanthology.org/2020.coling-main.150.pdf | coling-2020-8 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [-2.35430017e-01 -8.40353072e-02 -8.93215299e-01 -3.97897601e-01
-4.52526122e-01 -5.24345577e-01 7.75570929e-01 7.41716921e-01
-7.37226963e-01 5.13266385e-01 1.04891050e+00 -3.76912594e-01
-1.35547206e-01 -1.03036964e+00 -1.28921688e-01 -5.43452024e-01
-1.65432394e-01 4.84004170e-02 5.42390458e-02 -3.34378749... | [10.550362586975098, 8.487650871276855] |
2be0c558-d7ef-4978-a672-478776a445ee | curriculum-guided-abstractive-summarization-1 | 2302.01342 | null | https://arxiv.org/abs/2302.01342v2 | https://arxiv.org/pdf/2302.01342v2.pdf | Curriculum-Guided Abstractive Summarization | Recent Transformer-based summarization models have provided a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal with more complicated tasks such as novel word generation and sentence paraphrasing. Nonetheless, these models have two shortcomings: (1) the... | ['Nazli Goharian', 'Franck Dernoncourt', 'Hanieh Deilamsalehy', 'Sajad Sotudeh'] | 2023-02-02 | null | null | null | null | ['abstractive-text-summarization', 'extreme-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.18750137e-01 3.99960399e-01 -2.29206488e-01 -1.35744482e-01
-9.76866126e-01 -3.52432907e-01 6.36772275e-01 4.56677943e-01
-5.75995147e-01 9.38460588e-01 1.01386631e+00 -1.52681828e-01
8.13898072e-02 -6.82308137e-01 -6.82511687e-01 -2.82036185e-01
4.84622806e-01 3.10538352e-01 1.74216837e-01 -5.87386131... | [12.311898231506348, 9.356561660766602] |
f4921c57-b1d6-4642-919a-c34b3bf3c4bc | fact-vs-opinion-the-role-of-argumentation | null | null | https://aclanthology.org/2020.coling-main.540 | https://aclanthology.org/2020.coling-main.540.pdf | Fact vs. Opinion: the Role of Argumentation Features in News Classification | A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual in... | ['Daniel Preotiuc-Pietro', 'Smaranda Muresan', 'Tariq Alhindi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['news-classification'] | ['natural-language-processing'] | [-1.49426594e-01 6.63005829e-01 -9.98226702e-01 -1.30335033e-01
-1.14508319e+00 -1.05969167e+00 1.39968538e+00 1.21101665e+00
-5.23726642e-01 1.06604397e+00 1.27505803e+00 -8.72490525e-01
6.73145279e-02 -8.97102714e-01 -9.46097195e-01 -8.69037732e-02
3.72660518e-01 2.58636922e-01 1.56908423e-01 -5.68839133... | [9.037596702575684, 9.773233413696289] |
6bc854b9-02e8-46bb-8e7b-fe4f609dae02 | twitter-bot-detection-using-bidirectional | 2002.01336 | null | https://arxiv.org/abs/2002.01336v1 | https://arxiv.org/pdf/2002.01336v1.pdf | Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings | Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate bots generate a large amount of benign contextual content, i.e., tweets delivering news and updating... | ['Uyen Trang Nguyen', 'Feng Wei'] | 2020-02-03 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.93568641e-01 -1.63668975e-01 -4.56253976e-01 4.43087816e-02
-9.24448669e-02 -4.42436874e-01 8.58628511e-01 4.56900112e-02
-4.75947022e-01 3.60759288e-01 -2.02015433e-02 -4.42689836e-01
7.00509965e-01 -9.97091830e-01 -2.62942076e-01 -5.27389169e-01
-8.33698586e-02 3.14719528e-01 7.51895905e-01 -3.82744044... | [8.131558418273926, 10.166800498962402] |
28a6d1e0-2701-4912-8229-0d9e0cb22275 | multi-view-masked-world-models-for-visual | 2302.02408 | null | https://arxiv.org/abs/2302.02408v2 | https://arxiv.org/pdf/2302.02408v2.pdf | Multi-View Masked World Models for Visual Robotic Manipulation | Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation. Specif... | ['Pieter Abbeel', 'Jinwoo Shin', 'Kimin Lee', 'Stephen James', 'Junsu Kim', 'Younggyo Seo'] | 2023-02-05 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-1.56009957e-01 1.13710962e-01 -2.92623788e-01 -2.59631962e-01
-3.67373258e-01 -8.18239868e-01 4.68159169e-01 -8.23823929e-01
-5.43270968e-02 4.50898677e-01 2.63406396e-01 -2.88493070e-03
1.38366923e-01 -5.77589571e-01 -1.27765071e+00 -6.75846875e-01
3.47758025e-01 4.39005464e-01 -4.43209894e-02 -2.31872723... | [4.631661415100098, 0.7069984674453735] |
d669c8cf-e501-4c0d-8859-c06843343f3b | deep-exposure-fusion-with-deghosting-via | 2004.09089 | null | https://arxiv.org/abs/2004.09089v1 | https://arxiv.org/pdf/2004.09089v1.pdf | Deep Exposure Fusion with Deghosting via Homography Estimation and Attention Learning | Modern cameras have limited dynamic ranges and often produce images with saturated or dark regions using a single exposure. Although the problem could be addressed by taking multiple images with different exposures, exposure fusion methods need to deal with ghosting artifacts and detail loss caused by camera motion or ... | ['Yung-Yu Chuang', 'Sheng-Yeh Chen'] | 2020-04-20 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 7.22232401e-01 -2.95892060e-01 3.64050448e-01 -1.64046437e-01
-4.36040193e-01 -6.35763764e-01 3.91196847e-01 -5.16616523e-01
-2.48314798e-01 8.54629219e-01 1.55120060e-01 -8.18353444e-02
3.80834758e-01 -6.90788865e-01 -7.53612936e-01 -6.75099790e-01
2.04816893e-01 -6.06244028e-01 2.63909817e-01 -1.32807821... | [10.925236701965332, -2.1439049243927] |
f9968bb6-de12-4916-9a34-0bde325defd9 | utilizing-deep-learning-for-automated-tuning | 2306.14349 | null | https://arxiv.org/abs/2306.14349v1 | https://arxiv.org/pdf/2306.14349v1.pdf | Utilizing deep learning for automated tuning of database management systems | Managing the configurations of a database system poses significant challenges due to the multitude of configuration knobs that impact various system aspects.The lack of standardization, independence, and universality among these knobs further complicates the task of determining the optimal settings.To address this issu... | ['Rachana Acharya', 'Kajal Tiwari', 'Karthick Prasad Gunasekaran'] | 2023-06-25 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.35911757e-01 -4.91360694e-01 -2.66234785e-01 -4.56756294e-01
-3.56559992e-01 -5.19077659e-01 1.72963962e-01 4.38693911e-01
-2.96530992e-01 5.83535731e-01 5.94959520e-02 -6.09672308e-01
-5.86595714e-01 -4.93422002e-01 -3.50090712e-01 -3.55265260e-01
-1.90326661e-01 8.94033551e-01 4.82174158e-01 -2.84422070... | [7.19570779800415, 3.1830203533172607] |
52b3ca4e-9148-4595-8446-95799188edc9 | generating-texture-for-3d-human-avatar-from-a | 2305.00936 | null | https://arxiv.org/abs/2305.00936v1 | https://arxiv.org/pdf/2305.00936v1.pdf | Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks | There has been significant progress in generating an animatable 3D human avatar from a single image. However, recovering texture for the 3D human avatar from a single image has been relatively less addressed. Because the generated 3D human avatar reveals the occluded texture of the given image as it moves, it is critic... | ['Junyong Noh', 'Amirsaman Ashtari', 'Kwanggyoon Seo', 'Sihun Cha'] | 2023-05-01 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 3.63267839e-01 4.99678671e-01 2.54890293e-01 -1.90123752e-01
-4.13812369e-01 -3.79679173e-01 3.69896322e-01 -4.14872617e-01
1.38363540e-01 2.94955790e-01 -1.07539557e-01 6.39647245e-02
4.79164094e-01 -1.00639522e+00 -8.32627714e-01 -8.64707172e-01
3.73622388e-01 8.35462749e-01 5.47226727e-01 -3.87859613... | [9.480668067932129, -3.064960479736328] |
6f7949ab-e28f-4b41-a0a0-51630763e1ad | enhanced-multi-level-features-for-very-high | 2305.00679 | null | https://arxiv.org/abs/2305.00679v2 | https://arxiv.org/pdf/2305.00679v2.pdf | Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification | Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in VHR RS scene classification. However, they still provide an unstabl... | ['Jagannath Aryal', 'Sumesh KC', 'Chiranjibi Sitaula'] | 2023-05-01 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 3.03088218e-01 -5.94847739e-01 1.64876580e-01 -3.92973959e-01
-1.13106787e+00 -8.33221450e-02 7.35389411e-01 7.60766342e-02
-4.32807982e-01 8.26033056e-01 -1.32095441e-01 -5.39254770e-02
-3.17680359e-01 -9.71395969e-01 -4.16785091e-01 -1.10033989e+00
1.19592667e-01 -4.41236734e-01 2.52565801e-01 -3.06580245... | [9.78081226348877, -1.3942631483078003] |
eeb452ff-6e03-47d6-a1be-de13879f3556 | differentially-private-data-generative-models | 1812.02274 | null | http://arxiv.org/abs/1812.02274v1 | http://arxiv.org/pdf/1812.02274v1.pdf | Differentially Private Data Generative Models | Deep neural networks (DNNs) have recently been widely adopted in various
applications, and such success is largely due to a combination of algorithmic
breakthroughs, computation resource improvements, and access to a large amount
of data. However, the large-scale data collections required for deep learning
often contai... | ['Chong Xiang', 'Bo Li', 'Qingrong Chen', 'Minhui Xue', 'Dali Kaarfar', 'Nikita Borisov', 'Haojin Zhu'] | 2018-12-06 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.79149568e-01 2.46269181e-01 2.99008489e-01 -3.73978078e-01
-9.67544854e-01 -1.01837361e+00 6.03304684e-01 -2.60024786e-01
-3.01915169e-01 9.01750147e-01 -7.80154839e-02 -5.05193830e-01
3.97643298e-02 -1.16758108e+00 -1.03721392e+00 -1.23773885e+00
-3.18362713e-02 2.26091594e-01 -2.26028580e-02 1.32312447... | [5.895511150360107, 7.011638164520264] |
cd9f708e-409b-4444-9504-06d85ef2b882 | a-fully-automated-latent-fingerprint-matcher | 1406.6854 | null | http://arxiv.org/abs/1406.6854v1 | http://arxiv.org/pdf/1406.6854v1.pdf | A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module | Latent fingerprint has the practical value to identify the suspects who have
unintentionally left a trace of fingerprint in the crime scenes. However,
designing a fully automated latent fingerprint matcher is a very challenging
task as it needs to address many challenging issues including the separation of
overlapping ... | ['Xiuping Jia', 'Jinwei Xu', 'Jiankun Hu'] | 2014-06-26 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 6.30057812e-01 -3.83341402e-01 -3.81502867e-01 -4.35876250e-01
-6.52954936e-01 -9.48326051e-01 3.89974505e-01 -7.40615502e-02
-7.11823478e-02 4.46447551e-01 -3.60228062e-01 -5.59066474e-01
-1.65009633e-01 -7.88105726e-01 -5.03899038e-01 -6.23141646e-01
2.63781935e-01 7.21507072e-01 3.81465077e-01 3.62330645... | [12.937544822692871, 0.9756936430931091] |
a6d15430-39bf-49dc-a288-f39bb42ce2e9 | re-reproducing-learning-to-deceive-with | null | null | http://rescience.github.io/bibliography/Habacker_2021.html | https://zenodo.org/record/4834146/files/article.pdf | [Re] Reproducing Learning to Deceive With Attention-Based Explanations | Scope of Reproducibility
Based on the intuition that attention in neural networks is what the model focuses on, attention is now being used as an explanation for a modelsʼ prediction (see Galassi, Lippi, and Torroni1 for a survey). Pruthi et al.2 challenge the usage of attention-based explanation through a series of e... | ['Mathias Parisot', 'Ard Snijders', 'Rahel Habacker', 'Andrew Harrison'] | 2021-01-31 | null | null | null | rc-2020 | ['occupation-prediction'] | ['natural-language-processing'] | [ 2.71778405e-01 3.98361862e-01 -2.21312270e-01 -4.26216990e-01
-6.83978915e-01 -7.75387645e-01 7.23124385e-01 -1.76782951e-01
-6.43601537e-01 9.00821865e-01 2.44456202e-01 -9.25006390e-01
-3.06103323e-02 -3.77559900e-01 -7.72328138e-01 -6.73351169e-01
4.03996706e-02 5.39285183e-01 -1.70139104e-01 -4.25830245... | [10.572684288024902, 8.56872844696045] |
432f7dd8-d70b-43f6-98c0-b15e70fecb61 | joint-domain-adaptation-and-speech-bandwidth | 2203.16614 | null | https://arxiv.org/abs/2203.16614v1 | https://arxiv.org/pdf/2203.16614v1.pdf | Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification | Speech systems developed for a particular choice of acoustic domain and sampling frequency do not translate easily to others. The usual practice is to learn domain adaptation and bandwidth extension models independently. Contrary to this, we propose to learn both tasks together. Particularly, we learn to map narrowband... | ['Najim Dehak', 'Laureano Moro-Velázquez', 'Jesús Villalba', 'Saurabh Kataria'] | 2022-03-30 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 2.89817780e-01 9.07206014e-02 3.37556228e-02 -7.35410154e-01
-1.82473850e+00 -6.05488002e-01 6.07116282e-01 -5.16145885e-01
-6.31317556e-01 7.78323412e-01 3.19616497e-01 -6.61925435e-01
2.06903368e-02 -1.19606309e-01 -6.61369920e-01 -8.01459968e-01
1.57727897e-01 5.23450851e-01 1.84711978e-01 -1.23151243... | [14.560944557189941, 6.482390880584717] |
68fdbeec-a827-4c5a-ab4a-4f4f887bd0a5 | local-network-community-detection-with | 1601.05775 | null | http://arxiv.org/abs/1601.05775v2 | http://arxiv.org/pdf/1601.05775v2.pdf | Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means | Local network community detection is the task of finding a single community
of nodes concentrated around few given seed nodes in a localized way.
Conductance is a popular objective function used in many algorithms for local
community detection. This paper studies a continuous relaxation of conductance.
We show that con... | ['Twan van Laarhoven', 'Elena Marchiori'] | 2016-01-21 | null | null | null | null | ['local-community-detection'] | ['graphs'] | [-2.01209057e-02 1.92541450e-01 8.68346244e-02 2.20750749e-01
-4.32692230e-01 -6.62097037e-01 2.56485343e-01 7.77665079e-01
-4.13930446e-01 4.24184322e-01 -2.13217229e-01 -2.15171248e-01
-2.52638876e-01 -1.21755064e+00 -3.01360279e-01 -8.29007030e-01
-8.03091109e-01 5.58580279e-01 5.58787942e-01 -9.46312547... | [6.976230621337891, 5.2472076416015625] |
4eb2b9e6-a64c-4a2b-abed-4a0fca887cd3 | what-makes-imagenet-look-unlike-laion | 2306.15769 | null | https://arxiv.org/abs/2306.15769v1 | https://arxiv.org/pdf/2306.15769v1.pdf | What Makes ImageNet Look Unlike LAION | ImageNet was famously created from Flickr image search results. What if we recreated ImageNet instead by searching the massive LAION dataset based on image captions alone? In this work, we carry out this counterfactual investigation. We find that the resulting ImageNet recreation, which we call LAIONet, looks distinctl... | ['Moritz Hardt', 'Ali Shirali'] | 2023-06-27 | null | null | null | null | ['image-retrieval', 'image-captioning', 'selection-bias'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.22027731e-01 1.95259348e-01 -2.00031102e-01 -3.96068633e-01
-4.28148389e-01 -1.04460227e+00 1.03645802e+00 -6.51157508e-03
-5.36076128e-01 5.31209111e-01 6.03788257e-01 -6.24637008e-01
-2.52290487e-01 -7.57291973e-01 -9.92662549e-01 -4.55595821e-01
3.16392511e-01 1.07065670e-01 -9.39156339e-02 -3.13973755... | [10.82689094543457, 1.5555078983306885] |
33cbce65-d2e0-4d6e-8d91-d7a45722a09e | self-supervised-anomaly-detection-of-rogue | 2305.05495 | null | https://arxiv.org/abs/2305.05495v1 | https://arxiv.org/pdf/2305.05495v1.pdf | Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors | IoT data is a central element in the successful digital transformation of agriculture. However, IoT data comes with its own set of challenges. E.g., the risk of data contamination due to rogue sensors. A sensor is considered rogue when it provides incorrect measurements over time. To ensure correct analytical results, ... | ['Estefanía Serral Asensio', 'Jan Diels', 'Bart Baesens', 'Boje Deforce'] | 2023-05-09 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection', 'dynamic-time-warping'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 6.01322651e-01 -1.92604974e-01 6.68242865e-04 -2.22974867e-01
-2.69906729e-01 -7.91081727e-01 3.36397648e-01 6.04738116e-01
-3.49600054e-02 5.09230137e-01 -3.57099503e-01 -2.56696671e-01
-1.12120986e-01 -1.00339961e+00 -9.37206626e-01 -1.13445055e+00
-4.17024165e-01 2.43406698e-01 3.03023577e-01 -2.73112476... | [7.3027448654174805, 2.6799449920654297] |
0d0feb87-84f9-41ef-a5dd-5d95c650ef05 | self-attention-comparison-module-for-boosting | 2012.11357 | null | https://arxiv.org/abs/2012.11357v1 | https://arxiv.org/pdf/2012.11357v1.pdf | Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems | Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently. Most of the previous works select a suitable response only according to the matching degree between the query and each individual candidate response. Althou... | ['Heyan Huang', 'Wei Wei', 'Zhipeng Zhao', 'Xian-Ling Mao', 'Tian Lan'] | 2020-12-21 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-2.43377760e-01 -1.19085193e-01 -3.97227794e-01 -5.52983880e-01
-9.69897270e-01 -4.93868917e-01 6.29783809e-01 1.31119668e-01
-4.73218292e-01 6.09258831e-01 4.19677585e-01 -1.38502136e-01
-1.11060016e-01 -6.62526548e-01 1.51270637e-02 -3.07264507e-01
5.77433586e-01 7.71449864e-01 6.39355004e-01 -6.16612256... | [12.559213638305664, 7.8484697341918945] |
56db824b-57fd-410d-b2bf-491b67a882b3 | learning-dynamic-hierarchical-models-for | 1608.03474 | null | http://arxiv.org/abs/1608.03474v1 | http://arxiv.org/pdf/1608.03474v1.pdf | Learning Dynamic Hierarchical Models for Anytime Scene Labeling | With increasing demand for efficient image and video analysis, test-time cost
of scene parsing becomes critical for many large-scale or time-sensitive vision
applications. We propose a dynamic hierarchical model for anytime scene
labeling that allows us to achieve flexible trade-offs between efficiency and
accuracy in ... | ['Buyu Liu', 'Xuming He'] | 2016-08-11 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.86060882e-01 2.00536489e-01 -4.19536889e-01 -8.24457765e-01
-1.26794016e+00 -4.28762466e-01 2.60448270e-02 -7.41574019e-02
-5.92833579e-01 3.75895888e-01 -5.48289597e-01 -3.79021227e-01
-1.18991852e-01 -6.83682084e-01 -8.52295935e-01 -5.94103932e-01
3.44902575e-02 8.06204379e-01 7.01153159e-01 5.21095097... | [9.207330703735352, -0.05894557386636734] |
14012e90-e36d-494e-9da0-98023678d1a8 | pt-resnet-perspective-transformation-based | 1910.13055 | null | https://arxiv.org/abs/1910.13055v1 | https://arxiv.org/pdf/1910.13055v1.pdf | PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation | Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optim... | ['Yu-An Wang', 'Ming Liu', 'Weidong Zhang', 'Rui Fan', 'Peng Han', 'Lei Qiao', 'Ruiwen Yao', 'Ioannis Pitas'] | 2019-10-29 | null | null | null | null | ['road-scene-understanding', 'road-segementation'] | ['computer-vision', 'computer-vision'] | [ 4.81828541e-01 2.33415306e-01 -1.78944558e-01 -7.47341812e-01
-3.91384542e-01 -2.77231455e-01 3.94056439e-01 -4.56871003e-01
-4.53973472e-01 2.15505809e-01 1.06327765e-01 -3.80770534e-01
2.01508135e-01 -1.10206783e+00 -7.69853354e-01 -5.11287391e-01
4.02692616e-01 1.09518953e-01 5.36396682e-01 -1.18060783... | [8.626986503601074, -1.7137805223464966] |
d936bb9a-3dea-4b1e-ae48-baca170a2a57 | conditioning-hierarchical-reinforcement | 2302.10639 | null | https://arxiv.org/abs/2302.10639v1 | https://arxiv.org/pdf/2302.10639v1.pdf | Conditioning Hierarchical Reinforcement Learning on Flexible Constraints | Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks (goal is not too far away). In this paper, we are specifically interested in the problem of solving temporally extended de... | ['Arunesh Sinha', 'Pradeep Varakantham', 'Yuxiao Lu'] | 2023-02-21 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 3.43845263e-02 4.87406611e-01 -3.41697901e-01 -2.94703186e-01
-1.08363760e+00 -5.39064407e-01 4.58118290e-01 4.99864012e-01
-6.01222038e-01 1.13772631e+00 -4.23061214e-02 -4.13522452e-01
-7.83617854e-01 -9.90853608e-01 -7.91930377e-01 -7.43702233e-01
-8.12036693e-01 7.71084845e-01 2.35848367e-01 -6.01016402... | [4.607389450073242, 1.9782577753067017] |
72f8ea7a-ddcd-4eee-a3fd-db6097826439 | analysis-computational-complexity-reduction | 2206.09071 | null | https://arxiv.org/abs/2206.09071v1 | https://arxiv.org/pdf/2206.09071v1.pdf | Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques | Accurate depth estimation with lowest compute and energy cost is a crucial requirement for unmanned and battery operated autonomous systems. Robotic applications require real time depth estimation for navigation and decision making under rapidly changing 3D surroundings. A high accuracy algorithm may provide the best d... | ['Varo Ly', 'Rajeev Patwari'] | 2022-06-18 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.43758589e-01 5.76335788e-02 3.00496608e-01 -2.91418850e-01
-4.63039041e-01 -5.79969764e-01 5.88089764e-01 -1.52630433e-01
-9.08925235e-01 8.76940191e-01 -2.34922096e-01 -2.59189308e-01
2.91957378e-01 -1.02502370e+00 -4.92862850e-01 -5.64517140e-01
-6.92481995e-02 5.16291440e-01 7.39506245e-01 -1.72357112... | [8.675045013427734, -2.3673534393310547] |
bddc4316-e84c-4318-9a33-3cd695ac38de | 190600638 | 1906.00638 | null | https://arxiv.org/abs/1906.00638v1 | https://arxiv.org/pdf/1906.00638v1.pdf | Federated Hierarchical Hybrid Networks for Clickbait Detection | Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization. As the adverse effects of clickbait attract more and more attention, researchers have started to explore machine learning techniques to automatic... | ['Hankz Hankui Zhuo', 'Feng Liao', 'Xiaoling Huang', 'Yu Zhang'] | 2019-06-03 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-4.10592943e-01 -2.50217676e-01 -9.09952462e-01 -4.45173621e-01
-8.80949736e-01 -8.23469579e-01 6.47727728e-01 2.29296342e-01
-4.32985067e-01 7.24443674e-01 2.29542842e-03 -5.97253859e-01
-9.49656069e-02 -9.80767488e-01 -8.24525416e-01 -1.82032902e-02
1.13756582e-01 4.22618866e-01 4.95669156e-01 5.40521666... | [7.732699394226074, 9.775704383850098] |
947cb963-1de1-47e0-b123-ffb65d1f9584 | generalized-feedback-loop-for-joint-hand | 1903.10883 | null | http://arxiv.org/abs/1903.10883v1 | http://arxiv.org/pdf/1903.10883v1.pdf | Generalized Feedback Loop for Joint Hand-Object Pose Estimation | We propose an approach to estimating the 3D pose of a hand, possibly handling
an object, given a depth image. We show that we can correct the mistakes made
by a Convolutional Neural Network trained to predict an estimate of the 3D pose
by using a feedback loop. The components of this feedback loop are also Deep
Network... | ['Markus Oberweger', 'Vincent Lepetit', 'Paul Wohlhart'] | 2019-03-25 | null | null | null | null | ['hand-object-pose'] | ['computer-vision'] | [-2.49702364e-01 3.30820382e-02 9.73639917e-03 -7.90815204e-02
-4.88826156e-01 -6.45046234e-01 1.90864027e-01 -1.61959410e-01
-5.95434189e-01 1.82081923e-01 4.99018058e-02 -3.94899165e-03
1.69765398e-01 -5.27312398e-01 -1.07744586e+00 -3.97676677e-01
4.27644141e-02 1.29184055e+00 3.97666484e-01 1.28649607... | [6.567220211029053, -0.8325075507164001] |
fe50083a-4dda-4978-b26c-fdef8739accb | large-scale-cloze-test-dataset-designed-by | null | null | https://openreview.net/forum?id=rJJzTyWCZ | https://openreview.net/pdf?id=rJJzTyWCZ | Large-scale Cloze Test Dataset Designed by Teachers | Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and c... | ['Qizhe Xie', 'Zihang Dai', 'Guokun Lai', 'Eduard Hovy'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['cloze-test'] | ['natural-language-processing'] | [-2.60979056e-01 1.45986691e-01 -1.47545353e-01 -6.12611324e-02
-1.08626688e+00 -9.83504117e-01 2.55329967e-01 4.28648770e-01
-3.39933217e-01 6.17234111e-01 3.96764845e-01 -1.04336131e+00
-2.61513442e-01 -7.03098893e-01 -6.61663830e-01 8.16079080e-02
5.35231888e-01 1.52434334e-01 3.36326450e-01 -4.02273297... | [10.172881126403809, 7.6978278160095215] |
30426f57-8458-4d51-b9fa-db84aed0cd97 | ernie-gen-an-enhanced-multi-flow-pre-training | 2001.11314 | null | https://arxiv.org/abs/2001.11314v3 | https://arxiv.org/pdf/2001.11314v3.pdf | ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation | Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inf... | ['Yu Sun', 'Hua Wu', 'Hao Tian', 'Yukun Li', 'Han Zhang', 'Dongling Xiao', 'Haifeng Wang'] | 2020-01-26 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 4.57029015e-01 7.29945242e-01 1.40828833e-01 -4.07088906e-01
-1.15985894e+00 -5.18935442e-01 9.88461852e-01 -1.19091921e-01
-2.55571246e-01 1.21037745e+00 9.37767565e-01 -4.06364352e-01
3.99756640e-01 -1.12730110e+00 -6.45179629e-01 -7.08751231e-02
4.78667736e-01 7.04148948e-01 -6.33010417e-02 -9.12354529... | [11.930377960205078, 9.041038513183594] |
e5c03297-e6af-4010-9fba-35d66ca0db24 | an-effective-automatic-image-annotation-model | 2001.10590 | null | https://arxiv.org/abs/2001.10590v1 | https://arxiv.org/pdf/2001.10590v1.pdf | An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium | Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally ... | ['Mostafa Rahimi', 'Milad Taleby Ahvanooey', 'Amir Vatani'] | 2020-01-26 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.21171671e-01 -2.02853978e-01 -2.47117057e-02 -3.61435354e-01
-5.16316712e-01 -8.94131064e-02 5.28992295e-01 2.92047143e-01
-7.06269145e-01 2.71748453e-01 -1.28796622e-01 9.75461397e-03
-3.37729193e-02 -8.66568029e-01 -3.86681050e-01 -8.39489102e-01
2.43025154e-01 9.83249545e-02 4.19557333e-01 1.45616094... | [11.050382614135742, 1.5528103113174438] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.