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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
91564374-f631-41c6-a9eb-b8498486ae8a | facegan-facial-attribute-controllable | 2011.04439 | null | https://arxiv.org/abs/2011.04439v1 | https://arxiv.org/pdf/2011.04439v1.pdf | FACEGAN: Facial Attribute Controllable rEenactment GAN | The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have demonstrated high quality results by combining the facial landmark based motion representations with the generative adversarial networks. T... | ['Esa Rahtu', 'Juho Kannala', 'Soumya Tripathy'] | 2020-11-09 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.96741664e-01 4.87746865e-01 5.96907474e-02 -2.00423852e-01
-4.31195229e-01 -6.98504746e-01 6.52666867e-01 -1.03195119e+00
-4.77287397e-02 7.10603118e-01 2.28482202e-01 2.49169439e-01
5.16853213e-01 -8.46898377e-01 -1.02902913e+00 -1.01255894e+00
2.07836807e-01 5.76552786e-02 -1.65457457e-01 -3.18874389... | [12.671876907348633, -0.2317209392786026] |
bd884041-01fc-40b7-a4a7-c079ae465286 | self-supervised-context-aware-style | 2206.12559 | null | https://arxiv.org/abs/2206.12559v1 | https://arxiv.org/pdf/2206.12559v1.pdf | Self-supervised Context-aware Style Representation for Expressive Speech Synthesis | Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper... | ['Jian-Yun Nie', 'Ruihua Song', 'Lei He', 'Shaofei Zhang', 'Xi Wang', 'Yihan Wu'] | 2022-06-25 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.68039787e-01 9.78319719e-03 3.62090603e-03 -6.27679288e-01
-1.32792401e+00 -9.73427892e-01 3.02343160e-01 -9.04544294e-02
-5.66533916e-02 5.41989565e-01 6.26245141e-01 2.14322597e-01
2.57741392e-01 -3.14436138e-01 -5.09829938e-01 -4.52234745e-01
4.93661404e-01 5.53758919e-01 -2.10323274e-01 -3.09632182... | [14.94023323059082, 6.508439064025879] |
97edeff5-7045-4353-b910-cf30a81e2ac5 | stage-span-tagging-and-greedy-inference | 2211.15003 | null | https://arxiv.org/abs/2211.15003v3 | https://arxiv.org/pdf/2211.15003v3.pdf | STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes ha... | ['Dangyang Chen', 'Rui Fang', 'Yuanyuan Fu', 'Xian-Ling Mao', 'Wei Wei', 'Shuo Liang'] | 2022-11-28 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 0.33006763 -0.30675486 -0.38783488 -0.40465072 -0.6531443 -0.69028074
0.37330833 0.37462607 -0.4247258 0.6717858 0.2608524 -0.2786851
-0.10622717 -0.7399811 -0.44775516 -0.78315306 0.21263933 0.43004504
0.0177623 -0.32991192 0.2768348 0.01268525 -1.4516296 0.32185644
0.73956746 1.3835036 -0.0... | [11.494612693786621, 6.632240295410156] |
dab0de67-9db5-48ec-a8ad-49ca3249c77a | soundstorm-efficient-parallel-audio | 2305.09636 | null | https://arxiv.org/abs/2305.09636v1 | https://arxiv.org/pdf/2305.09636v1.pdf | SoundStorm: Efficient Parallel Audio Generation | We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach ... | ['Marco Tagliasacchi', 'Neil Zeghidour', 'Eugene Kharitonov', 'Damien Vincent', 'Matt Sharifi', 'Zalán Borsos'] | 2023-05-16 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.17527920e-01 5.79669178e-01 4.57616538e-01 -2.97402203e-01
-1.64082229e+00 -6.81023538e-01 6.03184521e-01 3.01565975e-02
4.03706729e-02 6.76482439e-01 8.45143795e-01 -3.83073717e-01
5.71849525e-01 -4.99191433e-01 -7.76211619e-01 -8.33617896e-02
-5.42104281e-02 7.83003509e-01 3.03742494e-02 -1.55581102... | [15.196174621582031, 6.325460433959961] |
98b83178-511b-46dc-9d68-5b38c47c8fec | a-joint-convolution-auto-encoder-network-for | 2201.10736 | null | https://arxiv.org/abs/2201.10736v1 | https://arxiv.org/pdf/2201.10736v1.pdf | A Joint Convolution Auto-encoder Network for Infrared and Visible Image Fusion | Background: Leaning redundant and complementary relationships is a critical step in the human visual system. Inspired by the infrared cognition ability of crotalinae animals, we design a joint convolution auto-encoder (JCAE) network for infrared and visible image fusion. Methods: Our key insight is to feed infrared and... | ['Xiao-Jun Wu', 'Xiaoqing Luo', 'Mengyu Xiong', 'Yuanhao Gao', 'Zhancheng Zhang'] | 2022-01-26 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.87022680e-01 -1.95167542e-01 8.27736408e-02 -5.41997313e-01
-2.31800735e-01 -6.24625571e-02 4.90165472e-01 -3.99913847e-01
-5.54193377e-01 3.47588092e-01 2.64680058e-01 -5.58063649e-02
1.33723048e-02 -6.08459651e-01 -5.68158507e-01 -6.14908755e-01
2.85086870e-01 -4.50390667e-01 -4.28632684e-02 -2.41585687... | [10.569998741149902, -1.8390570878982544] |
a4bb807e-3bb6-4134-8cc0-b315e0b798d9 | towards-direct-comparison-of-community | 2209.12841 | null | https://arxiv.org/abs/2209.12841v1 | https://arxiv.org/pdf/2209.12841v1.pdf | Towards Direct Comparison of Community Structures in Social Networks | Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or... | ['Anupam Biswas', 'Soumita Das'] | 2022-09-26 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.45303264e-01 -9.98493750e-04 2.94941843e-01 -4.71678227e-02
-5.56444526e-02 -7.71494389e-01 6.99106932e-01 1.06982386e+00
-4.93228018e-01 5.02745926e-01 1.61198527e-01 4.77393419e-02
-7.69590080e-01 -1.30244064e+00 9.07019898e-02 -5.31638384e-01
-4.41132903e-01 4.87812698e-01 6.13971055e-01 -2.15091005... | [6.987318515777588, 5.328951358795166] |
02b55186-ba82-498e-94f3-746a83f8b943 | a-modulation-front-end-for-music-audio | 2105.11836 | null | https://arxiv.org/abs/2105.11836v1 | https://arxiv.org/pdf/2105.11836v1.pdf | A Modulation Front-End for Music Audio Tagging | Convolutional Neural Networks have been extensively explored in the task of automatic music tagging. The problem can be approached by using either engineered time-frequency features or raw audio as input. Modulation filter bank representations that have been actively researched as a basis for timbre perception have the... | ['György Fazekas', 'Charalampos Saitis', 'Cyrus Vahidi'] | 2021-05-25 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.68157613e-01 1.21669960e-03 -6.57962710e-02 -2.05475956e-01
-9.12514746e-01 -8.26899230e-01 6.12284839e-01 1.81686893e-01
-5.72500110e-01 1.79896146e-01 5.95897436e-01 -1.34702856e-02
-4.36261952e-01 -4.62312281e-01 -3.70420009e-01 -5.06648481e-01
-4.93416518e-01 -1.80848211e-01 1.38874352e-01 -2.54726112... | [15.74539852142334, 5.291847229003906] |
07252850-8623-4f51-88f4-2c6fb08d9d01 | a-machine-learning-data-fusion-model-for-soil | 2206.09649 | null | https://arxiv.org/abs/2206.09649v2 | https://arxiv.org/pdf/2206.09649v2.pdf | A Machine Learning Data Fusion Model for Soil Moisture Retrieval | We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moist... | ['Varun Gulshan', 'Grey Nearing', 'Vishal Batchu'] | 2022-06-20 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 2.34096006e-01 3.96643952e-02 -3.35033774e-01 -4.56477642e-01
-7.23178804e-01 -5.48693419e-01 6.30454481e-01 6.36958480e-01
-5.09116232e-01 1.29348469e+00 1.26762137e-01 -9.66361344e-01
-1.10025525e-01 -1.64849246e+00 -7.61150777e-01 -8.02085221e-01
-8.61003101e-01 5.93571179e-02 1.60284847e-01 -7.26607919... | [9.463770866394043, -1.5413070917129517] |
3f0a3148-0eb8-4ddd-9224-20adffaaa139 | multimodal-explanations-justifying-decisions | 1802.08129 | null | http://arxiv.org/abs/1802.08129v1 | http://arxiv.org/pdf/1802.08129v1.pdf | Multimodal Explanations: Justifying Decisions and Pointing to the Evidence | Deep models that are both effective and explainable are desirable in many
settings; prior explainable models have been unimodal, offering either
image-based visualization of attention weights or text-based generation of
post-hoc justifications. We propose a multimodal approach to explanation, and
argue that the two mod... | ['Marcus Rohrbach', 'Anna Rohrbach', 'Zeynep Akata', 'Trevor Darrell', 'Lisa Anne Hendricks', 'Dong Huk Park', 'Bernt Schiele'] | 2018-02-15 | multimodal-explanations-justifying-decisions-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Park_Multimodal_Explanations_Justifying_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Park_Multimodal_Explanations_Justifying_CVPR_2018_paper.pdf | cvpr-2018-6 | ['explainable-models'] | ['computer-vision'] | [ 2.88890451e-01 8.11598480e-01 -2.74765491e-01 -5.65972745e-01
-7.05117702e-01 -5.76536596e-01 1.09187841e+00 5.08329570e-01
-4.81004231e-02 7.62376070e-01 8.82744789e-01 -8.33855450e-01
-4.14810747e-01 -1.31903902e-01 -6.68944418e-01 -3.98855209e-01
1.94739819e-01 6.29442692e-01 -3.93330425e-01 6.82017282... | [10.856901168823242, 1.9385665655136108] |
acf439f7-3295-4484-be57-a42961473025 | enhanced-temporal-knowledge-embeddings-with | 2203.09590 | null | https://arxiv.org/abs/2203.09590v5 | https://arxiv.org/pdf/2203.09590v5.pdf | ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations | Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts. However, existing enhancement approaches cannot apply to temporal knowledge graphs (tKGs), which contain time-dependent e... | ['Yujia Gu', 'Jindong Gu', 'Hinrich Schütze', 'Heinz Köppl', 'Volker Tresp', 'Zifeng Ding', 'Yao Zhang', 'Ruotong Liao', 'Zhen Han'] | 2022-03-17 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.40473020e-01 -2.03402653e-01 -5.07776558e-01 -4.57541980e-02
-1.42487988e-01 -6.63912475e-01 7.42843986e-01 4.10205871e-01
-6.23224854e-01 6.08019650e-01 6.16994739e-01 -3.89820904e-01
-5.45307994e-01 -1.04035187e+00 -5.77888012e-01 -4.41249371e-01
-2.79377043e-01 2.82387305e-02 4.39822704e-01 -2.62628049... | [8.610037803649902, 7.923614025115967] |
394825ef-ceee-4e2d-82a2-46e15afe7e59 | quality-assessment-for-tone-mapped-hdr-images | 1810.08339 | null | https://arxiv.org/abs/1810.08339v2 | https://arxiv.org/pdf/1810.08339v2.pdf | Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information | Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possi... | ['Ming Jiang', 'Tingting Jiang', 'Dingquan Li', 'Qin He'] | 2018-10-19 | null | null | null | null | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 5.72414815e-01 -6.47531271e-01 6.10089079e-02 -5.55117071e-01
-1.10318196e+00 -3.63791524e-03 2.82744229e-01 -1.50498241e-01
-2.47212455e-01 6.50184393e-01 3.38414401e-01 1.89197600e-01
-2.56913275e-01 -1.12662697e+00 -4.32825178e-01 -5.65319538e-01
-8.47036913e-02 -1.95093408e-01 3.21913511e-01 -5.58275223... | [11.042671203613281, -2.3046839237213135] |
6f6dc8be-8655-4a67-88bb-d2b486f9e092 | pac-man-pete-an-extensible-framework-for | 2211.14385 | null | https://arxiv.org/abs/2211.14385v1 | https://arxiv.org/pdf/2211.14385v1.pdf | Pac-Man Pete: An extensible framework for building AI in VEX Robotics | This technical report details VEX Robotics team BLRSAI's development of a fully autonomous robot for VEX Robotics' Tipping Point AI Competition. We identify and develop three separate critical components. This includes a Unity simulation and reinforcement learning model training pipeline, a malleable computer vision pi... | ['Sagar Patil', 'Will Xu', 'Manish Pylla', 'Aref Malek', 'Cole Roberts', 'Nicholas Wade', 'Jacob Zietek'] | 2022-11-25 | null | null | null | null | ['unity'] | ['computer-vision'] | [-4.54899430e-01 5.49286902e-01 -2.14473102e-02 -4.46292579e-01
-5.95492385e-02 -6.39879465e-01 4.35703248e-01 -1.46049634e-01
-3.09151024e-01 5.00344753e-01 5.43386163e-03 -5.06015897e-01
-3.03852856e-02 -3.50353211e-01 -7.01184392e-01 -2.22099438e-01
-2.64653474e-01 6.46933556e-01 5.00951469e-01 -6.30172789... | [4.271167278289795, 1.1112815141677856] |
4d9b1e32-37b3-48e4-b0e0-1c71818a1d54 | modeling-temporal-concept-receptive-field | 2111.11653 | null | https://arxiv.org/abs/2111.11653v1 | https://arxiv.org/pdf/2111.11653v1.pdf | Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis | Event analysis in untrimmed videos has attracted increasing attention due to the application of cutting-edge techniques such as CNN. As a well studied property for CNN-based models, the receptive field is a measurement for measuring the spatial range covered by a single feature response, which is crucial in improving t... | ['Qingming Huang', 'Weigang Zhang', 'Li Su', 'Chi Su', 'Shuhui Wang', 'Zhaobo Qi'] | 2021-11-23 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.02406107e-02 -7.50724554e-01 -5.83797246e-02 -3.90139043e-01
-2.93572601e-02 -5.78390658e-01 5.12800753e-01 4.24514055e-01
-4.72226650e-01 1.67146280e-01 3.04225445e-01 -3.35146710e-02
-3.70486647e-01 -8.05117190e-01 -5.75074852e-01 -6.72853947e-01
-3.88884634e-01 -3.32184702e-01 5.12751102e-01 -7.64156878... | [8.417284965515137, 0.6751063466072083] |
440eeb66-c878-42ad-b5db-a21c752bb5da | pivoine-instruction-tuning-for-open-world | 2305.14898 | null | https://arxiv.org/abs/2305.14898v1 | https://arxiv.org/pdf/2305.14898v1.pdf | PIVOINE: Instruction Tuning for Open-world Information Extraction | We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional closed-world setting of Information Extraction (IE), Open-world IE considers a more general situation where entities and relations could be ... | ['Jianshu Chen', 'Dong Yu', 'Hongming Zhang', 'Kaiqiang Song', 'Xiaoman Pan', 'Keming Lu'] | 2023-05-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-1.02541838e-02 4.65714186e-01 -5.90229809e-01 -1.88969761e-01
-9.71652150e-01 -7.22400725e-01 5.20393014e-01 4.98965234e-01
-6.65133357e-01 6.44611657e-01 4.22566950e-01 -3.79543722e-01
-5.26563644e-01 -1.12885606e+00 -1.06216836e+00 1.03374295e-01
-2.39502698e-01 9.43636537e-01 5.32528400e-01 -6.31512463... | [9.87919807434082, 8.580204010009766] |
5d6a51a3-88c4-44dd-bc3e-7ed6b2887d45 | training-energy-based-models-with-diffusion | 2307.01668 | null | https://arxiv.org/abs/2307.01668v1 | https://arxiv.org/pdf/2307.01668v1.pdf | Training Energy-Based Models with Diffusion Contrastive Divergences | Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which leads to an irreconcilable trade-off between the computational burden and the validity of t... | ['Zhihua Zhang', 'Zhenguo Li', 'Jiacheng Sun', 'Tianyang Hu', 'Hao Jiang', 'Weijian Luo'] | 2023-07-04 | null | null | null | null | ['image-denoising', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 2.03000903e-01 -3.69582117e-01 4.14193094e-01 -7.76943266e-02
-8.77003491e-01 -2.07910061e-01 7.89149284e-01 -1.50817201e-01
-5.98294020e-01 7.64838874e-01 -1.71768755e-01 -2.60076165e-01
-3.05307265e-02 -8.72318149e-01 -7.36773849e-01 -1.22061193e+00
1.69065356e-01 3.68396223e-01 3.38873535e-01 -2.59233289... | [6.941612243652344, 3.7694461345672607] |
5a38aab3-3df4-4854-845b-c5614fe70b5a | an-experimental-study-on-pretraining | 2301.10444 | null | https://arxiv.org/abs/2301.10444v1 | https://arxiv.org/pdf/2301.10444v1.pdf | An Experimental Study on Pretraining Transformers from Scratch for IR | Finetuning Pretrained Language Models (PLM) for IR has been de facto the standard practice since their breakthrough effectiveness few years ago. But, is this approach well understood? In this paper, we study the impact of the pretraining collection on the final IR effectiveness. In particular, we challenge the current ... | ['Stéphane Clinchant', 'Hervé Déjean', 'Carlos Lassance'] | 2023-01-25 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.33647233e-01 6.51137382e-02 -4.42543834e-01 -2.75964051e-01
-1.32111681e+00 -1.01704109e+00 8.75238955e-01 1.76081926e-01
-1.10845828e+00 5.22690296e-01 5.57808101e-01 -4.34828997e-01
-2.89144456e-01 -2.86748171e-01 -7.62054801e-01 -2.93664485e-01
2.27247745e-01 8.99467349e-01 9.38837454e-02 -6.10273898... | [11.494336128234863, 7.871092319488525] |
0124740a-15e9-4cee-a201-f64ca3086b48 | identity-encoder-for-personalized-diffusion | 2304.07429 | null | https://arxiv.org/abs/2304.07429v1 | https://arxiv.org/pdf/2304.07429v1.pdf | Identity Encoder for Personalized Diffusion | Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization by fine-tuning one model for each person. While being successful, this approach incurs additional computation and storage overhead for each... | ['Xuhui Jia', 'Huisheng Wang', 'Boqing Gong', 'Han Zhang', 'Yang Zhao', 'Yandong Li', 'Kelvin C. K. Chan', 'Yu-Chuan Su'] | 2023-04-14 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 3.75658810e-01 1.92765117e-01 1.95599627e-02 -3.70463401e-01
-8.45271468e-01 -4.89740610e-01 7.21699595e-01 -3.89484763e-01
-2.97355980e-01 8.48149121e-01 1.80241466e-01 2.87325829e-01
3.46666902e-01 -8.87497425e-01 -8.29128563e-01 -7.43098915e-01
2.51591146e-01 5.73408902e-01 -8.86311978e-02 -8.40587541... | [11.62338638305664, -0.4844084084033966] |
7df20c39-74ce-4690-8542-5eb1c84504bb | complex-qa-and-language-models-hybrid | 2302.09051 | null | https://arxiv.org/abs/2302.09051v4 | https://arxiv.org/pdf/2302.09051v4.pdf | Complex QA and language models hybrid architectures, Survey | This paper reviews the state-of-the-art of language models architectures and strategies for "complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large Language Models (LLM) are good at leveraging public data on standard problems but once you want to tackle more specific complex questions or proble... | ['Elisabeth Murisasco', 'Vincent Martin', 'Emmanuel Bruno', 'Patrice Bellot', 'Xavier Daull'] | 2023-02-17 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.93888143e-01 4.35629308e-01 9.96500179e-02 -3.08986932e-01
-7.22724199e-01 -1.12627888e+00 5.36652207e-01 4.05311674e-01
-2.58171707e-01 8.63767147e-01 3.95661116e-01 -6.44805431e-01
-8.05259883e-01 -3.30209404e-01 -4.74601924e-01 -1.89546481e-01
1.91212624e-01 1.01822293e+00 -8.74854848e-02 -8.92133176... | [11.012150764465332, 7.953547954559326] |
71d00fee-35ff-4a87-94f5-bc6959bf51e3 | emg-based-feature-extraction-and | 2107.00733 | null | https://arxiv.org/abs/2107.00733v1 | https://arxiv.org/pdf/2107.00733v1.pdf | EMG-Based Feature Extraction and Classification for Prosthetic Hand Control | In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a... | ['Mehrdad Nourani', 'Mohammad Esmaeili', 'Reza Bagherian Azhiri'] | 2021-07-01 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 6.67899787e-01 -4.22215939e-01 4.79453057e-02 -2.12911800e-01
-7.62910306e-01 -2.92321481e-02 1.35024682e-01 -2.05222785e-01
-6.03489637e-01 9.28338110e-01 -2.36524284e-01 4.10730131e-02
-5.59462607e-01 -4.66063470e-01 -2.57078230e-01 -7.04388440e-01
8.84762872e-03 -2.37206355e-01 3.10483664e-01 1.61637813... | [6.85933256149292, 0.17170430719852448] |
afc543de-042a-48e7-b3e5-60252a4c47a6 | counterfactual-vqa-a-cause-effect-look-at | 2006.04315 | null | https://arxiv.org/abs/2006.04315v4 | https://arxiv.org/pdf/2006.04315v4.pdf | Counterfactual VQA: A Cause-Effect Look at Language Bias | VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias ... | ['Ji-Rong Wen', 'Xian-Sheng Hua', 'Zhiwu Lu', 'Hanwang Zhang', 'Yulei Niu', 'Kaihua Tang'] | 2020-06-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.pdf | cvpr-2021-1 | ['counterfactual-inference'] | ['miscellaneous'] | [-1.11603409e-01 7.48607144e-02 -6.76518738e-01 -5.94932199e-01
-1.11555278e+00 -8.16133261e-01 9.24086988e-01 1.57856122e-02
-4.58384275e-01 9.99458432e-01 7.18225837e-01 -6.12109303e-01
6.22465136e-03 -9.19583499e-01 -8.36136103e-01 -6.19763136e-01
5.71695268e-01 4.01650190e-01 -1.15382403e-01 -3.47857803... | [10.248746871948242, 7.711668491363525] |
ed91a0ee-d41d-4178-abf8-b27bf1e5ad48 | compfeat-comprehensive-feature-aggregation | 2012.03400 | null | https://arxiv.org/abs/2012.03400v1 | https://arxiv.org/pdf/2012.03400v1.pdf | CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation | Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects and they suffer in the video scenario due to several distinct challenges such as m... | ['Humphrey Shi', 'Thomas S. Huang', 'Ding Liu', 'Linjie Yang', 'Yang Fu'] | 2020-12-07 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-6.69058189e-02 -5.58679402e-01 -1.77082449e-01 -3.45141500e-01
-6.90280199e-01 -6.22044384e-01 3.91357541e-01 1.60059750e-01
-4.74829704e-01 3.44353616e-01 7.58520439e-02 2.01397777e-01
-3.48062292e-02 -3.93152773e-01 -6.59947395e-01 -6.85995996e-01
1.10750966e-01 -1.43656522e-01 8.05282176e-01 1.36622429... | [9.197434425354004, -0.20655502378940582] |
cf754898-55fd-4599-9a1c-2935f37bd083 | generalized-iris-presentation-attack | 2010.13244 | null | https://arxiv.org/abs/2010.13244v1 | https://arxiv.org/pdf/2010.13244v1.pdf | Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings | Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While i... | ['Richa Singh', 'Mayank Vatsa', 'Akshay Agarwal', 'Vishal Singh', 'Mehak Gupta'] | 2020-10-25 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.84058711e-01 -4.80927914e-01 8.90163258e-02 -5.08287475e-02
-2.45534137e-01 -4.52254981e-01 4.71147567e-01 -1.25949815e-01
-2.85295010e-01 5.48506081e-01 -7.99920335e-02 -3.01434040e-01
-2.48711497e-01 -5.24931252e-01 -4.24299955e-01 -7.85988867e-01
-5.25686741e-02 -1.83463488e-02 -1.45097822e-01 -1.05586506... | [3.813354730606079, -3.5922648906707764] |
789de4fa-ccf3-4097-ba17-d4e93153db80 | thu_ngn-at-semeval-2018-task-1-fine-grained | null | null | https://aclanthology.org/S18-1028 | https://aclanthology.org/S18-1028.pdf | THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM | Traditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can{'}t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is aimed to automatically determine the intensity of emotions or sentiment of tweets to min... | ['Yongfeng Huang', 'Junxin Liu', 'Zhigang Yuan', 'Fangzhao Wu', 'Sixing Wu', 'Chuhan Wu'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-9.71135199e-02 -5.68086132e-02 -3.62078846e-01 -8.65822792e-01
-5.31446457e-01 -2.27469191e-01 5.18895209e-01 1.13022812e-01
-8.21711719e-01 5.01969576e-01 4.67781097e-01 2.21630614e-02
2.41511032e-01 -6.99841321e-01 -4.00831282e-01 -6.17570639e-01
1.20528936e-01 -1.92242742e-01 -3.63276869e-01 -5.18461883... | [11.36906623840332, 6.799574851989746] |
6d9682ae-48dd-4c90-a58a-622550d820b2 | privacy-preserving-representation-learning | 2302.04383 | null | https://arxiv.org/abs/2302.04383v1 | https://arxiv.org/pdf/2302.04383v1.pdf | Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes | Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy concerns when nodes represent people or human-related variables. Moreover, standard NRLs that leverage ... | ['Victor S. Sheng', 'Huixin Zhan'] | 2023-02-09 | null | null | null | null | ['inference-attack', 'membership-inference-attack', 'topological-data-analysis', 'graph-reconstruction', 'learning-network-representations'] | ['adversarial', 'computer-vision', 'graphs', 'graphs', 'methodology'] | [ 7.55951524e-01 6.28657699e-01 -3.25787306e-01 -2.58901715e-01
-1.98788077e-01 -9.09291685e-01 7.18825877e-01 2.35993743e-01
1.01885490e-01 7.67231584e-01 -7.59661496e-02 -7.55021274e-01
-4.42309678e-01 -1.55897355e+00 -8.81301463e-01 -8.62758577e-01
-7.06221581e-01 4.33531910e-01 -2.30908200e-01 -2.09917754... | [6.059266090393066, 7.134435653686523] |
eaff1117-798d-42d5-8af3-6b7d913e5ea1 | megan-memory-enhanced-graph-attention-network | 2110.15327 | null | https://arxiv.org/abs/2110.15327v2 | https://arxiv.org/pdf/2110.15327v2.pdf | MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution | Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspired by the recent success to consider spatial-temporal information for space-time super-resolution, our main goal in this work is to take ful... | ['Wei Fan', 'Hui Tang', 'Ruihan Zhao', 'Aosong Feng', 'Lianyi Han', 'Chenyu You'] | 2021-10-28 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.52390528e-01 -4.17023659e-01 3.21853757e-02 -2.07207263e-01
-8.08768868e-01 4.70765727e-03 3.99320751e-01 -2.41357595e-01
-1.93867102e-01 7.41766930e-01 6.78091347e-01 2.62913704e-01
-3.26077729e-01 -7.98168600e-01 -6.08792186e-01 -6.37074172e-01
-2.75863975e-01 -3.89567494e-01 6.42226279e-01 -2.44521454... | [11.03122329711914, -1.8670783042907715] |
1f659cf9-2e52-49ed-a28a-ee27a5cb67bb | domain-adaptive-full-face-gaze-estimation-via | 2305.16140 | null | https://arxiv.org/abs/2305.16140v1 | https://arxiv.org/pdf/2305.16140v1.pdf | Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement | Along with the recent development of deep neural networks, appearance-based gaze estimation has succeeded considerably when training and testing within the same domain. Compared to the within-domain task, the variance of different domains makes the cross-domain performance drop severely, preventing gaze estimation depl... | ['Yusuke Sugano', 'Xucong Zhang', 'Takuru Shimoyama', 'Jiawei Qin'] | 2023-05-25 | null | null | null | null | ['gaze-estimation', '3d-reconstruction', 'novel-view-synthesis', 'unsupervised-domain-adaptation', 'disentanglement'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 1.88317105e-01 4.59988527e-02 -7.10405931e-02 -6.59120142e-01
-3.82767260e-01 -2.24953353e-01 3.79365087e-01 -7.50919461e-01
-2.82817215e-01 6.68765783e-01 -4.69321944e-02 7.30163008e-02
1.68356210e-01 -2.59107709e-01 -7.56373346e-01 -7.65832424e-01
4.25193071e-01 1.11911744e-01 -6.92204908e-02 -1.14301264... | [14.094612121582031, 0.02810746058821678] |
e9480f33-6b76-4500-b1ef-a429240652e3 | generalized-active-learning-and-design-of | 1904.03909 | null | http://arxiv.org/abs/1904.03909v1 | http://arxiv.org/pdf/1904.03909v1.pdf | Generalized active learning and design of statistical experiments for manifold-valued data | Characterizing the appearance of real-world surfaces is a fundamental problem
in multidimensional reflectometry, computer vision and computer graphics. For
many applications, appearance is sufficiently well characterized by the
bidirectional reflectance distribution function (BRDF). We treat BRDF
measurements as sample... | ['Mikhail A. Langovoy'] | 2019-04-08 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 1.75356403e-01 -3.33212793e-01 -1.94840096e-02 -3.49913567e-01
-4.61610287e-01 -3.13537598e-01 3.62351954e-01 -2.45764509e-01
-3.66320312e-02 7.21753776e-01 -1.88846096e-01 -2.37037554e-01
-3.43090296e-01 -8.00539732e-01 -8.41431141e-01 -1.04966295e+00
7.01720566e-02 3.66609931e-01 -9.87074152e-02 1.84056208... | [9.805234909057617, -2.9351563453674316] |
3a264df7-61ea-40ca-9256-b93f343fc941 | convergence-rates-of-kernel-conjugate | 1607.02387 | null | http://arxiv.org/abs/1607.02387v1 | http://arxiv.org/pdf/1607.02387v1.pdf | Convergence rates of Kernel Conjugate Gradient for random design regression | We prove statistical rates of convergence for kernel-based least squares
regression from i.i.d. data using a conjugate gradient algorithm, where
regularization against overfitting is obtained by early stopping. This method
is related to Kernel Partial Least Squares, a regression method that combines
supervised dimensio... | ['Nicole Krämer', 'Gilles Blanchard'] | 2016-07-08 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.01791987e-01 3.28754574e-01 -1.86010540e-01 -2.58240819e-01
-9.36580300e-01 -4.24793512e-01 1.78950638e-01 1.69875935e-01
-7.37624824e-01 8.47685218e-01 -3.27910855e-02 -9.75883529e-02
-3.55322838e-01 -2.88364142e-01 -7.38392711e-01 -1.05233908e+00
-1.68101609e-01 1.67525649e-01 -1.64951444e-01 -9.70437154... | [7.614120006561279, 4.132737636566162] |
522d285a-2d2c-4b50-9677-3c64f7f3cbda | multimodal-recurrent-neural-networks-with | 1803.04687 | null | http://arxiv.org/abs/1803.04687v1 | http://arxiv.org/pdf/1803.04687v1.pdf | Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling | This paper proposes a new method called Multimodal RNNs for RGB-D scene
semantic segmentation. It is optimized to classify image pixels given two input
sources: RGB color channels and Depth maps. It simultaneously performs training
of two recurrent neural networks (RNNs) that are crossly connected through
information t... | ['Lap-Pui Chau', 'Abrar H. Abdulnabi', 'Gang Wang', 'Bing Shuai', 'Zhen Zuo'] | 2018-03-13 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.43821728e-01 2.13394046e-01 -5.60257971e-01 -7.51225770e-01
-1.04765296e+00 -4.77132559e-01 4.84029591e-01 -3.96244496e-01
-4.96840477e-01 2.29014009e-01 2.92474151e-01 -4.22990322e-01
2.78716475e-01 -6.86308384e-01 -7.61880875e-01 -9.66000676e-01
1.43372566e-01 5.23645222e-01 9.26433057e-02 -7.85684958... | [9.479464530944824, -0.8277812004089355] |
4f333b0c-74ab-4e8e-a4f3-0672d5d97223 | face-transfer-with-generative-adversarial | 1710.06090 | null | http://arxiv.org/abs/1710.06090v1 | http://arxiv.org/pdf/1710.06090v1.pdf | Face Transfer with Generative Adversarial Network | Face transfer animates the facial performances of the character in the target
video by a source actor. Traditional methods are typically based on face
modeling. We propose an end-to-end face transfer method based on Generative
Adversarial Network. Specifically, we leverage CycleGAN to generate the face
image of the tar... | ['Wei-Nan Zhang', 'Zhiming Zhou', 'Runze Xu', 'Yong Yu'] | 2017-10-17 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [ 7.75992051e-02 4.12289053e-01 3.56218368e-01 -2.84290791e-01
-4.36436981e-01 -6.10751033e-01 5.78568459e-01 -1.18987048e+00
-5.79610467e-02 6.83820963e-01 6.76758066e-02 2.43423343e-01
7.97099710e-01 -6.45944953e-01 -1.18225765e+00 -9.26545978e-01
2.43401110e-01 -7.37989545e-02 -3.41180116e-01 7.17802020... | [12.792884826660156, -0.13583704829216003] |
bdb2bf43-6495-466e-906b-d4e9a4c478a3 | learn-to-not-link-exploring-nil-prediction-in | 2305.15725 | null | https://arxiv.org/abs/2305.15725v1 | https://arxiv.org/pdf/2305.15725v1.pdf | Learn to Not Link: Exploring NIL Prediction in Entity Linking | Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to identify mentions without a corresponding entity in the knowledge base, has received insufficient attention. We categorize mentions linking to... | ['Zhifang Sui', 'Lei Hou', 'Juanzi Li', 'Hailong Jin', 'Jifan Yu', 'Fangwei Zhu'] | 2023-05-25 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-4.88012999e-01 3.35600048e-01 -5.75962722e-01 -4.17621315e-01
-8.05079222e-01 -6.51179135e-01 4.53703076e-01 4.34624851e-01
-5.92234433e-01 9.96517479e-01 4.90016550e-01 -6.12410940e-02
2.15268165e-01 -1.03673029e+00 -9.72084463e-01 -5.20579368e-02
1.73612814e-02 4.20290262e-01 2.63754010e-01 -1.87853172... | [9.466826438903809, 8.865026473999023] |
c523a067-3d7b-48ca-9ef9-eacac4289485 | single-image-reflection-removal-using-deep | 1802.00094 | null | http://arxiv.org/abs/1802.00094v1 | http://arxiv.org/pdf/1802.00094v1.pdf | Single Image Reflection Removal Using Deep Encoder-Decoder Network | Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer visio... | ['Xiaolin Wu', 'Xiao Shu', 'Jinjin Gu', 'Zhixiang Chi'] | 2018-01-31 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 1.03735971e+00 1.64177567e-01 8.15041780e-01 -3.69555593e-01
-5.30831575e-01 -1.86091691e-01 5.94672501e-01 -7.03821361e-01
-1.46033645e-01 4.17647660e-01 1.69566087e-02 -2.14354128e-01
2.09659040e-01 -6.72987103e-01 -1.37235701e+00 -7.47226417e-01
3.12476069e-01 -2.56123114e-03 6.92527145e-02 -6.93719536... | [10.551324844360352, -2.7634379863739014] |
a90bdb6b-38ed-456b-b065-4fb397e9a280 | deer-detection-agnostic-end-to-end-recognizer | 2203.05122 | null | https://arxiv.org/abs/2203.05122v1 | https://arxiv.org/pdf/2203.05122v1.pdf | DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting | Recent end-to-end scene text spotters have achieved great improvement in recognizing arbitrary-shaped text instances. Common approaches for text spotting use region of interest pooling or segmentation masks to restrict features to single text instances. However, this makes it hard for the recognizer to decode correct s... | ['Youngmin Baek', 'Bado Lee', 'Seunghyun Park', 'Jaeheung Surh', 'Taeho Kil', 'Han-Cheol Cho', 'Yoonsik Kim', 'Seung Shin', 'Seonghyeon Kim'] | 2022-03-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 7.29881704e-01 -2.29273707e-01 4.16286178e-02 -2.76585251e-01
-7.93781817e-01 -7.45950580e-01 4.33199972e-01 1.17192119e-01
-6.71573579e-01 1.74425960e-01 -1.55478984e-01 -3.13172013e-01
3.96062106e-01 -6.09360039e-01 -7.40051746e-01 -6.81029260e-01
6.45845950e-01 5.81760764e-01 6.82894349e-01 1.15591109... | [11.987504005432129, 2.268937110900879] |
b7b4808a-f9a4-4a52-90b1-90978690d42a | diffmic-dual-guidance-diffusion-network-for | 2303.10610 | null | https://arxiv.org/abs/2303.10610v3 | https://arxiv.org/pdf/2303.10610v3.pdf | DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification | Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general... | ['Angelica I. Aviles-Rivero', 'Lei Zhu', 'Carola-Bibiane Schönlieb', 'Huazhu Fu', 'Yijun Yang'] | 2023-03-19 | null | null | null | null | ['skin-lesion-classification', 'diabetic-retinopathy-grading'] | ['medical', 'medical'] | [ 2.89087147e-01 2.81762898e-01 -2.03059644e-01 -5.59684396e-01
-7.86467135e-01 -2.65174419e-01 6.17031932e-01 -1.47040151e-02
-1.12065323e-01 5.22257090e-01 4.12589520e-01 -2.94807374e-01
-4.23914135e-01 -7.34704733e-01 -4.23617303e-01 -1.02291691e+00
2.70401686e-01 3.59493524e-01 1.04508772e-01 1.39718965... | [14.611976623535156, -2.28882098197937] |
b01198d7-52e1-4733-a433-40175f31c41b | robust-registration-of-multimodal-remote | 2103.16871 | null | https://arxiv.org/abs/2103.16871v1 | https://arxiv.org/pdf/2103.16871v1.pdf | Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity | Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), whic... | ['Li Shen', 'Lorenzo Bruzzone', 'Jie Shan', 'Yuanxin Ye'] | 2021-03-31 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 5.07041395e-01 -7.04889596e-01 1.34481028e-01 -2.72859484e-01
-7.57630825e-01 -3.80007684e-01 7.71126390e-01 2.29801878e-01
-5.73215663e-01 2.93828666e-01 2.25451648e-01 1.72192127e-01
-6.10937417e-01 -7.53246367e-01 -8.42981115e-02 -8.64020050e-01
-9.82338637e-02 1.78609815e-04 2.08365887e-01 -3.71272236... | [10.263724327087402, -1.7831144332885742] |
aae3b30c-c3e3-4cf0-af36-316872b4e4a3 | scenereplica-benchmarking-real-world-robot | 2306.15620 | null | https://arxiv.org/abs/2306.15620v1 | https://arxiv.org/pdf/2306.15620v1.pdf | SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Reproducible Scenes | We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed... | ['Yu Xiang', 'Balakrishnan Prabhakaran', 'Jishnu Jaykumar P', 'Yangxiao Lu', 'Sai Haneesh Allu', 'Ninad Khargonkar'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking', 'robotic-grasping', 'robot-manipulation', 'motion-planning'] | ['miscellaneous', 'robots', 'robots', 'robots', 'robots'] | [-1.14261888e-01 -5.50310671e-01 -3.45845759e-01 -2.85953671e-01
-1.51426628e-01 -7.77904093e-01 3.04291576e-01 1.42411545e-01
-2.33668193e-01 2.96335310e-01 -1.91745639e-01 6.47336021e-02
-4.92790073e-01 -8.24959099e-01 -7.39089429e-01 -4.88221616e-01
-7.16080844e-01 8.75880301e-01 4.92192537e-01 -4.81379181... | [5.607956886291504, -0.6237496733665466] |
7e2a2295-0928-41e3-aa13-2a719ed02134 | read-look-and-detect-bounding-box-annotation | 2306.06149 | null | https://arxiv.org/abs/2306.06149v1 | https://arxiv.org/pdf/2306.06149v1.pdf | Read, look and detect: Bounding box annotation from image-caption pairs | Various methods have been proposed to detect objects while reducing the cost of data annotation. For instance, weakly supervised object detection (WSOD) methods rely only on image-level annotations during training. Unfortunately, data annotation remains expensive since annotators must provide the categories describing ... | ['Eduardo Hugo Sanchez'] | 2023-06-09 | null | null | null | null | ['weakly-supervised-object-detection', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 2.13411868e-01 3.25181872e-01 -2.89937109e-01 -3.02077532e-01
-9.85057592e-01 -7.24059224e-01 6.38859749e-01 3.33556145e-01
-8.64023924e-01 3.97692949e-01 -1.98072091e-01 -6.02312982e-02
4.97912467e-01 -5.23493230e-01 -1.15507329e+00 -4.55435663e-01
2.27550030e-01 3.64021510e-01 6.56522691e-01 -2.18949802... | [9.896169662475586, 1.4973504543304443] |
8277d371-a575-4168-8480-46e09e3cefcd | cfad-coarse-to-fine-action-detector-for | 2008.08332 | null | https://arxiv.org/abs/2008.08332v1 | https://arxiv.org/pdf/2008.08332v1.pdf | CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization | Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD),an orig... | ['Shugong Xu', 'Ke Yan', 'John See', 'Yuxi Li', 'Weiyao Lin', 'Ning Xu', 'Cong Yang'] | 2020-08-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2588_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610494.pdf | eccv-2020-8 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 1.48605630e-01 -3.75635743e-01 -5.70546567e-01 -2.22029120e-01
-1.04632008e+00 -5.17923474e-01 5.73248625e-01 1.69768527e-01
-7.56053805e-01 5.72328627e-01 6.68416262e-01 1.23590976e-01
1.41194418e-01 -4.57273632e-01 -6.68384969e-01 -6.15015924e-01
-1.52269930e-01 3.35416287e-01 9.68091249e-01 1.24621905... | [8.348435401916504, 0.4354734718799591] |
a870281a-f68f-46b4-b88d-4b501f9312c7 | variational-imbalanced-regression | 2306.06599 | null | https://arxiv.org/abs/2306.06599v1 | https://arxiv.org/pdf/2306.06599v1.pdf | Variational Imbalanced Regression | Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces... | ['Hao Wang', 'Ziyan Wang'] | 2023-06-11 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-4.53531116e-01 1.90101430e-01 -5.71434081e-01 -7.96674192e-01
-1.13715231e+00 -1.30524069e-01 6.80190146e-01 1.53066218e-01
-1.11443602e-01 1.01302361e+00 2.55162179e-01 1.22298105e-02
-1.25154838e-01 -9.66765702e-01 -1.16576326e+00 -9.07544553e-01
2.22231790e-01 9.77277696e-01 -2.26911247e-01 9.09374356... | [7.900232315063477, 4.00047492980957] |
49b80201-0398-400a-ba3f-ea871382c2ee | error-detection-for-text-to-sql-semantic | 2305.13683 | null | https://arxiv.org/abs/2305.13683v1 | https://arxiv.org/pdf/2305.13683v1.pdf | Error Detection for Text-to-SQL Semantic Parsing | Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. At the same time, modern deep learning based text-to-SQL parsers are often over-confident and thus casting doubt on their trustworthiness when deployed for real use. To that end, w... | ['Yu Su', 'Huan Sun', 'Ziru Chen', 'Shijie Chen'] | 2023-05-23 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-9.88275334e-02 5.61744273e-01 -9.04778281e-05 -9.95128036e-01
-1.41964710e+00 -5.87242842e-01 3.21080506e-01 5.52875638e-01
-2.59025749e-02 2.37484932e-01 1.13366142e-01 -8.26377988e-01
1.40867576e-01 -9.92556274e-01 -1.18544102e+00 2.86019355e-01
1.64472952e-01 8.56594861e-01 3.96606058e-01 -1.78770274... | [9.843378067016602, 7.893618106842041] |
cac3405d-2962-4b43-bcfd-82f14b8c2a00 | spatio-temporal-pixel-level-contrastive | 2303.14361 | null | https://arxiv.org/abs/2303.14361v1 | https://arxiv.org/pdf/2303.14361v1.pdf | Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation | Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data. However, the access to source data is often restricted or infeasible in real-world scenarios. Under the source data restrictive circumst... | ['Vishal M. Patel', 'Alejandro Galindo', 'Sumanth Chennupati', 'Poojan Oza', 'Shao-Yuan Lo'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-semantic-segmentation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.56981069e-01 -1.45688862e-01 -6.07783198e-01 -4.16001618e-01
-8.52469325e-01 -5.20162761e-01 3.57076257e-01 -1.02437027e-01
-4.00191069e-01 6.82707250e-01 -3.51976864e-02 -1.51048273e-01
1.27508134e-01 -7.69881964e-01 -8.36155772e-01 -8.66537571e-01
3.18198115e-01 3.97915363e-01 5.73581934e-01 2.79342029... | [9.624452590942383, 1.3500066995620728] |
06f310c0-67ec-4d00-90cb-ba5486b1c80a | efficient-lifting-of-symmetry-breaking | 2205.07129 | null | https://arxiv.org/abs/2205.07129v1 | https://arxiv.org/pdf/2205.07129v1.pdf | Efficient lifting of symmetry breaking constraints for complex combinatorial problems | Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of represen... | ['Konstantin Schekotihin', 'Mark Law', 'Martin Gebser', 'Alice Tarzariol'] | 2022-05-14 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 3.43104631e-01 3.85141641e-01 -6.81520760e-01 -2.83363700e-01
-5.83615780e-01 -6.13959074e-01 6.18333593e-02 1.13099851e-01
2.39615187e-01 1.21699727e+00 -3.14318568e-01 -5.25951028e-01
-8.16227496e-01 -1.13079619e+00 -7.32483685e-01 -2.12822497e-01
-1.19473517e-01 8.51958692e-01 2.54944950e-01 -4.23520297... | [8.577601432800293, 6.66124153137207] |
0f8ba65e-57ae-40f5-90de-b0aca25efa24 | histopathology-whole-slide-image-analysis-1 | 2307.04189 | null | https://arxiv.org/abs/2307.04189v1 | https://arxiv.org/pdf/2307.04189v1.pdf | Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning | Graph-based methods have been extensively applied to whole-slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different entities. However, most of the existing methods focus on modeling WSIs with homogeneous graphs (e.g., with homogeneous node type). Despite their... | ['Lequan Yu', 'Guosheng Yin', 'Lan Ma', 'Fernando Julio Cendra', 'Tsai Hor Chan'] | 2023-07-09 | histopathology-whole-slide-image-analysis | http://openaccess.thecvf.com//content/CVPR2023/html/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['representation-learning', 'graph-representation-learning', 'pseudo-label', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 9.64757949e-02 6.30855709e-02 -5.78465402e-01 -2.13634372e-01
-7.17268884e-01 -5.10244191e-01 5.87363541e-01 6.90717280e-01
-8.37901235e-02 6.73408389e-01 1.97474763e-01 -2.65181005e-01
-4.03443068e-01 -1.02457964e+00 -6.22379899e-01 -1.24897635e+00
-2.18382463e-01 2.39007995e-01 4.01509911e-01 4.94635198... | [15.103282928466797, -2.9449682235717773] |
95f715c9-16ba-48b7-96db-d3738ed1e987 | investigating-math-word-problems-using-1 | null | null | https://openreview.net/forum?id=jMI7ZlAC_J | https://openreview.net/pdf?id=jMI7ZlAC_J | Investigating Math Word Problems using Pretrained Multilingual Language Models | In this paper, we revisit math word problems~(MWPs) from the {\em cross-lingual} and {\em multilingual} perspective.We construct our MWP solvers over pretrained multilingual language models using the sequence-to-sequence model with copy mechanism.We compare how the MWP solvers perform in cross-lingual and multilingual ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.84896576e-01 -2.32397437e-01 -1.82836801e-01 -3.97413641e-01
-1.10484338e+00 -1.24456549e+00 3.23903263e-01 3.04479655e-02
-6.43809259e-01 1.22431254e+00 -9.89089683e-02 -8.18714380e-01
3.26684676e-02 -9.41686451e-01 -1.15812111e+00 -3.08841586e-01
1.20345771e-01 6.99598730e-01 -1.89516202e-01 -6.43812537... | [11.072067260742188, 9.947103500366211] |
571bee40-e084-4d37-a9d6-353c666b8687 | on-regularization-parameter-estimation-under | 1608.00250 | null | http://arxiv.org/abs/1608.00250v1 | http://arxiv.org/pdf/1608.00250v1.pdf | On Regularization Parameter Estimation under Covariate Shift | This paper identifies a problem with the usual procedure for
L2-regularization parameter estimation in a domain adaptation setting. In such
a setting, there are differences between the distributions generating the
training data (source domain) and the test data (target domain). The usual
cross-validation procedure requ... | ['Marco Loog', 'Wouter M. Kouw'] | 2016-07-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.05868125e-01 2.65579700e-01 -3.33948851e-01 -5.19927740e-01
-1.01389909e+00 -5.50296485e-01 3.54784876e-01 7.82651454e-02
-6.15375340e-01 1.30152965e+00 1.81227654e-01 -2.88823426e-01
-7.29368925e-02 -5.41729927e-01 -4.80102420e-01 -8.44714820e-01
4.95300949e-01 6.88074350e-01 2.43977115e-01 -9.25932229... | [10.224770545959473, 3.240058660507202] |
e580bd8e-45c2-4a2c-8850-e593c4546c76 | detecting-adversarial-samples-using-density | 1705.02224 | null | http://arxiv.org/abs/1705.02224v4 | http://arxiv.org/pdf/1705.02224v4.pdf | Detecting Adversarial Samples Using Density Ratio Estimates | Machine learning models, especially based on deep architectures are used in
everyday applications ranging from self driving cars to medical diagnostics. It
has been shown that such models are dangerously susceptible to adversarial
samples, indistinguishable from real samples to human eye, adversarial samples
lead to in... | ['Lovedeep Gondara'] | 2017-05-05 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.54371911e-01 4.49236453e-01 1.02340944e-01 -6.69440255e-02
-8.43925595e-01 -7.54649043e-01 7.33296812e-01 -3.32265377e-01
-2.12721065e-01 1.02781117e+00 -2.50291109e-01 -3.48837525e-01
3.09619308e-01 -1.00040519e+00 -8.91155958e-01 -7.26420105e-01
-1.70491710e-01 5.42499244e-01 2.36179978e-01 -1.22318463... | [5.6159515380859375, 7.836112976074219] |
153b6f4a-5101-4137-9276-2c69541191a5 | a-flexible-convolutional-solver-for-fast | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Puy_A_Flexible_Convolutional_Solver_for_Fast_Style_Transfers_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Puy_A_Flexible_Convolutional_Solver_for_Fast_Style_Transfers_CVPR_2019_paper.pdf | A Flexible Convolutional Solver for Fast Style Transfers | We propose a new flexible deep convolutional neural network (convnet) to perform fast neural style transfers. Our network is trained to solve approximately, but rapidly, the artistic style transfer problem of [Gatys et al.] for arbritary styles. While solutions already exist, our network is uniquely flexible by design:... | [' Patrick Perez', 'Gilles Puy'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['video-style-transfer'] | ['computer-vision'] | [ 4.31341588e-01 3.13670754e-01 3.28483880e-01 -3.37929040e-01
-1.47170305e-01 -1.04914391e+00 5.12501538e-01 -4.61367607e-01
-5.82337320e-01 9.21409845e-01 -1.16866469e-01 -2.82637626e-01
9.17339027e-02 -7.24356055e-01 -1.08569121e+00 -4.55920339e-01
1.80903450e-01 4.31995034e-01 1.96936965e-01 -5.22814512... | [11.580144882202148, -0.48877719044685364] |
87e4f0a5-d9ab-4b99-9449-83c210785122 | learning-multi-view-camera-relocalization | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.pdf | Learning Multi-View Camera Relocalization With Graph Neural Networks | We propose to construct a view graph to excavate the information of the whole given sequence for absolute camera pose estimation. Specifically, we harness GNNs to model the graph, allowing even non-consecutive frames to exchange information with each other. Rather than adopting the regular GNNs directly, we redefine th... | [' Junqiu Wang', ' Shaojun Cai', ' Xin Wu', 'Fei Xue'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['camera-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [-2.11808801e-01 2.31332064e-01 1.17303044e-01 -3.28306943e-01
-3.02673131e-01 -6.81741357e-01 2.64248699e-01 -4.07443613e-01
-3.68025601e-01 4.57129836e-01 1.19966656e-01 5.09261787e-02
-1.03108846e-01 -7.24540174e-01 -8.56315017e-01 -4.26345170e-01
-1.51362503e-02 9.66593623e-02 1.82530671e-01 -9.81342345... | [7.971895217895508, -2.2701125144958496] |
babe1ae2-b61d-4738-9c57-8343099940af | local-causal-discovery-for-estimating-causal | 2302.08070 | null | https://arxiv.org/abs/2302.08070v3 | https://arxiv.org/pdf/2302.08070v3.pdf | Local Causal Discovery for Estimating Causal Effects | Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can i... | ['Zachary C. Lipton', 'David Childers', 'Shantanu Gupta'] | 2023-02-16 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.09424010e-01 4.69446480e-01 -1.05487239e+00 1.15540472e-03
-6.25165284e-01 -9.60102975e-01 6.39181316e-01 4.95988131e-01
1.77394539e-01 8.20172608e-01 4.28639591e-01 -8.43328416e-01
-7.20661581e-01 -1.08698034e+00 -8.39267790e-01 -6.26748085e-01
-4.97088939e-01 6.89029515e-01 3.70635182e-01 2.96209782... | [7.743166446685791, 5.306945323944092] |
8fd4ad94-9e14-4ba2-a3e6-c3aeddb4accf | multimodal-and-multilingual-embeddings-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/8466f9ace6a9acbe71f75762ffc890f1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8466f9ace6a9acbe71f75762ffc890f1-Paper.pdf | Multimodal and Multilingual Embeddings for Large-Scale Speech Mining | We present an approach to encode a speech signal into a fixed-size representation which minimizes the cosine loss with the existing massively multilingual LASER text embedding space. Sentences are close in this embedding space, independently of their language and modality, either text or audio. Using a similarity metri... | ['Holger Schwenk', 'Hongyu Gong', 'Paul-Ambroise Duquenne'] | 2021-12-01 | null | https://openreview.net/forum?id=6fmgB38rLI1 | https://openreview.net/pdf?id=6fmgB38rLI1 | neurips-2021-12 | ['speech-to-speech-translation'] | ['speech'] | [ 7.76506662e-02 2.44774237e-01 1.13748737e-01 -4.62346911e-01
-1.76967061e+00 -7.84901023e-01 7.66748250e-01 -1.44555829e-02
-7.60968983e-01 6.73533976e-01 5.14350295e-01 -5.45073986e-01
2.69792676e-01 -3.47812593e-01 -7.16023207e-01 -4.18058604e-01
1.24556623e-01 7.86816835e-01 -1.87322244e-01 -4.09504503... | [14.447022438049316, 7.149083137512207] |
469b922a-190c-42ce-9807-4bda7a840771 | multimodal-knowledge-learning-for-named | null | null | https://openreview.net/forum?id=-0pzbYBTRmt | https://openreview.net/pdf?id=-0pzbYBTRmt | Multimodal Knowledge Learning for Named Entity Disambiguation | With the popularity of online social medias in recent years, massive-scale multimodal information has brought new challenges to traditional Named Entity Disambiguation (NED) tasks. Recently, Multimodal Named Entity Disambiguation (MNED) is proposed to link ambiguous mentions with the textual and visual contexts to a pr... | ['Anonymous'] | 2021-08-17 | null | null | null | acl-arr-august-2021-8 | ['entity-disambiguation'] | ['natural-language-processing'] | [-2.03713581e-01 1.99087262e-01 -4.45971221e-01 -5.44973686e-02
-1.03184295e+00 -7.42717087e-01 7.80869484e-01 3.16748589e-01
-8.27658892e-01 1.00645292e+00 4.31903839e-01 2.07483005e-02
6.75183237e-02 -5.03716290e-01 -4.86531138e-01 -3.75589818e-01
2.94873044e-02 4.69996363e-01 2.50666142e-01 -2.93622583... | [10.874228477478027, 1.6976264715194702] |
12802577-c12e-4175-8f30-57a9bf97d15f | large-language-models-can-be-used-to | 2305.06972 | null | https://arxiv.org/abs/2305.06972v2 | https://arxiv.org/pdf/2305.06972v2.pdf | Large Language Models Can Be Used To Effectively Scale Spear Phishing Campaigns | Recent progress in artificial intelligence (AI), particularly in the domain of large language models (LLMs), has resulted in powerful and versatile dual-use systems. Indeed, cognition can be put towards a wide variety of tasks, some of which can result in harm. This study investigates how LLMs can be used for spear phi... | ['Julian Hazell'] | 2023-05-11 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.61390334e-01 4.00399625e-01 -1.57406971e-01 9.46203619e-02
-5.66692889e-01 -1.05754948e+00 9.56973314e-01 2.80028552e-01
-5.61825573e-01 4.40824121e-01 4.31657910e-01 -1.28667808e+00
-4.00072813e-01 -8.42730999e-01 -6.38038397e-01 -2.38081455e-01
3.18620741e-01 3.90970170e-01 -6.87200278e-02 -6.54096246... | [6.300368785858154, 7.89099645614624] |
6e427450-06d5-4759-bc71-3bb432183ea1 | multi-objective-distributed-optimization-for | 2202.09762 | null | https://arxiv.org/abs/2202.09762v1 | https://arxiv.org/pdf/2202.09762v1.pdf | Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids | The issue of voltage variations caused by integration of renewables has been addressed in this paper through distributed management of Microgrids (MGs). The distribution network (DN) takes the network losses and voltage quality as objectives, an alternating direction method of multipliers (ADMM) with adaptive penalty m... | ['Lingxu Guo', 'Rujing Wang', 'Zuozheng Liu', 'Lemeng Liang', 'Tao Xu'] | 2022-02-20 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.71939087e-01 -1.15738250e-01 -2.92562425e-01 5.91241345e-02
-9.84159783e-02 -4.22818869e-01 7.08487025e-03 1.51348457e-01
-4.70160060e-02 1.69781578e+00 -3.08885217e-01 1.67217106e-01
-6.09842122e-01 -8.26587498e-01 1.60154670e-01 -1.18235183e+00
-3.31957817e-01 2.80899778e-02 -3.11900586e-01 -2.51529932... | [5.683462619781494, 2.5443594455718994] |
87c649ee-aaf0-40df-b550-280a820a25a5 | positive-unlabeled-learning-with-tensor | 2211.14085 | null | https://arxiv.org/abs/2211.14085v2 | https://arxiv.org/pdf/2211.14085v2.pdf | Positive unlabeled learning with tensor networks | Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. We apply the locally purified state tensor network to the positive unlabeled learning problem an... | ['Bojan Žunkovič'] | 2022-11-25 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.09063768e-01 1.06143236e-01 -9.08390999e-01 -7.71612942e-01
-1.00057065e+00 -9.01864648e-01 5.80251813e-01 -1.65607929e-01
-4.54984367e-01 1.05813503e+00 -1.05698489e-01 -3.70395482e-01
1.14235558e-01 -5.74742556e-01 -8.55909526e-01 -5.92107356e-01
-1.77458793e-01 8.26678932e-01 -1.07298471e-01 9.37717259... | [9.492608070373535, 3.47318959236145] |
b2f6ffc4-b47d-48b7-ba70-6ac02d6ecf2f | extract-denoise-and-enforce-evaluating-and | 2104.08724 | null | https://arxiv.org/abs/2104.08724v2 | https://arxiv.org/pdf/2104.08724v2.pdf | Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation | Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important conc... | ['Xiang Ren', 'Jiawei Han', 'Deren Lei', 'Wenchang Ma', 'Yuning Mao'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.413 | https://aclanthology.org/2021.emnlp-main.413.pdf | emnlp-2021-11 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.36824411e-01 3.45125705e-01 -2.32055232e-01 -2.32135445e-01
-8.70855689e-01 -8.48433256e-01 8.98134112e-01 3.37822080e-01
-5.95572054e-01 1.08569360e+00 7.90289521e-01 -3.55388135e-01
-8.01657140e-02 -6.37293518e-01 -2.96640635e-01 -5.05948901e-01
2.75441408e-01 6.42490745e-01 -1.50856063e-01 -5.79535306... | [11.686338424682617, 9.077165603637695] |
36c7cf86-827a-4e1b-943c-73df08b4f600 | annotation-and-analysis-of-extractive | null | null | https://aclanthology.org/L18-1508 | https://aclanthology.org/L18-1508.pdf | Annotation and Analysis of Extractive Summaries for the Kyutech Corpus | null | ['Takashi Yamamura', 'Kazutaka Shimada'] | 2018-05-01 | annotation-and-analysis-of-extractive-1 | https://aclanthology.org/L18-1508 | https://aclanthology.org/L18-1508.pdf | lrec-2018-5 | ['meeting-summarization'] | ['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... | [-7.380148410797119, 3.6448440551757812] |
7818057d-1fc6-444c-8eee-bc68d85ea964 | neural-message-passing-for-quantum-chemistry | 1704.01212 | null | http://arxiv.org/abs/1704.01212v2 | http://arxiv.org/pdf/1704.01212v2.pdf | Neural Message Passing for Quantum Chemistry | Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm... | ['Samuel S. Schoenholz', 'Patrick F. Riley', 'Justin Gilmer', 'George E. Dahl', 'Oriol Vinyals'] | 2017-04-04 | neural-message-passing-for-quantum-chemistry-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=529 | http://proceedings.mlr.press/v70/gilmer17a/gilmer17a.pdf | icml-2017-8 | ['graph-regression', 'formation-energy'] | ['graphs', 'miscellaneous'] | [ 6.57929122e-01 2.05640748e-01 -7.91568935e-01 -4.80807126e-01
-2.86143363e-01 -4.39925969e-01 8.60354245e-01 4.89833325e-01
-2.16426492e-01 9.52571869e-01 -1.92937553e-01 -6.25851572e-01
-4.63365823e-01 -1.07349837e+00 -8.67354929e-01 -7.25517988e-01
-6.02208257e-01 4.09917951e-01 2.07860023e-01 -1.82735965... | [5.520411491394043, 5.690989971160889] |
ff8dc5ff-2c83-4337-9af0-d6e8d4646ab4 | queaco-borrowing-treasures-from-weakly | 2108.08468 | null | https://arxiv.org/abs/2108.08468v3 | https://arxiv.org/pdf/2108.08468v3.pdf | QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction | We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: {named entity recognition (NER)} and {attribute value normalizatio... | ['Qiang Yang', 'Tuo Zhao', 'Bing Yin', 'Yiwei Song', 'Hanqing Lu', 'Tony Wu', 'Chen Luo', 'Tianyu Cao', 'Zheng Li', 'Danqing Zhang'] | 2021-08-19 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.60143541e-02 3.92569676e-02 -7.01632202e-01 -9.33512330e-01
-1.06443131e+00 -8.31641614e-01 3.07098269e-01 2.66294181e-01
-7.19378710e-01 4.30310935e-01 1.17579997e-01 -1.94582656e-01
-1.54645704e-02 -1.03096819e+00 -5.41866243e-01 -5.88459790e-01
4.17035580e-01 7.03890622e-01 1.61340401e-01 -4.04566318... | [9.972691535949707, 6.4085187911987305] |
a39f7d85-8009-4dd6-8623-9ddee7684f8a | anomaly-detection-via-oversampling-principal | null | null | https://link.springer.com/chapter/10.1007/978-3-642-00909-9_43 | https://github.com/SohanLalYadav2304/Anomaly-detection-via-oversampling-principal-component-analysis/blob/main/C18_Anomaly%20Detection%20via%20Over-sampling%20Principal%20Component%20Analysis.pdf | Anomaly Detection via oversampling Principal Component Analysis | Abstract Outlier detection is an important issue in data mining and has been studied
in different research areas. It can be used for detecting the small amount of deviated
data. In this article, we use “Leave One Out” procedure to check each individual
point the “with or without” effect on the variation of principal... | ['Yuh-Jye Lee', 'Zheng-Yi Lee', 'Yi-Ren Yeh'] | 2013-07-01 | null | null | null | 11th-ieee-international-conference-on | ['anomaly-detection', 'outlier-detection'] | ['methodology', 'methodology'] | [-2.57783383e-01 -6.75721228e-01 2.52373010e-01 -9.78129953e-02
-1.74000300e-02 -1.67967230e-01 6.84388950e-02 5.78834534e-01
-2.40751803e-01 3.57418954e-01 -1.38342276e-01 -2.13220999e-01
-3.62357289e-01 -7.32979953e-01 -3.43203425e-01 -7.68576324e-01
-1.63694844e-01 3.25889438e-01 5.52845061e-01 5.87823652... | [7.4241437911987305, 2.7043263912200928] |
ef4c7105-11ac-44ec-ba5f-ff420c3c8a95 | sslayout360-semi-supervised-indoor-layout | 2103.13696 | null | https://arxiv.org/abs/2103.13696v3 | https://arxiv.org/pdf/2103.13696v3.pdf | SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama | Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to lear... | ['Phi Vu Tran'] | 2021-03-25 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 4.06323612e-01 1.52593464e-01 4.87756394e-02 -7.99454153e-01
-8.11967492e-01 -8.97972345e-01 6.28162980e-01 3.79411906e-01
-2.42920980e-01 6.10147417e-01 3.77711415e-01 -7.12721705e-01
9.41287205e-02 -6.32308185e-01 -8.62429917e-01 -2.73506612e-01
-8.89800936e-02 5.76829076e-01 -1.25678003e-01 7.33444095... | [8.749372482299805, -2.846717119216919] |
1a7ae3d7-236d-4435-84c1-8973a1eb5daa | an-energy-approach-describes-spine | null | null | https://link.springer.com/article/10.1007/s10237-020-01390-9 | https://link.springer.com/article/10.1007/s10237-020-01390-9 | An energy approach describes spine equilibrium in adolescent idiopathic scoliosis | The adolescent idiopathic scoliosis (AIS) is a 3D deformity of the spine whose origin is unknown and clinical evolution unpredictable. In this work, a mixed theoretical and numerical approach based on energetic considerations is proposed to study the global spine deformations. The introduced mechanical model aims at ov... | ['Roxane Compagnon & Pascal Swider', 'Jérôme Sales de Gauzy', 'Franck Accadbled', 'Vincent Doyeux', 'Pauline Assemat', 'Baptiste Brun-Cottan'] | 2020-10-02 | null | null | null | biomechanics-and-modeling-in-mechanobiology | ['total-energy'] | ['miscellaneous'] | [-1.94908321e-01 4.03324097e-01 -1.11791305e-01 7.03295246e-02
-1.13293953e-01 -2.62680829e-01 2.03102335e-01 2.30279952e-01
-4.62179720e-01 7.16468990e-01 6.54311851e-02 -4.09210138e-02
-7.95955479e-01 -4.82658982e-01 -6.50760412e-01 -5.62840521e-01
-3.54849607e-01 1.31879091e+00 5.37661314e-01 -7.48334527... | [6.330603122711182, 3.143789052963257] |
af3a07b5-15b7-4ab2-a76a-dd566018272a | an-overview-of-facial-micro-expression | 2012.11307 | null | https://arxiv.org/abs/2012.11307v1 | https://arxiv.org/pdf/2012.11307v1.pdf | An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge | Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has dr... | ['Wen-Huang Cheng', 'Hong-Han Shuai', 'Ling Lo', 'Hong-Xia Xie'] | 2020-12-21 | null | null | null | null | ['micro-expression-spotting', 'micro-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.47837770e-01 -9.59757194e-02 -4.05998826e-01 -8.92215371e-01
-4.96428519e-01 -2.40703851e-01 3.47987711e-01 -4.95307982e-01
-1.60632089e-01 6.06800914e-01 6.59547672e-02 4.67085868e-01
1.70974627e-01 -4.09901083e-01 -2.24522963e-01 -1.08248985e+00
-9.44346413e-02 -2.61636823e-01 -5.66720963e-01 -6.64494395... | [13.562216758728027, 1.9231032133102417] |
5e4deef2-88f8-46e1-9bb0-d45001fbd909 | child-face-recognition-at-scale-synthetic | 2304.11685 | null | https://arxiv.org/abs/2304.11685v1 | https://arxiv.org/pdf/2304.11685v1.pdf | Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark | We address the need for a large-scale database of children's faces by using generative adversarial networks (GANs) and face age progression (FAP) models to synthesize a realistic dataset referred to as HDA-SynChildFaces. To this end, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult su... | ['Christian Rathgeb', 'Mathias Ibsen', 'Anders Bensen Ottsen', 'Magnus Falkenberg'] | 2023-04-23 | null | null | null | null | ['face-recognition', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 3.57627533e-02 2.66840279e-01 3.39806587e-01 -7.04956293e-01
-2.51809508e-01 -5.23840964e-01 7.33439386e-01 -6.60627782e-01
-1.50056526e-01 5.23817837e-01 2.21205831e-01 3.63783091e-01
4.80043054e-01 -7.61641204e-01 -5.40512919e-01 -6.45012081e-01
-1.15651347e-01 3.27541143e-01 -3.79048079e-01 -6.79289997... | [12.812910079956055, 0.5424520969390869] |
030ca9a5-db8b-45e7-9d60-358f910973e4 | graph-neural-processes-for-spatio-temporal | 2305.18719 | null | https://arxiv.org/abs/2305.18719v1 | https://arxiv.org/pdf/2305.18719v1.pdf | Graph Neural Processes for Spatio-Temporal Extrapolation | We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either ... | ['Roger Zimmermann', 'Yu Zheng', 'Hongyang Chen', 'Zhencheng Fan', 'Yuxuan Liang', 'Junfeng Hu'] | 2023-05-30 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 3.50089036e-02 2.23834470e-01 -1.30356357e-01 -3.68865639e-01
-6.49482131e-01 -2.98973233e-01 8.48471940e-01 4.25217986e-01
2.87268937e-01 9.46879625e-01 3.98056775e-01 -5.21707535e-01
-5.32159686e-01 -1.31008410e+00 -1.15464675e+00 -7.24418581e-01
-6.17926538e-01 5.31276822e-01 2.97987282e-01 4.13922876... | [7.011075973510742, 3.43898344039917] |
a1beab54-b844-4240-8dcd-840ccb40f1ba | advancing-the-state-of-the-art-for-ecg | 2211.07579 | null | https://arxiv.org/abs/2211.07579v1 | https://arxiv.org/pdf/2211.07579v1.pdf | Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models | The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced structured state space models (SSMs) as a particularly promising approach due to its ability to capture long-term dependencies in time series. We ... | ['Nils Strodthoff', 'Temesgen Mehari'] | 2022-11-14 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.24827170e-01 -1.89786479e-02 -1.57284200e-01 -2.81212032e-01
-5.94978631e-01 -2.40693241e-01 3.39679599e-01 5.30156910e-01
-4.77757126e-01 6.90505564e-01 9.70968604e-02 -4.66925949e-01
-6.44523382e-01 -3.96371752e-01 -3.24975967e-01 -6.47406280e-01
-7.66549647e-01 5.46587966e-02 1.55177772e-01 -2.84827858... | [14.30038070678711, 3.281919002532959] |
8b7ec351-b3ae-4740-ba7a-b52ea7cf69d0 | bio-joie-joint-representation-learning-of | 2103.04283 | null | https://arxiv.org/abs/2103.04283v1 | https://arxiv.org/pdf/2103.04283v1.pdf | Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases | The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely... | ['Wei Wang', 'Carlo Zaniolo', 'Yizhou Sun', 'Muhao Chen', 'Chelsea Ju', 'Junheng Hao'] | 2021-03-07 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.30945519e-01 -4.56361519e-03 -2.95435667e-01 -2.91339129e-01
-2.76744545e-01 -6.63101315e-01 1.98089164e-02 5.64792693e-01
8.19679070e-03 8.97259176e-01 2.17239588e-01 -2.61390030e-01
-5.17942846e-01 -9.04530585e-01 -8.99509907e-01 -9.88148093e-01
-4.96190310e-01 5.84879279e-01 -6.18661605e-02 -2.48505980... | [5.655592441558838, 5.895936965942383] |
52e5be3c-bfeb-4948-9b12-423c782ea346 | the-gesture-authoring-space-authoring | 2207.01092 | null | https://arxiv.org/abs/2207.01092v1 | https://arxiv.org/pdf/2207.01092v1.pdf | The Gesture Authoring Space: Authoring Customised Hand Gestures for Grasping Virtual Objects in Immersive Virtual Environments | Natural user interfaces are on the rise. Manufacturers for Augmented, Virtual, and Mixed Reality head mounted displays are increasingly integrating new sensors into their consumer grade products, allowing gesture recognition without additional hardware. This offers new possibilities for bare handed interaction within v... | ['Didier Stricker', 'Gerd Reis', 'Alexander Schäfer'] | 2022-07-03 | null | null | null | null | ['template-matching', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.18463598e-01 -6.29221499e-02 -9.91772786e-02 -1.43150717e-01
1.21815853e-01 -8.51685405e-01 4.99776065e-01 -4.72001195e-01
-7.19716489e-01 2.91175693e-01 -7.24794418e-02 -3.97604018e-01
-4.91340697e-01 -4.35499251e-01 1.12946806e-02 -5.47287464e-01
2.56658822e-01 4.14014339e-01 4.55285728e-01 -2.30412915... | [6.477417945861816, -0.2743462324142456] |
1a2c1c46-dfa2-4cc5-b8db-a2f48a7eede7 | viewpoint-estimation-insights-model | 1807.01312 | null | http://arxiv.org/abs/1807.01312v1 | http://arxiv.org/pdf/1807.01312v1.pdf | Viewpoint Estimation-Insights & Model | This paper addresses the problem of viewpoint estimation of an object in a
given image. It presents five key insights that should be taken into
consideration when designing a CNN that solves the problem. Based on these
insights, the paper proposes a network in which (i) The architecture jointly
solves detection, classi... | ['Ayellet Tal', 'Gilad Divon'] | 2018-07-03 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.02710673e-01 1.99181437e-02 1.78283170e-01 -4.75495964e-01
-4.53028351e-01 -3.01785707e-01 4.66359288e-01 -2.71230638e-01
-3.99509430e-01 1.58748627e-01 9.13427770e-03 -7.25119710e-02
2.06441358e-01 -6.83129489e-01 -7.33158946e-01 -5.37630796e-01
2.22357064e-01 2.53723711e-01 5.62692404e-01 7.07934238... | [8.01606559753418, -2.440056562423706] |
7795c483-9bc9-4173-9b74-739bc9b95ff5 | dani-net-uncalibrated-photometric-stereo-by | 2303.15101 | null | https://arxiv.org/abs/2303.15101v2 | https://arxiv.org/pdf/2303.15101v2.pdf | DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering | Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with c... | ['Xudong Jiang', 'Gang Pan', 'Boxin Shi', 'Qian Zheng', 'Zongrui Li'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.pdf | cvpr-2023-1 | ['inverse-rendering'] | ['computer-vision'] | [ 9.26692069e-01 5.16100526e-02 6.52325094e-01 -6.01497114e-01
-2.97655284e-01 -4.59036410e-01 3.40034872e-01 -1.02099097e+00
2.00387523e-01 5.91527104e-01 1.10560656e-01 -2.64583528e-01
-1.16675444e-01 -8.26913714e-01 -4.23085809e-01 -7.36932576e-01
4.42894131e-01 5.06058156e-01 5.26678741e-01 -2.73083925... | [9.759957313537598, -3.053640127182007] |
d2471696-dd55-4c66-9194-d603c048705c | thompson-sampling-for-high-dimensional-sparse | 2211.05964 | null | https://arxiv.org/abs/2211.05964v2 | https://arxiv.org/pdf/2211.05964v2.pdf | Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits | We consider the stochastic linear contextual bandit problem with high-dimensional features. We analyze the Thompson sampling algorithm using special classes of sparsity-inducing priors (e.g., spike-and-slab) to model the unknown parameter and provide a nearly optimal upper bound on the expected cumulative regret. To th... | ['Ambuj Tewari', 'Saptarshi Roy', 'Sunrit Chakraborty'] | 2022-11-11 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 6.53720051e-02 -5.00432029e-02 -8.97050142e-01 -4.37605351e-01
-1.31844091e+00 -3.94016147e-01 4.05641049e-01 -2.48625100e-01
-1.71140775e-01 1.17042029e+00 2.72395641e-01 -5.95918059e-01
-3.68178070e-01 -6.06395245e-01 -1.08460474e+00 -9.11563277e-01
3.45591232e-02 7.29704559e-01 2.84924060e-02 6.54878914... | [4.4829182624816895, 3.191601514816284] |
796d4196-0155-4985-98b8-a65bf538e4b2 | lip-reading-sentences-in-the-wild | 1611.05358 | null | http://arxiv.org/abs/1611.05358v2 | http://arxiv.org/pdf/1611.05358v2.pdf | Lip Reading Sentences in the Wild | The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild ... | ['Joon Son Chung', 'Andrew Zisserman', 'Oriol Vinyals', 'Andrew Senior'] | 2016-11-16 | lip-reading-sentences-in-the-wild-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Chung_Lip_Reading_Sentences_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chung_Lip_Reading_Sentences_CVPR_2017_paper.pdf | cvpr-2017-7 | ['lipreading'] | ['computer-vision'] | [ 6.22202158e-01 3.81345272e-01 -4.75983590e-01 -3.09197068e-01
-1.32817674e+00 -3.93325150e-01 7.07860768e-01 -4.53039080e-01
-3.73021424e-01 4.70664442e-01 6.43552840e-01 -5.03040791e-01
5.19292533e-01 5.77263050e-02 -9.19052899e-01 -7.03073859e-01
1.85009271e-01 1.38441190e-01 1.00217827e-01 1.87988698... | [14.335977554321289, 5.022488594055176] |
e7b320ce-773d-4131-a9de-314c7197e3b9 | phonocardiographic-sensing-using-deep | 1801.08322 | null | https://arxiv.org/abs/1801.08322v4 | https://arxiv.org/pdf/1801.08322v4.pdf | Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection | Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the ... | ['Muhammad Usman', 'Siddique Latif', 'Junaid Qadir', 'Rajib Rana'] | 2018-01-25 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 3.55114579e-01 2.16264687e-02 5.96882164e-01 -2.80855484e-02
-4.06757116e-01 -2.85045505e-01 -2.42709473e-01 -2.24243645e-02
-2.36089155e-01 4.67056215e-01 2.46850595e-01 -7.00756609e-01
-3.58110726e-01 -3.31086338e-01 1.07604012e-01 -6.74554110e-01
-2.69936383e-01 4.35378969e-01 -9.84636098e-02 -1.31904915... | [14.312309265136719, 3.3034896850585938] |
3c62ef77-9d5f-4ad3-94a8-db9cfd8f5255 | coupling-machine-learning-and-crop-modeling | 2008.04060 | null | https://arxiv.org/abs/2008.04060v2 | https://arxiv.org/pdf/2008.04060v2.pdf | Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt | This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most acc... | ['Sotirios V. Archontoulis', 'Guiping Hu', 'Mohsen Shahhosseini', 'Isaiah Huber'] | 2020-07-28 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.56127289e-01 -1.33202463e-01 -8.22781503e-01 -2.39014924e-01
3.10359269e-01 -4.23218042e-01 2.71622926e-01 7.70300448e-01
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-4.75459486e-01 -1.35723388e+00 -5.95337331e-01 -5.17710388e-01
-1.32950187e-01 -1.50303960e-01 -2.77424812e-01 -6.21853530... | [9.318674087524414, -1.6573569774627686] |
a3546c0c-4c91-4065-b8d3-2f31693ef48b | gpt4graph-can-large-language-models | 2305.15066 | null | https://arxiv.org/abs/2305.15066v2 | https://arxiv.org/pdf/2305.15066v2.pdf | GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking | Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language processing tasks. In the real world, graph data is ubiquitous and an essential part of AGI and prevails in domains like social network analysis, bi... | ['Shi Han', 'Xinyi He', 'Mengyu Zhou', 'Hengyu Liu', 'Lun Du', 'Jiayan Guo'] | 2023-05-24 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.07112676e-01 5.14835656e-01 -2.58598298e-01 -1.07481457e-01
6.14119880e-02 -5.43675840e-01 5.31816602e-01 7.91209459e-01
-1.50806457e-01 4.17487293e-01 1.56807154e-03 -8.78386438e-01
-3.06695580e-01 -1.29599559e+00 -2.82660246e-01 1.33540723e-02
-2.73139775e-01 6.63871944e-01 1.91768274e-01 -4.27038610... | [8.804367065429688, 7.478641510009766] |
a5c1577c-2554-4e9e-8a39-f1a49fe5af9b | chmusic-a-traditional-chinese-music-dataset | 2108.08470 | null | https://arxiv.org/abs/2108.08470v2 | https://arxiv.org/pdf/2108.08470v2.pdf | ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition | Musical instruments recognition is a widely used application for music information retrieval. As most of previous musical instruments recognition dataset focus on western musical instruments, it is difficult for researcher to study and evaluate the area of traditional Chinese musical instrument recognition. This paper ... | ['Haoran Wei', 'Haidi Zhu', 'Yuxiang Zhu', 'Xia Gong'] | 2021-08-19 | null | null | null | null | ['instrument-recognition', 'music-information-retrieval'] | ['audio', 'music'] | [ 3.79610881e-02 -1.09471583e+00 -4.09838825e-01 1.71307772e-01
-5.34967721e-01 -7.24520922e-01 8.59661773e-02 -6.50603116e-01
-5.25559664e-01 4.51834172e-01 2.74231583e-01 3.33898574e-01
-6.80627584e-01 -3.98764670e-01 1.27623811e-01 -6.38719261e-01
1.15687788e-01 2.37991199e-01 -3.80271189e-02 -1.02964468... | [15.937295913696289, 5.196574687957764] |
9fb4cf4c-5adf-4bb8-90da-e1f31659c262 | few-shot-weakly-supervised-cybersecurity | 2304.07470 | null | https://arxiv.org/abs/2304.07470v1 | https://arxiv.org/pdf/2304.07470v1.pdf | Few-shot Weakly-supervised Cybersecurity Anomaly Detection | With increased reliance on Internet based technologies, cyberattacks compromising users' sensitive data are becoming more prevalent. The scale and frequency of these attacks are escalating rapidly, affecting systems and devices connected to the Internet. The traditional defense mechanisms may not be sufficiently equipp... | ['Vrizlynn L. L. Thing', 'Rahul Kale'] | 2023-04-15 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-1.93177640e-01 -4.51458722e-01 -1.44607022e-01 -3.08301568e-01
-1.97748274e-01 -5.57309210e-01 7.44851768e-01 7.56725729e-01
-3.19070905e-01 4.92930114e-01 -1.07252404e-01 -6.67854905e-01
1.04218151e-03 -9.14336145e-01 -3.56038034e-01 -5.39198101e-01
-3.69525194e-01 4.80367541e-01 4.14522678e-01 -3.84587914... | [5.290387153625488, 7.2658586502075195] |
5de1d25a-7ac6-4eab-85cc-e4b0f085c0b2 | margin-mixup-a-method-for-robust-speaker | 2304.03515 | null | https://arxiv.org/abs/2304.03515v1 | https://arxiv.org/pdf/2304.03515v1.pdf | Margin-Mixup: A Method for Robust Speaker Verification in Multi-Speaker Audio | This paper is concerned with the task of speaker verification on audio with multiple overlapping speakers. Most speaker verification systems are designed with the assumption of a single speaker being present in a given audio segment. However, in a real-world setting this assumption does not always hold. In this paper, ... | ['Kris Demuynck', 'Nilesh Madhu', 'Jenthe Thienpondt'] | 2023-04-07 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 2.01578513e-01 1.84284430e-02 1.58133224e-01 -6.26622319e-01
-1.40971506e+00 -6.71105027e-01 5.18262863e-01 1.40864223e-01
-2.08784342e-01 2.39781454e-01 2.32770592e-01 -3.87205482e-01
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1.50382649e-02 2.04602227e-01 5.11889346e-02 -1.12002656... | [14.319768905639648, 6.081748962402344] |
b210f7bc-88aa-42d4-ac6a-4adb0ce9306c | interactive-portrait-harmonization | 2203.08216 | null | https://arxiv.org/abs/2203.08216v1 | https://arxiv.org/pdf/2203.08216v1.pdf | Interactive Portrait Harmonization | Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive... | ['Vishal M. Patel', 'Kalyan Sunkavalli', 'Zijun Wei', 'Yinglan Ma', 'Jose Echevarria', 'Zhe Lin', 'Yilin Wang', 'Jianming Zhang', 'He Zhang', 'Jeya Maria Jose Valanarasu'] | 2022-03-15 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.28910044e-01 -3.06730807e-01 -1.40227200e-02 -1.96090773e-01
-6.47064507e-01 -6.43898070e-01 3.93336773e-01 -8.59251916e-02
-1.87943920e-01 4.91970569e-01 -5.95598929e-02 -1.24556171e-02
1.34648263e-01 -9.65526581e-01 -5.81240118e-01 -8.37063015e-01
5.36484718e-01 -2.73402482e-02 3.27183157e-01 -3.81924897... | [11.263912200927734, -1.173510193824768] |
31013d4a-9867-4dd4-a4cb-9cd2fe828ead | visolo-grid-based-space-time-aggregation-for | 2112.04177 | null | https://arxiv.org/abs/2112.04177v2 | https://arxiv.org/pdf/2112.04177v2.pdf | VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation | For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous methods follow a two-stage approach requiring additional computations such as RPN and RoIAlign, and do not fully exploit the available informat... | ['Seon Joo Kim', 'Min-Jung Kim', 'Hyunwoo Kim', 'Yeonchool Park', 'Seoung Wug Oh', 'Sukjun Hwang', 'Su Ho Han'] | 2021-12-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Han_VISOLO_Grid-Based_Space-Time_Aggregation_for_Efficient_Online_Video_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Han_VISOLO_Grid-Based_Space-Time_Aggregation_for_Efficient_Online_Video_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-2.91173190e-01 -2.70091712e-01 -2.07896873e-01 -2.39181221e-01
-6.39402509e-01 -5.81895709e-01 1.93173945e-01 -4.25670035e-02
-6.40226543e-01 6.02249920e-01 -7.27147087e-02 -1.31095052e-01
1.22281559e-01 -8.00403357e-01 -7.90585279e-01 -3.91533554e-01
-1.69529855e-01 4.36710119e-02 8.99448097e-01 -1.16614774... | [9.104352951049805, -0.08056142181158066] |
1d2693a5-ac86-4882-9d63-a81f95d7197f | show-dont-tell-demonstrations-outperform | null | null | https://aclanthology.org/2022.naacl-main.336 | https://aclanthology.org/2022.naacl-main.336.pdf | Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue | Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge. Recent works have leveraged natural language descriptions of schema elements to enable such systems; however, descriptions only indirectly convey schema semantics. In t... | ['Yonghui Wu', 'Abhinav Rastogi', 'Yuan Cao', 'Jeffrey Zhao', 'Harrison Lee', 'Raghav Gupta'] | null | null | null | null | naacl-2022-7 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.66303024e-01 6.90144718e-01 -3.95551175e-01 -8.96009922e-01
-1.01703775e+00 -8.16236496e-01 9.82216179e-01 2.11050764e-01
-2.01722413e-01 8.92111659e-01 7.12174356e-01 -1.90535158e-01
2.48800367e-01 -5.64948678e-01 -4.18210000e-01 -1.33510688e-02
1.23713188e-01 9.81017888e-01 4.48599696e-01 -1.10220706... | [12.700191497802734, 7.896152973175049] |
2f239409-5c55-457b-9cb1-d5cdc353b11e | a-one-class-classification-method-based-on | null | null | https://doi.org/10.1016/j.inffus.2022.07.023 | https://doi.org/10.1016/j.inffus.2022.07.023 | A One-Class Classification method based on Expanded Non-Convex Hulls | This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method ... | ['Bertha Guijarro-Berdiñas', 'Oscar Fontenla-Romero', 'David Novoa-Paradela'] | 2022-08-01 | null | null | null | information-fusion-2022-8 | ['supervised-anomaly-detection', 'one-class-classification'] | ['computer-vision', 'miscellaneous'] | [ 8.60456899e-02 3.54748100e-01 2.00205445e-01 -2.13235945e-01
-1.89738750e-01 -7.87079751e-01 5.02171993e-01 3.26556921e-01
-3.34929347e-01 6.51482403e-01 -1.44209594e-01 -2.91191459e-01
-4.41084862e-01 -1.09635687e+00 -2.21649170e-01 -8.25509608e-01
-2.50842035e-01 1.46392429e+00 3.83198023e-01 -1.11067453... | [7.648467063903809, 4.332597255706787] |
6a296ea7-4eed-4668-b230-887b6778ab68 | impact-of-naturalistic-field-acoustic | 2201.13246 | null | https://arxiv.org/abs/2201.13246v1 | https://arxiv.org/pdf/2201.13246v1.pdf | Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System | Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process. Forensic speech data as potential evidence can be obtained in r... | ['John H. L. Hansen', 'Zhenyu Wang'] | 2022-01-28 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.22754388e-01 -6.34503663e-01 8.64958942e-01 -4.61428791e-01
-1.36206961e+00 -7.50678718e-01 2.56334931e-01 2.29957372e-01
-3.49032104e-01 5.20008147e-01 5.60488462e-01 -3.89058679e-01
-1.46249726e-01 -9.27895159e-02 -5.86813450e-01 -7.77269185e-01
-1.89106509e-01 2.11675093e-01 1.03449531e-01 1.16055384... | [14.118186950683594, 5.891513824462891] |
57e9419a-88ee-4ac9-9845-9c7bd1d4fc73 | activation-template-matching-loss-for | 2207.02179 | null | https://arxiv.org/abs/2207.02179v1 | https://arxiv.org/pdf/2207.02179v1.pdf | Activation Template Matching Loss for Explainable Face Recognition | Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. Th... | ['Linlin Shen', 'Qiufu Li', 'Haozhe Liu', 'Huawei Lin'] | 2022-07-05 | null | null | null | null | ['template-matching', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [ 5.94947971e-02 6.11796498e-01 -2.65300184e-01 -9.66151834e-01
-1.27661660e-01 -2.98228145e-01 3.11941117e-01 -7.30779648e-01
4.27795947e-01 4.15837348e-01 -1.09033667e-01 -3.51442699e-03
-1.81598976e-01 -5.54931939e-01 -8.44970167e-01 -4.73893791e-01
-1.22245010e-02 1.46498710e-01 -5.72302282e-01 1.47237390... | [13.219259262084961, 0.5636754631996155] |
20d3d700-65c8-4ff2-9528-06731ee4784f | structure-transformed-texture-enhanced | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Structure-Transformed_Texture-Enhanced_Network_for_Person_Image_Synthesis_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Structure-Transformed_Texture-Enhanced_Network_for_Person_Image_Synthesis_ICCV_2021_paper.pdf | Structure-Transformed Texture-Enhanced Network for Person Image Synthesis | Pose-guided virtual try-on task aims to modify the fashion item based on pose transfer task. These two tasks that belong to person image synthesis have strong correlations and similarities. However, existing methods treat them as two individual tasks and do not explore correlations between them. Moreover, these two... | ['Ge Li', 'Thomas H. Li', 'Shan Liu', 'Yuanqi Chen', 'Munan Xu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 2.94846177e-01 -5.28150760e-02 2.06477165e-01 -2.53464371e-01
-2.66902059e-01 -5.11646986e-01 7.10554123e-01 -8.94323885e-01
-1.33999005e-01 5.66093326e-01 2.45923787e-01 3.12541336e-01
2.62747258e-01 -7.66085446e-01 -8.08329165e-01 -6.31453931e-01
6.12139523e-01 3.94140452e-01 1.17229164e-01 -5.45741856... | [12.017045021057129, -0.7993993759155273] |
56d7d829-e4a0-44df-a68e-03e90de73b3f | image-clustering-with-contrastive-learning | 2207.07173 | null | https://arxiv.org/abs/2207.07173v1 | https://arxiv.org/pdf/2207.07173v1.pdf | Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks | Deep clustering has recently attracted significant attention. Despite the remarkable progress, most of the previous deep clustering works still suffer from two limitations. First, many of them focus on some distribution-based clustering loss, lacking the ability to exploit sample-wise (or augmentation-wise) relationshi... | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Dong Huang', 'Yuanku Xu'] | 2022-07-14 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 1.87394582e-02 -1.36388287e-01 -5.63931428e-02 -3.12108099e-01
-6.86164558e-01 -1.50777966e-01 6.28728628e-01 1.81623772e-01
-2.30115235e-01 1.98292837e-01 -3.15172076e-02 -7.84912612e-03
-3.03570449e-01 -8.35558176e-01 -7.61821330e-01 -1.18584883e+00
-2.04431817e-01 4.63700384e-01 1.80766970e-01 -4.31222515... | [9.108190536499023, 3.316051721572876] |
50ded73c-aed5-4fb9-927b-ec073d43fc75 | spatio-temporal-graph-mixformer-for-traffic | null | null | https://doi.org/10.1016/j.eswa.2023.120281 | https://doi.org/10.1016/j.eswa.2023.120281 | Spatio-Temporal Graph Mixformer for Traffic Forecasting | Traffic forecasting is of great importance for intelligent transportation systems (ITS). Because of the intricacy implied in traffic behavior and the non-Euclidean nature of traffic data, it is challenging to give an accurate traffic prediction. Despite that previous studies considered the relationship between differen... | ['Yanming Shen', 'Mourad Lablack'] | 2023-10-15 | null | null | null | expert-systems-with-applications-2023-10 | ['traffic-prediction'] | ['time-series'] | [-2.13337421e-01 -2.84364879e-01 -3.34924221e-01 -5.02421856e-01
-1.84998035e-01 -1.93342656e-01 6.56911790e-01 -1.22886978e-01
-1.86889216e-01 5.85177124e-01 2.07205489e-01 -6.04908884e-01
-2.15050042e-01 -1.00937450e+00 -6.78691924e-01 -6.78642273e-01
-2.44411185e-01 3.12452316e-01 5.39692461e-01 -3.39257270... | [6.461283206939697, 2.0155978202819824] |
392d2cf6-e2f7-4ab6-86fc-030f0ad6eb27 | identifying-implicitly-abusive-remarks-about | null | null | https://aclanthology.org/2022.naacl-main.410 | https://aclanthology.org/2022.naacl-main.410.pdf | Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach | We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”). Implicitly abusive language are utterances not conveyed by abusive words (e.g. “bimbo” or “scum”). So far, the detecti... | ['Josef Ruppenhofer', 'Elisabeth Eder', 'Michael Wiegand'] | null | null | null | null | naacl-2022-7 | ['abusive-language'] | ['natural-language-processing'] | [ 1.73710302e-01 2.87197709e-01 -3.86363834e-01 -6.66560709e-01
-4.09179270e-01 -9.31722403e-01 1.16556478e+00 3.04420292e-01
-3.24209511e-01 8.54561627e-01 5.66421509e-01 -2.42230237e-01
2.01063246e-01 -7.46499717e-01 -2.74352580e-01 -6.59173667e-01
1.20661579e-01 6.18089080e-01 -3.89810830e-01 -6.37405217... | [8.747307777404785, 10.452218055725098] |
bc8c2857-8f5d-42f9-908c-3a26ae4b9481 | lightweight-sound-event-detection-model-with | null | null | https://aclanthology.org/2022.rocling-1.17 | https://aclanthology.org/2022.rocling-1.17.pdf | Lightweight Sound Event Detection Model with RepVGG Architecture | In this paper, we proposed RepVGGRNN, which is a light weight sound event detection model. We use RepVGG convolution blocks in the convolution part to improve performance, and re-parameterize the RepVGG blocks after the model is trained to reduce the parameters of the convolution layers. To further improve the accuracy... | ['Wei-Yu Chen', 'Hsiang-Feng Chuang', 'Yu-Han Cheng', 'Bo-Cheng Chan', 'Chung-Li Lu', 'Chia-Ping Chen', 'Sung-Jen Huang', 'Chia-Chuan Liu'] | null | null | null | null | rocling-2022-11 | ['sound-event-detection'] | ['audio'] | [-2.48294026e-01 6.50528818e-03 3.07717949e-01 -1.65084392e-01
-7.85494268e-01 -1.74838707e-01 1.61720887e-01 -2.97982395e-01
-7.68800020e-01 3.48576039e-01 7.11451545e-02 -3.23128909e-01
3.86380494e-01 -7.05877662e-01 -7.02557623e-01 -8.74214172e-01
7.56493164e-03 -4.03811455e-01 5.57344437e-01 2.98787922... | [15.16569709777832, 5.278792381286621] |
ae546a4f-3dd2-447f-900a-09357f072e1b | symmetry-informed-geometric-representation | 2306.09375 | null | https://arxiv.org/abs/2306.09375v1 | https://arxiv.org/pdf/2306.09375v1.pdf | Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials | Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery. For these scientific problems, molecules serve as the fundamental building blocks, and machine l... | ['Jian Tang', 'Hongyu Guo', 'Jennifer Chayes', 'Christian Borgs', 'Anima Anandkumar', 'Omar Yaghi', 'ZhiMing Ma', 'Chenru Duan', 'Zhiling Zheng', 'Zhuoxinran Li', 'Yanjing Li', 'Weitao Du', 'Shengchao Liu'] | 2023-06-15 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 8.61102864e-02 -1.90478668e-01 -4.70701009e-01 -8.46119970e-02
-4.81040210e-01 -6.72586501e-01 7.03043640e-01 4.32145536e-01
3.87682952e-02 6.17417216e-01 2.06881702e-01 -6.64022744e-01
-2.14678466e-01 -9.45289016e-01 -6.11690104e-01 -8.49464357e-01
1.03040010e-01 5.93290508e-01 -1.84818685e-01 1.36454646... | [5.111411094665527, 5.793027400970459] |
d08e181b-2d4c-4ae0-9691-df710bf60268 | deciwatch-a-simple-baseline-for-10x-efficient | 2203.08713 | null | https://arxiv.org/abs/2203.08713v2 | https://arxiv.org/pdf/2203.08713v2.pdf | DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation | This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective... | ['Qiang Xu', 'Bo Dai', 'Xizhou Zhu', 'Ruiyuan Gao', 'Lei Yang', 'Xuan Ju', 'Ailing Zeng'] | 2022-03-16 | null | null | null | null | ['3d-pose-estimation', '2d-human-pose-estimation', '3d-shape-reconstruction-from-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.25023199e-02 2.71003358e-02 -1.81140363e-01 -1.42951146e-01
-7.51153529e-01 -5.90416640e-02 4.48516309e-02 -3.87091279e-01
-1.84512451e-01 4.72431004e-01 5.64518869e-01 5.03458917e-01
2.14413583e-01 -6.49528265e-01 -7.89683402e-01 -4.21236277e-01
-1.82138816e-01 7.36499548e-01 3.50617737e-01 -1.62979454... | [7.187933444976807, -0.7864431142807007] |
efd994b7-b628-4a0c-bd90-48d4f4458c9f | open-world-text-specified-object-counting | 2306.01851 | null | https://arxiv.org/abs/2306.01851v1 | https://arxiv.org/pdf/2306.01851v1.pdf | Open-world Text-specified Object Counting | Our objective is open-world object counting in images, where the target object class is specified by a text description. To this end, we propose CounTX, a class-agnostic, single-stage model using a transformer decoder counting head on top of pre-trained joint text-image representations. CounTX is able to count the numb... | ['Andrew Zisserman', 'Tengda Han', 'Kiana Amini-Naieni', 'Niki Amini-Naieni'] | 2023-06-02 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.94865823e-01 -2.66303629e-01 -1.44721746e-01 -5.24385095e-01
-1.04452872e+00 -7.70180523e-01 1.02110255e+00 2.01479912e-01
-9.04113889e-01 4.34217066e-01 8.22489187e-02 -2.65227735e-01
2.90259659e-01 -7.56956279e-01 -9.11305130e-01 -9.14127380e-02
1.13435969e-01 1.14750361e+00 3.40887994e-01 3.64274472... | [9.166144371032715, 0.6350067257881165] |
4375b4ae-a43f-48a0-84ac-ab116be8ae1a | ebsr-enhanced-binary-neural-network-for-image | 2303.12270 | null | https://arxiv.org/abs/2303.12270v1 | https://arxiv.org/pdf/2303.12270v1.pdf | EBSR: Enhanced Binary Neural Network for Image Super-Resolution | While the performance of deep convolutional neural networks for image super-resolution (SR) has improved significantly, the rapid increase of memory and computation requirements hinders their deployment on resource-constrained devices. Quantized networks, especially binary neural networks (BNN) for SR have been propose... | ['Ru Huang', 'Runsheng Wang', 'Yuchen Fan', 'Meng Li', 'Zechun Liu', 'Shuwen Zhang', 'Renjie Wei'] | 2023-03-22 | null | null | null | null | ['image-super-resolution', 'image-variation'] | ['computer-vision', 'computer-vision'] | [ 7.01208293e-01 -3.32796574e-01 -1.57504052e-01 -4.46386188e-01
-6.64489448e-01 -1.17511503e-01 1.16257988e-01 -3.37585270e-01
-4.38691020e-01 9.01364803e-01 1.40182763e-01 -2.00196087e-01
-5.11764511e-02 -8.00903141e-01 -6.84511244e-01 -7.88265288e-01
-1.02777764e-01 -6.26048863e-01 6.38720393e-01 -2.92983651... | [11.004769325256348, -1.987963318824768] |
01bcccc1-5c89-47aa-a068-47135a807368 | contrastive-mixture-of-posteriors-for | 2106.08161 | null | https://arxiv.org/abs/2106.08161v4 | https://arxiv.org/pdf/2106.08161v4.pdf | Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness | Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology. Adopting a Conditional VAE framework, we show that marginal independence between the representation and a condition v... | ['Aaron Sim', 'Sam Abujudeh', 'Páidí Creed', 'Craig A Glastonbury', 'Árpi Vezér', 'Adam Foster'] | 2021-06-15 | null | https://openreview.net/forum?id=AjZCiKuWQ9n | https://openreview.net/pdf?id=AjZCiKuWQ9n | neurips-2021-12 | ['data-integration', 'counterfactual-inference'] | ['knowledge-base', 'miscellaneous'] | [ 9.75536168e-01 -2.47676969e-02 -2.12397039e-01 -3.37479174e-01
-1.23393786e+00 -5.70842743e-01 8.60396504e-01 1.82913974e-01
-4.72696632e-01 1.18982077e+00 5.80596864e-01 -3.49131495e-01
-3.08601648e-01 -1.82791799e-01 -1.03837013e+00 -1.22639644e+00
2.21288636e-01 6.03126943e-01 -4.33474332e-01 1.35138854... | [6.963874340057373, 5.123685836791992] |
4273db44-2b1c-49fb-a2d0-0fee2db79c0b | graph-sampling-based-meta-learning-for | 2306.16780 | null | https://arxiv.org/abs/2306.16780v1 | https://arxiv.org/pdf/2306.16780v1.pdf | Graph Sampling-based Meta-Learning for Molecular Property Prediction | Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-... | ['Huajun Chen', 'Yin Fang', 'Keyan Ding', 'Bin Wu', 'Qiang Zhang', 'Xiang Zhuang'] | 2023-06-29 | null | null | null | null | ['graph-sampling', 'property-prediction', 'meta-learning', 'molecular-property-prediction'] | ['graphs', 'medical', 'methodology', 'miscellaneous'] | [ 3.08970690e-01 -1.66254893e-01 -8.20513070e-01 -2.56988019e-01
-7.70872295e-01 -3.85830045e-01 2.97732532e-01 6.33796096e-01
6.99815080e-02 1.14509284e+00 -3.00790798e-02 1.64316837e-02
-1.78669304e-01 -1.11275542e+00 -8.92726064e-01 -9.27014649e-01
-1.42743900e-01 2.27572903e-01 3.10826242e-01 1.54147655... | [5.2247314453125, 5.94139289855957] |
f5ec1e31-348f-4a6b-ae7d-f397f793b67a | splicecombo-a-hybrid-technique-efficiently | 1907.09401 | null | http://arxiv.org/abs/1907.09401v1 | http://arxiv.org/pdf/1907.09401v1.pdf | SpliceCombo: A Hybrid Technique efficiently use for Principal Component Analysis of Splice Site Prediction | The primary step in search of the gene prediction is an identification of the
coding region from genomic DNA sequence. Gene structure in the case of a
eukaryotic organism is composed of promoter, intron, start codon, exons, stop
codon, etc. Splice site prediction, which separates the junction between exon
and intron, t... | [] | 2019-07-19 | null | null | null | null | ['splice-site-prediction'] | ['medical'] | [ 3.54395419e-01 -7.38437101e-02 4.92549911e-02 -1.34571344e-01
-4.90441918e-01 -4.80611295e-01 2.04033345e-01 3.04213669e-02
-2.36971349e-01 9.11593139e-01 -5.88335982e-03 -3.46214622e-01
-2.83528101e-02 -6.39318585e-01 -1.44930109e-01 -1.10886335e+00
3.83855879e-01 4.98378664e-01 2.03627750e-01 2.99815126... | [4.815533638000488, 5.451318264007568] |
32ec48be-7099-4b6d-9c77-8be6ba3f16ec | reliable-multimodal-trajectory-prediction-via | 2212.04812 | null | https://arxiv.org/abs/2212.04812v1 | https://arxiv.org/pdf/2212.04812v1.pdf | Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization | Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is challenging as ground truth for uncertainty estimates is not available. Ideally, in a well... | ['Michael Paulitsch', 'Omesh Tickoo', 'Akash Dhamasia', 'Ranganath Krishnan', 'Neslihan Kose'] | 2022-12-09 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-1.48061246e-01 1.88341156e-01 -3.94186646e-01 -9.96490836e-01
-1.31842506e+00 -4.39877331e-01 5.05845428e-01 1.34562343e-01
-8.30871165e-01 1.22413421e+00 1.98356420e-01 -4.28508878e-01
-7.26429820e-02 -6.72121942e-01 -1.29421222e+00 -5.38598776e-01
1.01365685e-01 4.59272474e-01 3.19303572e-02 2.68277436... | [7.48942756652832, 3.836200714111328] |
eb978c2f-54dd-4033-93bb-008d5a21d952 | deep-partial-multi-label-learning-with-graph | 2305.05882 | null | https://arxiv.org/abs/2305.05882v1 | https://arxiv.org/pdf/2305.05882v1.pdf | Deep Partial Multi-Label Learning with Graph Disambiguation | In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevale... | ['Gang Chen', 'Songhe Feng', 'Ke Chen', 'Tianlei Hu', 'Weiwei Liu', 'Gengyu Lyu', 'Shisong Yang', 'Haobo Wang'] | 2023-05-10 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 3.05664480e-01 1.28024489e-01 -4.17165875e-01 -6.90974653e-01
-1.32648981e+00 -4.71712649e-01 3.61421138e-01 5.48693299e-01
-7.02934191e-02 7.52817452e-01 -4.68048573e-01 -2.93368232e-02
-2.11238042e-01 -8.11206520e-01 -6.00611746e-01 -7.16300488e-01
1.21664152e-01 6.28878772e-01 2.02093109e-01 3.87664616... | [9.528903007507324, 4.040844440460205] |
82b8abad-01f9-4abe-a9ce-474f12990c7e | mapconnet-self-supervised-3d-pose-transfer | 2304.13819 | null | https://arxiv.org/abs/2304.13819v1 | https://arxiv.org/pdf/2304.13819v1.pdf | MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning | 3D pose transfer is a challenging generation task that aims to transfer the pose of a source geometry onto a target geometry with the target identity preserved. Many prior methods require keypoint annotations to find correspondence between the source and target. Current pose transfer methods allow end-to-end correspond... | ['Tae-Kyun Kim', 'Zhixiang Chen', 'Jiaze Sun'] | 2023-04-26 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.88044333e-01 4.63900059e-01 -1.83316424e-01 -5.88896871e-01
-9.79634881e-01 -7.90392995e-01 8.03562403e-01 2.56171405e-01
-3.57559770e-02 3.72167021e-01 7.90179670e-02 2.21129403e-01
2.34070979e-03 -7.82916427e-01 -1.08186162e+00 -3.79146844e-01
-1.74583435e-01 1.30796385e+00 3.69103789e-01 -1.49123803... | [8.173246383666992, -2.648322105407715] |
bd9a5791-f8a0-4b50-bacf-c9693a4ca433 | time-distributed-feature-learning-in-network | 2109.14696 | null | https://arxiv.org/abs/2109.14696v1 | https://arxiv.org/pdf/2109.14696v1.pdf | Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things | The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network da... | ['Xiao-Ping Zhang', 'Sihao Zhao', 'Yoga Suhas Kuruba Manjunath'] | 2021-09-29 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-5.97454654e-03 -7.12365031e-01 -4.76230323e-01 -6.37625337e-01
9.74269435e-02 -1.91327319e-01 5.57860136e-01 -4.00694788e-01
-2.27280617e-01 6.66569710e-01 -1.85934976e-01 -6.42815948e-01
-5.77724397e-01 -9.91351604e-01 -3.07704329e-01 -7.16175318e-01
-4.69422042e-01 2.74093211e-01 6.70387149e-01 2.29399383... | [5.065375328063965, 7.231903553009033] |
2f6fa269-d819-4c2e-9f51-8430a058a7d5 | factorising-meaning-and-form-for-intent | 2105.15053 | null | https://arxiv.org/abs/2105.15053v1 | https://arxiv.org/pdf/2105.15053v1.pdf | Factorising Meaning and Form for Intent-Preserving Paraphrasing | We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an enco... | ['Mirella Lapata', 'Tom Hosking'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-long.112 | https://aclanthology.org/2021.acl-long.112.pdf | acl-2021-5 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.47846738e-01 4.29860771e-01 -1.73051253e-01 -4.31878030e-01
-1.03982329e+00 -8.71938705e-01 6.16776586e-01 1.71791494e-01
-3.72119159e-01 6.61216497e-01 4.28774416e-01 -2.43393451e-01
8.00532475e-02 -1.03096712e+00 -8.54854882e-01 -4.42728460e-01
5.73290408e-01 6.53104186e-01 -4.42209514e-03 -2.51426309... | [11.678594589233398, 9.271889686584473] |
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