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35a3412d-18c2-444b-94b5-7a1eabdc92c6 | no-blind-spots-full-surround-multi-object | 1802.08755 | null | http://arxiv.org/abs/1802.08755v4 | http://arxiv.org/pdf/1802.08755v4.pdf | No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs | Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (vision and range) and a variable number of sensors, to produce
continuous object tracks. This work is a generalization of the MDP framework
for MOT, with some key extensions - First, we track objects across multiple
cameras and across different sensor modalities. This is done by fusing object
proposals across sensors accurately and efficiently. Second, the objects of
interest (targets) are tracked directly in the real world. This is a departure
from traditional techniques where objects are simply tracked in the image
plane. Doing so allows the tracks to be readily used by an autonomous agent for
navigation and related tasks.
To verify the effectiveness of our approach, we test it on real world highway
data collected from a heavily sensorized testbed capable of capturing
full-surround information. We demonstrate that our framework is well-suited to
track objects through entire maneuvers around the ego-vehicle, some of which
take more than a few minutes to complete. We also leverage the modularity of
our approach by comparing the effects of including/excluding different sensors,
changing the total number of sensors, and the quality of object proposals on
the final tracking result. | ['Akshay Rangesh', 'Mohan M. Trivedi'] | 2018-02-23 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-6.48000687e-02 -9.00203586e-02 -6.15092292e-02 -2.21411407e-01
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495adf42-b0b0-40b6-a7af-521cd472c4ba | auto-regressive-image-synthesis-with | 2207.10776 | null | https://arxiv.org/abs/2207.10776v1 | https://arxiv.org/pdf/2207.10776v1.pdf | Auto-regressive Image Synthesis with Integrated Quantization | Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression that naturally leads to diverse image generation. Instead of independently quantizing the features of multiple domains as in prior research, we design an integrated quantization scheme with a variational regularizer that mingles the feature discretization in multiple domains, and markedly boosts the auto-regressive modeling performance. Notably, the variational regularizer enables to regularize feature distributions in incomparable latent spaces by penalizing the intra-domain variations of distributions. In addition, we design a Gumbel sampling strategy that allows to incorporate distribution uncertainty into the auto-regressive training procedure. The Gumbel sampling substantially mitigates the exposure bias that often incurs misalignment between the training and inference stages and severely impairs the inference performance. Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art. | ['Shijian Lu', 'Changgong Zhang', 'Kaiwen Cui', 'Jiahui Zhang', 'Rongliang Wu', 'Yingchen Yu', 'Fangneng Zhan'] | 2022-07-21 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.04918075e-01 -1.03214569e-02 5.70937991e-02 -3.05421859e-01
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8eb8a97f-b985-43a0-b637-290e8bca7140 | interior-attention-aware-network-for-infrared | null | null | https://ieeexplore.ieee.org/document/9745054 | https://ieeexplore.ieee.org/document/9745054 | Interior Attention-Aware Network for Infrared Small Target Detection | Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of the local information of one pixel, however, is not sufficient for accurate detection, which may lead to missing detection and false alarms. As a harmonious whole, information between pixels are necessary to determine if a pixel belongs to the target or the background.
Motivated by the fact that pixels from targets or backgrounds are correlated to each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection. Specifically, a region proposal network (RPN) is first applied to obtain coarse target regions and filter out backgrounds. Then, we leverage a transformer encoder to model the attention between pixels in coarse target regions, outputting attention-aware features. Finally, predictions are obtained by feeding attention-aware features to a classification head. Extensive experiments show that our approach is capable of detecting targets precisely, of suppressing a variety of false alarm sources, and works effectively in various background environments and target appearances. We show that our IAANet outperforms the state-of-the-art methods by a large margin. Code will be made available at: https://github.com/kwwcv/iaanet. | ['Zhiguo Cao', 'Chengxin Liu', 'Shuaiyuan Du', 'Kewei Wang'] | 2022-03-30 | null | null | null | ieee-transactions-on-geoscience-and-remote-8 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 5.80631614e-01 -3.09925616e-01 -2.47799084e-01 -2.53628671e-01
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30371005-77ca-4215-ae99-885c9f0ca500 | dont-take-it-literally-an-edit-invariant | null | null | https://aclanthology.org/2022.naacl-main.150 | https://aclanthology.org/2022.naacl-main.150.pdf | Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence. Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available. To address the challenge, we propose a novel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of a target n-gram with all n-grams in the generated sequence. EISL is designed to be robust to various noises and edits in the target sequences. Moreover, the EISL computation is essentially an approximate convolution operation with target n-grams as kernels, which is easy to implement and efficient to compute with existing libraries. To demonstrate the effectiveness of EISL, we conduct experiments on a wide range of tasks, including machine translation with noisy target sequences, unsupervised text style transfer with only weak training signals, and non-autoregressive generation with non-predefined generation order. Experimental results show our method significantly outperforms the common CE loss and other strong baselines on all the tasks. EISL has a simple API that can be used as a drop-in replacement of the CE loss: https://github.com/guangyliu/EISL. | ['Zhiting Hu', 'Shuguang Cui', 'Xiaodong He', 'Zhen Li', 'Junwei Bao', 'Xiaodan Liang', 'Tianhua Tao', 'Zichao Yang', 'Guangyi Liu'] | null | null | null | null | naacl-2022-7 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.93326211e-01 -1.35633528e-01 -3.31998952e-02 -3.10906321e-01
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e04af4e6-a9f5-4645-9db7-33e611bbed3b | explainable-graph-pyramid-autoformer-for-long | 2209.13123 | null | https://arxiv.org/abs/2209.13123v1 | https://arxiv.org/pdf/2209.13123v1.pdf | Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting | Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35 % better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic. | ['Prasanna Balaprakash', 'Jane Macfarlane', 'Hadi Meidani', 'Tanwi Mallick', 'Weiheng Zhong'] | 2022-09-27 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-4.13219303e-01 4.36527655e-02 -5.33536136e-01 -6.11261785e-01
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0afed527-0f6d-4b42-a7c3-bb06d76127e1 | covid-cxnet-detecting-covid-19-in-frontal | 2006.13807 | null | https://arxiv.org/abs/2006.13807v2 | https://arxiv.org/pdf/2006.13807v2.pdf | COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning | One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system. | ['Mahdiyar Molahasani Majdabadi', 'Younhee Choi', 'Arman Haghanifar', 'Seokbum Ko', 'S. Deivalakshmi'] | 2020-06-16 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.19433033e-01 -6.59598053e-01 1.14889313e-02 -3.07306737e-01
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c116c467-9120-444e-a388-a49e1f18db8c | composing-rnns-and-fsts-for-small-data-1 | 2208.10248 | null | https://arxiv.org/abs/2208.10248v1 | https://arxiv.org/pdf/2208.10248v1.pdf | Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text | In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model. One method is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN). We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data. | ['Brendan Shillingford', 'Oiwi Parker Jones'] | 2022-07-24 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.97107419e-01 2.75468528e-01 1.18425146e-01 -2.88443863e-01
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b07cc47e-aa2f-41bd-9ed2-5dd7090a132d | extending-the-service-composition-formalism | 1909.04393 | null | https://arxiv.org/abs/1909.04393v1 | https://arxiv.org/pdf/1909.04393v1.pdf | Extending the Service Composition Formalism with Relational Parameters | Web Service Composition deals with the (re)use of Web Services to provide complex functionality, inexistent in any single service. Over the state-of-the-art, we introduce a new type of modeling, based on ontologies and relations between objects, which allows us to extend the expressiveness of problems that can be solved automatically. | ['Radu Mereuta', 'Liana Tucar', 'Paul Diac'] | 2019-09-10 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-1.21064804e-01 4.55037892e-01 2.12189376e-01 -4.52639610e-01
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3190b31b-f9f2-4e28-87be-1482a0bb9dcc | speak-memory-an-archaeology-of-books-known-to | 2305.00118 | null | https://arxiv.org/abs/2305.00118v1 | https://arxiv.org/pdf/2305.00118v1.pdf | Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4 | In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks. We argue that this supports a case for open models whose training data is known. | ['David Bamman', 'Sandeep Soni', 'Mackenzie Cramer', 'Kent K. Chang'] | 2023-04-28 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-4.16168571e-01 3.19434136e-01 -2.92217880e-01 -1.23073392e-01
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ef713d66-3999-461a-8390-16fd090334c5 | differentiable-signal-processing-with-black | 2105.04752 | null | https://arxiv.org/abs/2105.04752v1 | https://arxiv.org/pdf/2105.04752v1.pdf | Differentiable Signal Processing With Black-Box Audio Effects | We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network. We then train a deep encoder to analyze input audio and control effect parameters to perform the desired signal manipulation, requiring only input-target paired audio data as supervision. To train our network with non-differentiable black-box effects layers, we use a fast, parallel stochastic gradient approximation scheme within a standard auto differentiation graph, yielding efficient end-to-end backpropagation. We demonstrate the power of our approach with three separate automatic audio production applications: tube amplifier emulation, automatic removal of breaths and pops from voice recordings, and automatic music mastering. We validate our results with a subjective listening test, showing our approach not only can enable new automatic audio effects tasks, but can yield results comparable to a specialized, state-of-the-art commercial solution for music mastering. | ['Nicholas J. Bryan', 'Paris Smaragdis', 'Oliver Wang', 'Marco A. Martínez Ramírez'] | 2021-05-11 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.89566690e-01 -5.14567457e-02 4.92914617e-01 -9.83779803e-02
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71932c6c-936c-4c40-8752-06133317ec35 | liveseg-unsupervised-multimodal-temporal | 2210.05840 | null | https://arxiv.org/abs/2210.05840v1 | https://arxiv.org/pdf/2210.05840v1.pdf | LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos | Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials. However, Livestream tutorial videos are usually hours long, recorded, and uploaded to the Internet directly after the live sessions, making it hard for other people to catch up quickly. An outline will be a beneficial solution, which requires the video to be temporally segmented according to topics. In this work, we introduced a large Livestream video dataset named MultiLive, and formulated the temporal segmentation of the long Livestream videos (TSLLV) task. We propose LiveSeg, an unsupervised Livestream video temporal Segmentation solution, which takes advantage of multimodal features from different domains. Our method achieved a $16.8\%$ F1-score performance improvement compared with the state-of-the-art method. | ['Hailin Jin', 'Ding Zhao', 'Zhaowen Wang', 'Trung Bui', 'Franck Dernoncourt', 'JieLin Qiu'] | 2022-10-12 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.20754123e-01 -3.70783538e-01 -3.90563548e-01 -4.52854007e-01
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e4717504-43d6-4040-ad9a-c3ded9076b37 | deep-reinforcement-learning-for-time-1 | null | null | https://doi.org/10.1109/WCNC51071.2022.9771580 | https://hdl.handle.net/10356/155437 | Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication | Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment. | ['G David González', 'Yong Liang Guan', 'Dusit Niyato', 'Yanyu Cheng', 'Joash Lee'] | 2022-05-19 | null | null | null | ieee-wireless-communications-and-networking | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'joint-radar-communication', 'intelligent-communication'] | ['medical', 'reasoning', 'robots', 'time-series'] | [ 7.83460960e-02 2.23944336e-01 -3.51182520e-01 -4.12651867e-01
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841755da-985e-4c90-9e55-8a9bc3847505 | semi-supervised-clustering-of-sparse-graphs | 2205.11677 | null | https://arxiv.org/abs/2205.11677v2 | https://arxiv.org/pdf/2205.11677v2.pdf | Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold | The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data. Decades of extensive study on the problem have established many profound results, among which the phase transition at the Kesten-Stigum threshold is particularly interesting both from a mathematical and an applied standpoint. It states that no estimator based on the network topology can perform substantially better than chance on sparse graphs if the model parameter is below certain threshold. Nevertheless, if we slightly extend the horizon to the ubiquitous semi-supervised setting, such a fundamental limitation will disappear completely. We prove that with arbitrary fraction of the labels revealed, the detection problem is feasible throughout the parameter domain. Moreover, we introduce two efficient algorithms, one combinatorial and one based on optimization, to integrate label information with graph structures. Our work brings a new perspective to stochastic model of networks and semidefinite program research. | ['Thomas Strohmer', 'JunDa Sheng'] | 2022-05-24 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 5.12643576e-01 5.40879905e-01 -7.36362875e-01 -4.91981842e-02
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187a0e08-1546-4b20-819b-dfb40c875c33 | segmentation-from-natural-language | 1603.06180 | null | http://arxiv.org/abs/1603.06180v1 | http://arxiv.org/pdf/1603.06180v1.pdf | Segmentation from Natural Language Expressions | In this paper we approach the novel problem of segmenting an image based on a
natural language expression. This is different from traditional semantic
segmentation over a predefined set of semantic classes, as e.g., the phrase
"two men sitting on the right bench" requires segmenting only the two people on
the right bench and no one standing or sitting on another bench. Previous
approaches suitable for this task were limited to a fixed set of categories
and/or rectangular regions. To produce pixelwise segmentation for the language
expression, we propose an end-to-end trainable recurrent and convolutional
network model that jointly learns to process visual and linguistic information.
In our model, a recurrent LSTM network is used to encode the referential
expression into a vector representation, and a fully convolutional network is
used to a extract a spatial feature map from the image and output a spatial
response map for the target object. We demonstrate on a benchmark dataset that
our model can produce quality segmentation output from the natural language
expression, and outperforms baseline methods by a large margin. | ['Trevor Darrell', 'Ronghang Hu', 'Marcus Rohrbach'] | 2016-03-20 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 7.77849257e-01 4.22500879e-01 -2.72538483e-01 -7.96985090e-01
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153f333e-5811-4083-88c8-9a2cc3759127 | swin-unetr-swin-transformers-for-semantic | 2201.01266 | null | https://arxiv.org/abs/2201.01266v1 | https://arxiv.org/pdf/2201.01266v1.pdf | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images | Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited kernel size of convolution layers in FCNNs, their performance of modeling long-range information is sub-optimal, and this can lead to deficiencies in the segmentation of tumors with variable sizes. On the other hand, transformer models have demonstrated excellent capabilities in capturing such long-range information in multiple domains, including natural language processing and computer vision. Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input data is projected into a 1D sequence of embedding and used as an input to a hierarchical Swin transformer as the encoder. The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip connections. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase. Code: https://monai.io/research/swin-unetr | ['Daguang Xu', 'Holger Roth', 'Dong Yang', 'Yucheng Tang', 'Vishwesh Nath', 'Ali Hatamizadeh'] | 2022-01-04 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 4.52057242e-01 2.56449908e-01 -2.37427667e-01 -2.77134925e-01
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80c98f0e-0602-414b-b3e7-0c60ab8bca05 | improving-fairness-in-facial-albedo | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.pdf | Improving Fairness in Facial Albedo Estimation via Visual-Textual Cues | Recent 3D face reconstruction methods have made significant advances in geometry prediction, yet further cosmetic improvements are limited by lagged albedo because inferring albedo from appearance is an ill-posed problem. Although some existing methods consider prior knowledge from illumination to improve albedo estimation, they still produce a light-skin bias due to racially biased albedo models and limited light constraints. In this paper, we reconsider the relationship between albedo and face attributes and propose an ID2Albedo to directly estimate albedo without constraining illumination. Our key insight is that intrinsic semantic attributes such as race, skin color, and age can constrain the albedo map. We first introduce visual-textual cues and design a semantic loss to supervise facial albedo estimation. Specifically, we pre-define text labels such as race, skin color, age, and wrinkles. Then, we employ the text-image model (CLIP) to compute the similarity between the text and the input image, and assign a pseudo-label to each facial image. We constrain generated albedos in the training phase to have the same attributes as the inputs. In addition, we train a high-quality, unbiased facial albedo generator and utilize the semantic loss to learn the mapping from illumination-robust identity features to the albedo latent codes. Finally, our ID2Albedo is trained in a self-supervised way and outperforms state-of-the-art albedo estimation methods in terms of accuracy and fidelity. It is worth mentioning that our approach has excellent generalizability and fairness, especially on in-the-wild data. | ['Xiaokang Yang', 'Yichao Yan', 'Chao Ma', 'Jiankang Deng', 'Xingyu Ren'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.57519364e-01 4.16928940e-02 -1.19818568e-01 -7.92473912e-01
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667619db-99ea-4901-8239-8f5ad38e70d7 | designing-an-encoder-for-fast-personalization | 2302.12228 | null | https://arxiv.org/abs/2302.12228v3 | https://arxiv.org/pdf/2302.12228v3.pdf | Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models | Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language prompts. However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To overcome these limitations, we propose an encoder-based domain-tuning approach. Our key insight is that by underfitting on a large set of concepts from a given domain, we can improve generalization and create a model that is more amenable to quickly adding novel concepts from the same domain. Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e.g. a specific face, and learns to map it into a word-embedding representing the concept. Second, a set of regularized weight-offsets for the text-to-image model that learn how to effectively ingest additional concepts. Together, these components are used to guide the learning of unseen concepts, allowing us to personalize a model using only a single image and as few as 5 training steps - accelerating personalization from dozens of minutes to seconds, while preserving quality. | ['Daniel Cohen-Or', 'Gal Chechik', 'Amit H. Bermano', 'Yuval Atzmon', 'Moab Arar', 'Rinon Gal'] | 2023-02-23 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.12058854e-01 1.85781792e-01 -7.35341534e-02 -4.61907029e-01
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404ad6ab-f00c-4ea9-8d51-f79c7f2a271b | spatio-focal-bidirectional-disparity | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.pdf | Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image | Dual-pixel photography is monocular RGB-D photography with an ultra-high resolution, enabling many applications in computational photography. However, there are still several challenges to fully utilizing dual-pixel photography. Unlike the conventional stereo pair, the dual pixel exhibits a bidirectional disparity that includes positive and negative values, depending on the focus plane depth in an image. Furthermore, capturing a wide range of dual-pixel disparity requires a shallow depth of field, resulting in a severely blurred image, degrading depth estimation performance. Recently, several data-driven approaches have been proposed to mitigate these two challenges. However, due to the lack of the ground-truth dataset of the dual-pixel disparity, existing data-driven methods estimate either inverse depth or blurriness map. In this work, we propose a self-supervised learning method that learns bidirectional disparity by utilizing the nature of anisotropic blur kernels in dual-pixel photography. We observe that the dual-pixel left/right images have reflective-symmetric anisotropic kernels, so their sum is equivalent to that of a conventional image. We take a self-supervised training approach with the novel kernel-split symmetry loss accounting for the phenomenon. Our method does not rely on a training dataset of dual-pixel disparity that does not exist yet. Our method can estimate a complete disparity map with respect to the focus-plane depth from a dual-pixel image, outperforming the baseline dual-pixel methods. | ['Min H. Kim', 'Inchul Kim', 'Hyeonjoong Jang', 'Donggun Kim'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['disparity-estimation'] | ['computer-vision'] | [ 5.93155563e-01 -1.33219168e-01 6.43159524e-02 -4.04188484e-01
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5a3b7c65-3cd5-4fcf-a3d2-f20cb3d71411 | napss-paragraph-level-medical-text | 2302.05574 | null | https://arxiv.org/abs/2302.05574v1 | https://arxiv.org/pdf/2302.05574v1.pdf | NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization | Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS. | ['Gabriele Pergola', 'Yulan He', 'Byron C. Wallace', 'Jiazheng Li', 'Junru Lu'] | 2023-02-11 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 5.22993743e-01 3.64532471e-01 -3.59808981e-01 -3.43399286e-01
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260d041e-ea2c-4ae8-9dba-f1ea91d17ce3 | semeval-2018-task-9-hypernym-discovery | null | null | https://aclanthology.org/S18-1115 | https://aclanthology.org/S18-1115.pdf | SemEval-2018 Task 9: Hypernym Discovery | This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at \url{https://competitions.codalab.org/competitions/17119}. | ['Tommaso Pasini', 'Luis Espinosa-Anke', 'Jose Camacho-Collados', 'Sergio Oramas', 'Claudio Delli Bovi', 'Roberto Navigli', 'Enrico Santus', 'Vered Shwartz', 'Horacio Saggion'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 8.42478126e-02 3.48578513e-01 -3.70785296e-01 -2.18959048e-01
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211934ac-5b12-4098-9ce7-a49f746df991 | don-t-memorize-mimic-the-past-federated-class | 2307.00497 | null | https://arxiv.org/abs/2307.00497v1 | https://arxiv.org/pdf/2307.00497v1.pdf | Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory | Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines. | ['Salman Avestimehr', 'Mahdi Soltanolkotabi', 'Chaoyang He', 'Zalan Fabian', 'Sara Babakniya'] | 2023-07-02 | null | null | null | null | ['class-incremental-learning', 'continual-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [-2.21092120e-01 1.86614275e-01 -1.98670104e-01 -5.76000154e-01
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8c611faf-033a-4d6b-b248-bbfba960a379 | machine-learning-based-detection-of-parkinson | 2303.01389 | null | https://arxiv.org/abs/2303.01389v1 | https://arxiv.org/pdf/2303.01389v1.pdf | Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study | Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically validated frequency bands, and feature selection before evaluating the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively. | ['Alvaro Fernandez-Quilez', 'Kolbjørn Brønnick', 'John Fredy Ochoa-Gomez', 'Alberto Jaramillo-Jimenez', 'Anna Kurbatskaya'] | 2023-03-02 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 3.41967009e-02 -1.83208540e-01 2.19549745e-01 -2.22829342e-01
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7f015680-af19-4954-823e-dc243c119355 | few-shot-learning-of-compact-models-via-task | 2210.09922 | null | https://arxiv.org/abs/2210.09922v1 | https://arxiv.org/pdf/2210.09922v1.pdf | Few-Shot Learning of Compact Models via Task-Specific Meta Distillation | We consider a new problem of few-shot learning of compact models. Meta-learning is a popular approach for few-shot learning. Previous work in meta-learning typically assumes that the model architecture during meta-training is the same as the model architecture used for final deployment. In this paper, we challenge this basic assumption. For final deployment, we often need the model to be small. But small models usually do not have enough capacity to effectively adapt to new tasks. In the mean time, we often have access to the large dataset and extensive computing power during meta-training since meta-training is typically performed on a server. In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model. These two models are jointly learned during meta-training. Given a new task during meta-testing, the teacher model is first adapted to this task, then the adapted teacher model is used to guide the adaptation of the student model. The adapted student model is used for final deployment. We demonstrate the effectiveness of our approach in few-shot image classification using model-agnostic meta-learning (MAML). Our proposed method outperforms other alternatives on several benchmark datasets. | ['Yang Wang', 'Zhi Liu', 'Mehrdad Hosseinzadeh', 'Shekhor Chanda', 'Yong Wu'] | 2022-10-18 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.01204404e-01 -3.63397747e-02 -2.38060385e-01 -3.90399903e-01
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4320d656-12d0-4e62-9c0d-58bd437bff2e | knodle-modular-weakly-supervised-learning | 2104.11557 | null | https://arxiv.org/abs/2104.11557v3 | https://arxiv.org/pdf/2104.11557v3.pdf | Knodle: Modular Weakly Supervised Learning with PyTorch | Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a software framework that treats weak data annotations, deep learning models, and methods for improving weakly supervised training as separate, modular components. This modularization gives the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction. Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those that harness the interplay of neural networks and weakly labeled data. We illustrate the benchmarking potential of the framework with a performance comparison of several reference implementations on a selection of datasets that are already available in Knodle. The framework is published as an open-source Python package knodle and available at https://github.com/knodle/knodle. | ['Benjamin Roth', 'Marina Speranskaya', 'Andreas Stephan', 'Anastasiia Sedova'] | 2021-04-23 | null | https://aclanthology.org/2021.repl4nlp-1.12 | https://aclanthology.org/2021.repl4nlp-1.12.pdf | acl-repl4nlp-2021-8 | ['weakly-supervised-data-denoising'] | ['natural-language-processing'] | [ 1.13607079e-01 3.90196443e-01 -6.42156005e-01 -6.79502130e-01
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b82fb1d4-fbc3-4348-acc1-7699448a20e5 | self-score-self-supervised-learning-on-score | 2209.00835 | null | https://arxiv.org/abs/2209.00835v1 | https://arxiv.org/pdf/2209.00835v1.pdf | Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction | Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction. Most of these methods require a large amount of fully sampled MRI data as a training set, which, sometimes, is difficult to acquire in practice. This paper proposes a fully-sampled-data-free score-based diffusion model for MRI reconstruction, which learns the fully sampled MR image prior in a self-supervised manner on undersampled data. Specifically, we first infer the fully sampled MR image distribution from the undersampled data by Bayesian deep learning, then perturb the data distribution and approximate their probability density gradient by training a score function. Leveraging the learned score function as a prior, we can reconstruct the MR image by performing conditioned Langevin Markov chain Monte Carlo (MCMC) sampling. Experiments on the public dataset show that the proposed method outperforms existing self-supervised MRI reconstruction methods and achieves comparable performances with the conventional (fully sampled data trained) score-based diffusion methods. | ['Dong Liang', 'Yanjie Zhu', 'Haifeng Wang', 'Jing Cheng', 'Qingyong Zhu', 'Shaonan Liu', 'Chentao Cao', 'Zhuo-Xu Cui'] | 2022-09-02 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.03982645e-01 6.99421614e-02 -2.81281114e-01 -8.43212128e-01
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9aa5b432-0435-4c68-9dd6-c47f034689fe | deep-learning-based-cloud-detection-for | 1810.05801 | null | http://arxiv.org/abs/1810.05801v3 | http://arxiv.org/pdf/1810.05801v3.pdf | Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors | Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF, the symmetric encoder-decoder module, which provides
both local and global context by densifying feature maps with trainable
convolutional filter banks, is utilized to extract multi-scale and high-level
spatial features. The feature maps of multiple scales are then up-sampled and
concatenated, and a novel multi-scale feature fusion module is designed to fuse
the features of different scales for the output. The two output feature maps of
the network are cloud and cloud shadow maps, which are in turn fed to binary
classifiers outside the model to obtain the final cloud and cloud shadow mask.
The MSCFF method was validated on hundreds of globally distributed optical
satellite images, with spatial resolutions ranging from 0.5 to 50 m, including
Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and
collected high-resolution images exported from Google Earth. The experimental
results show that MSCFF achieves a higher accuracy than the traditional
rule-based cloud detection methods and the state-of-the-art deep learning
models, especially in bright surface covered areas. The effectiveness of MSCFF
means that it has great promise for the practical application of cloud
detection for multiple types of medium and high-resolution remote sensing
images. Our established global high-resolution cloud detection validation
dataset has been made available online. | ['Yuhao Liu', 'Qing Cheng', 'Zhiwei Li', 'Shucheng You', 'Zongyi He', 'Huanfeng Shen'] | 2018-10-13 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 8.99702031e-03 -1.05257845e+00 1.37690395e-01 -3.59218478e-01
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66d9bb1c-1855-4018-97a4-4808b5aa1002 | dualcf-efficient-model-extraction-attack-from | 2205.06504 | null | https://arxiv.org/abs/2205.06504v1 | https://arxiv.org/pdf/2205.06504v1.pdf | DualCF: Efficient Model Extraction Attack from Counterfactual Explanations | Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS) platforms to allow users to access large-scale cloudbased models via APIs. In addition to prediction outputs, these APIs can also provide other information in a more human-understandable way, such as counterfactual explanations (CF). However, such extra information inevitably causes the cloud models to be more vulnerable to extraction attacks which aim to steal the internal functionality of models in the cloud. Due to the black-box nature of cloud models, however, a vast number of queries are inevitably required by existing attack strategies before the substitute model achieves high fidelity. In this paper, we propose a novel simple yet efficient querying strategy to greatly enhance the querying efficiency to steal a classification model. This is motivated by our observation that current querying strategies suffer from decision boundary shift issue induced by taking far-distant queries and close-to-boundary CFs into substitute model training. We then propose DualCF strategy to circumvent the above issues, which is achieved by taking not only CF but also counterfactual explanation of CF (CCF) as pairs of training samples for the substitute model. Extensive and comprehensive experimental evaluations are conducted on both synthetic and real-world datasets. The experimental results favorably illustrate that DualCF can produce a high-fidelity model with fewer queries efficiently and effectively. | ['Chunyan Miao', 'Hangwei Qian', 'Yongjie Wang'] | 2022-05-13 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.83137614e-02 -4.60759178e-02 -4.06859070e-01 -2.56021380e-01
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19185e38-f72f-4181-aeff-bd57a61db369 | latent-relational-metric-learning-via-memory | 1707.05176 | null | http://arxiv.org/abs/1707.05176v3 | http://arxiv.org/pdf/1707.05176v3.pdf | Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking | This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets. | ['Anh Tuan Luu', 'Yi Tay', 'Siu Cheung Hui'] | 2017-07-17 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-1.09371930e-01 6.29771799e-02 -6.24388576e-01 -6.02861583e-01
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73b95177-23d9-4c42-bb0c-8cffd7a4bda6 | joint-learning-of-blind-super-resolution-and | 2302.12491 | null | https://arxiv.org/abs/2302.12491v2 | https://arxiv.org/pdf/2302.12491v2.pdf | Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images | This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks. In the proposed method, a SR network is jointly trained with a binary segmentation network in an end-to-end manner. This joint learning allows the SR network to be optimized for improving segmentation results. For realistic scenarios, the SR network is extended from non-blind to blind for processing a low-resolution image degraded by unknown blurs. The joint network is improved by our proposed two extra paths that further encourage the mutual optimization between SR and segmentation. Comparative experiments with SoTA segmentation methods demonstrate the superiority of our joint learning, and various ablation studies prove the effects of our contributions. | ['Yuki Kondo', 'Norimichi Ukita'] | 2023-02-24 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 6.15606129e-01 1.82032093e-01 -1.04738146e-01 -2.40104303e-01
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7e5307b3-b12d-4cd2-9ca4-136b50e55aef | resolving-semantic-confusions-for-improved-1 | 2212.06097 | null | https://arxiv.org/abs/2212.06097v1 | https://arxiv.org/pdf/2212.06097v1.pdf | Resolving Semantic Confusions for Improved Zero-Shot Detection | Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes. Recently, methods employing generative models like GANs have shown some of the best results, where unseen-class samples are generated based on their semantics by a GAN trained on seen-class data, enabling vanilla object detectors to recognize unseen objects. However, the problem of semantic confusion still remains, where the model is sometimes unable to distinguish between semantically-similar classes. In this work, we propose to train a generative model incorporating a triplet loss that acknowledges the degree of dissimilarity between classes and reflects them in the generated samples. Moreover, a cyclic-consistency loss is also enforced to ensure that generated visual samples of a class highly correspond to their own semantics. Extensive experiments on two benchmark ZSD datasets - MSCOCO and PASCAL-VOC - demonstrate significant gains over the current ZSD methods, reducing semantic confusion and improving detection for the unseen classes. | ['Arijit Sur', 'Sushil Kumar', 'Sandipan Sarma'] | 2022-12-12 | resolving-semantic-confusions-for-improved | https://bmvc2022.mpi-inf.mpg.de/347/ | https://bmvc2022.mpi-inf.mpg.de/0347.pdf | british-machine-vision-conference-2022-11 | ['zero-shot-object-detection'] | ['computer-vision'] | [ 5.08757532e-01 1.44919366e-01 5.11897802e-02 -4.20252413e-01
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72710783-823a-4014-91a9-99be9fae55f2 | post-retrieval-clustering-using-third-order | null | null | https://aclanthology.org/P13-2028 | https://aclanthology.org/P13-2028.pdf | Post-Retrieval Clustering Using Third-Order Similarity Measures | null | ['Ga{\\"e}l Dias', "Jos{\\'e} G. Moreno", 'Guillaume Cleuziou'] | 2013-08-01 | null | null | null | acl-2013-8 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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677ebc0f-d616-43b6-9ce2-ce2fafdece3a | feature-generation-for-long-tail | 2111.05956 | null | https://arxiv.org/abs/2111.05956v1 | https://arxiv.org/pdf/2111.05956v1.pdf | Feature Generation for Long-tail Classification | The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a \emph{long-tailed distribution}. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning, we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://github.com/rahulvigneswaran/TailCalibX. | ['Makarand Tapaswi', 'Vineeth N. Balasubramanian', 'Marc T. Law', 'Rahul Vigneswaran'] | 2021-11-10 | null | null | null | null | ['classification'] | ['methodology'] | [-2.83994563e-02 -2.01208398e-01 -1.25237003e-01 -6.09938443e-01
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a57cd7f2-c490-47ce-a56a-03d41cc90cad | nict-naist-system-for-wmt17-multimodal | null | null | https://aclanthology.org/W17-4753 | https://aclanthology.org/W17-4753.pdf | NICT-NAIST System for WMT17 Multimodal Translation Task | null | ['Masao Utiyama', 'Eiichro Sumita', 'Jingyi Zhang', 'Satoshi Nakamura', 'Graham Neubig'] | 2017-09-01 | null | null | null | ws-2017-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7aee98b6-c39f-419b-a85c-5a6932a590a9 | strongly-incremental-constituency-parsing | 2010.14568 | null | https://arxiv.org/abs/2010.14568v1 | https://arxiv.org/pdf/2010.14568v1.pdf | Strongly Incremental Constituency Parsing with Graph Neural Networks | Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift-reduce transition system, which does not align with how humans are known to parse sentences. Psycholinguistic research suggests that human parsing is strongly incremental: humans grow a single parse tree by adding exactly one token at each step. In this paper, we propose a novel transition system called attach-juxtapose. It is strongly incremental; it represents a partial sentence using a single tree; each action adds exactly one token into the partial tree. Based on our transition system, we develop a strongly incremental parser. At each step, it encodes the partial tree using a graph neural network and predicts an action. We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. Code is available at https://github.com/princeton-vl/attach-juxtapose-parser. | ['Jia Deng', 'Kaiyu Yang'] | 2020-10-27 | null | http://proceedings.neurips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/f7177163c833dff4b38fc8d2872f1ec6-Paper.pdf | neurips-2020-12 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.19900578e-01 7.90421188e-01 -3.46650660e-01 -7.48048782e-01
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0584a919-819e-4e83-98b8-eee6ac5d3404 | toward-trusted-and-swift-uav-communication | 2306.17350 | null | https://arxiv.org/abs/2306.17350v1 | https://arxiv.org/pdf/2306.17350v1.pdf | Toward Trusted and Swift UAV Communication: ISAC-Enabled Dual Identity Mapping | The UAV network has recently emerged as a capable carrier for ubiquitous wireless intelligent communication in the B5G/6G era. Nevertheless, the separation of dual identity raises challenges from the perspective of communication efficiency and security, including tedious communication feedback and malicious Sybil attacks. Meanwhile, thanks to the emerging integrated sensing and communication (ISAC) technology, the sensing ability incorporated in communication advances crucial opportunities for accurately and efficiently mapping identity from dual domains. This tutorial discusses the exciting intersection of ISAC and the future intelligent and efficient UAV network. We first describe the motivation scenario and present the framework of the proposed novel ISAC-enabled dual identity solution. The detailed modules of identity production, mapping, management, and authentication are discussed. By endowing UAVs with an advanced capability: opening their eyes when communicating with each other, we detail three typical applications and the advantages of our proposal. Finally, a series of key enabling techniques, open challenges, and potential solutions for ISAC-enabled dual-domain identity are discussed. This tutorial for the intelligent and efficient UAV network brings new insight on providing dual-domain identity via ISAC technology, with an eye on trusted and swift communication research tailored for the 6G UAV network. | ['Ping Zhang', 'Chenlong Xu', 'Zhiqing Wei', 'Qixun Zhang', 'Zhiyong Feng', 'Yanpeng Cui'] | 2023-06-30 | null | null | null | null | ['management', 'intelligent-communication'] | ['miscellaneous', 'time-series'] | [ 2.60360569e-01 -5.63259274e-02 -4.04108167e-01 2.17240751e-01
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dccb1bbf-6799-4ecc-a5c6-1f9dbef10aa8 | bootstrapping-single-channel-source | 1811.02130 | null | http://arxiv.org/abs/1811.02130v1 | http://arxiv.org/pdf/1811.02130v1.pdf | Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures | Separating an audio scene into isolated sources is a fundamental problem in
computer audition, analogous to image segmentation in visual scene analysis.
Source separation systems based on deep learning are currently the most
successful approaches for solving the underdetermined separation problem, where
there are more sources than channels. Traditionally, such systems are trained
on sound mixtures where the ground truth decomposition is already known. Since
most real-world recordings do not have such a decomposition available, this
limits the range of mixtures one can train on, and the range of mixtures the
learned models may successfully separate. In this work, we use a simple blind
spatial source separation algorithm to generate estimated decompositions of
stereo mixtures. These estimates, together with a weighting scheme in the
time-frequency domain, based on confidence in the separation quality, are used
to train a deep learning model that can be used for single-channel separation,
where no source direction information is available. This demonstrates how a
simple cue such as the direction of origin of source can be used to bootstrap a
model for source separation that can be used in situations where that cue is
not available. | ['Prem Seetharaman', 'Bryan Pardo', 'Jonathan Le Roux', 'Gordon Wichern'] | 2018-11-06 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [ 3.13048780e-01 -3.61012787e-01 -2.95921769e-02 -1.26048267e-01
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9f7cb0e3-c5bf-4937-adc9-6b3cb65b8d4c | cat-gen-improving-robustness-in-nlp-models | 2010.02338 | null | https://arxiv.org/abs/2010.02338v1 | https://arxiv.org/pdf/2010.02338v1.pdf | CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation | NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures. | ['Ed Chi', 'Alex Beutel', 'Jilin Chen', 'Kang Li', 'Ben Packer', 'Yao Qin', 'Xuezhi Wang', 'Tianlu Wang'] | 2020-10-05 | null | https://aclanthology.org/2020.emnlp-main.417 | https://aclanthology.org/2020.emnlp-main.417.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 6.16846263e-01 7.44338930e-01 1.12965807e-01 -3.92309457e-01
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717bc3cb-3e30-48cb-a7f9-2f3cb0a61022 | scalable-attentive-sentence-pair-modeling-via | 1908.05161 | null | https://arxiv.org/abs/1908.05161v3 | https://arxiv.org/pdf/1908.05161v3.pdf | Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding | Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence embedding techniques learn a sentence-to-vector mapping and compute the similarity between the sentence vectors via simple elementary operations. In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. The outline of DSE is as follows: Given a cross-attentive teacher model (e.g. a fine-tuned BERT), we train a sentence embedding based student model to reconstruct the sentence-pair scores obtained by the teacher model. We empirically demonstrate the effectiveness of DSE on five GLUE sentence-pair tasks. DSE significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT. Furthermore, we show that DSE produces sentence embeddings that reach state-of-the-art performance on universal sentence representation benchmarks. Our code is made publicly available at https://github.com/microsoft/Distilled-Sentence-Embedding. | ['Itzik Malkiel', 'Avi Caciularu', 'Ori Katz', 'Noam Koenigstein', 'Oren Barkan', 'Noam Razin'] | 2019-08-14 | null | null | null | null | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 3.01216006e-01 1.33790672e-01 -1.84207428e-02 -7.03030109e-01
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b466abbb-6e05-4803-b7a3-9d72abe45368 | a-novel-framework-for-multimodal-named-entity | 2305.08372 | null | https://arxiv.org/abs/2305.08372v1 | https://arxiv.org/pdf/2305.08372v1.pdf | A Novel Framework for Multimodal Named Entity Recognition with Multi-level Alignments | Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many downstream applications such as recommendation and intention under standing. With tweet posts tending to be multimodal, multimodal named entity recognition (MNER) has attracted more attention. In this paper, we propose a novel approach, which can dynamically align the image and text sequence and achieve the multi-level cross-modal learning to augment textual word representation for MNER improvement. To be specific, our framework can be split into three main stages: the first stage focuses on intra-modality representation learning to derive the implicit global and local knowledge of each modality, the second evaluates the relevance between the text and its accompanying image and integrates different grained visual information based on the relevance, the third enforces semantic refinement via iterative cross-modal interactions and co-attention. We conduct experiments on two open datasets, and the results and detailed analysis demonstrate the advantage of our model. | ['Limin Sun', 'Hongsong Zhu', 'Shuaizong Si', 'Jie Liu', 'Yimo Ren', 'Hong Li', 'Peipei Liu'] | 2023-05-15 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 3.15800846e-01 -2.87676863e-02 -3.50985974e-01 -4.33693081e-01
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7dd726cb-a590-4a12-aef6-d96e7f748df6 | algorithmic-trading-using-continuous-action | 2210.03469 | null | https://arxiv.org/abs/2210.03469v1 | https://arxiv.org/pdf/2210.03469v1.pdf | Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning | Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence. This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Unlike previous studies using a discrete action space reinforcement learning algorithm, the TD3 is continuous, offering both position and the number of trading shares. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm. The achieved strategy using the TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic, and deterministic strategies through two standard metrics, Return and Sharpe ratio. The results indicate that employing both position and the number of trading shares can improve the performance of a trading system based on the mentioned metrics. | ['Farokh Marvasti', 'Mahdi Shamsi', 'Naseh Majidi'] | 2022-10-07 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.39176667e-01 -1.31109744e-01 2.81123491e-03 3.22172098e-04
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14fc4b1d-e79b-4ec4-8af5-7b3df742fd73 | bi-vldoc-bidirectional-vision-language | 2206.13155 | null | https://arxiv.org/abs/2206.13155v1 | https://arxiv.org/pdf/2206.13155v1.pdf | Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding | Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks. Though existing document pre-trained models have achieved excellent performance on standard benchmarks for VrDU, the way they model and exploit the interactions between vision and language on documents has hindered them from better generalization ability and higher accuracy. In this work, we investigate the problem of vision-language joint representation learning for VrDU mainly from the perspective of supervisory signals. Specifically, a pre-training paradigm called Bi-VLDoc is proposed, in which a bidirectional vision-language supervision strategy and a vision-language hybrid-attention mechanism are devised to fully explore and utilize the interactions between these two modalities, to learn stronger cross-modal document representations with richer semantics. Benefiting from the learned informative cross-modal document representations, Bi-VLDoc significantly advances the state-of-the-art performance on three widely-used document understanding benchmarks, including Form Understanding (from 85.14% to 93.44%), Receipt Information Extraction (from 96.01% to 97.84%), and Document Classification (from 96.08% to 97.12%). On Document Visual QA, Bi-VLDoc achieves the state-of-the-art performance compared to previous single model methods. | ['Luo Si', 'Yang Xue', 'Chenliang Li', 'Lianwen Jin', 'Cong Yao', 'Qi Zheng', 'Guozhi Tang', 'Chuwei Luo'] | 2022-06-27 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 1.82246432e-01 -8.28037187e-02 -3.99565786e-01 -2.54469693e-01
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c2c2300b-06c2-462e-8fbe-7d94f0103b2a | shapetalk-a-language-dataset-and-framework | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.pdf | ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations | Editing 3D geometry is a challenging task requiring specialized skills. In this work, we aim to facilitate the task of editing the geometry of 3D models through the use of natural language. For example, we may want to modify a 3D chair model to "make its legs thinner" or to "open a hole in its back". To tackle this problem in a manner that promotes open-ended language use and enables fine-grained shape edits, we introduce the most extensive existing corpus of natural language utterances describing shape differences: ShapeTalk. ShapeTalk contains over half a million discriminative utterances produced by contrasting the shapes of common 3D objects for a variety of object classes and degrees of similarity. We also introduce a generic framework, ChangeIt3D, which builds on ShapeTalk and can use an arbitrary 3D generative model of shapes to produce edits that align the output better with the edit or deformation description. Finally, we introduce metrics for the quantitative evaluation of language-assisted shape editing methods that reflect key desiderata within this editing setup. We note that ShapeTalk allows methods to be trained with explicit 3D-to-language data, bypassing the necessity of "lifting" 2D to 3D using methods like neural rendering, as required by extant 2D image-language foundation models. Our code and data are publicly available at https://changeit3d.github.io/. | ['Leonidas Guibas', 'Sergey Tulyakov', 'Minhyuk Sung', 'IAn Huang', 'Panos Achlioptas'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 1.69573903e-01 4.00148541e-01 4.70504403e-01 -6.40523911e-01
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942cf291-67a8-43f8-9eab-88394bf29c73 | playing-against-the-board-rolling-horizon | 2103.15090 | null | https://arxiv.org/abs/2103.15090v1 | https://arxiv.org/pdf/2103.15090v1.pdf | Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic | Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions. | ['Antonios Liapis', 'Konstantinos Sfikas'] | 2021-03-28 | null | null | null | null | ['board-games'] | ['playing-games'] | [-6.14979723e-03 4.11890537e-01 1.69671446e-01 2.50647753e-01
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0403bbd2-16a9-4fc2-b27e-c2e6bc9e6311 | robust-ante-hoc-graph-explainer-using-bilevel | 2305.15745 | null | https://arxiv.org/abs/2305.15745v1 | https://arxiv.org/pdf/2305.15745v1.pdf | Robust Ante-hoc Graph Explainer using Bilevel Optimization | Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is particularly true in the case of models for graphs, where decisions often depend on complex patterns combining rich structural and attribute data. While recent work has focused on designing so-called post-hoc explainers, the question of what constitutes a good explanation remains open. One intuitive property is that explanations should be sufficiently informative to enable humans to approximately reproduce the predictions given the data. However, we show that post-hoc explanations do not achieve this goal as their explanations are highly dependent on fixed model parameters (e.g., learned GNN weights). To address this challenge, this paper proposes RAGE (Robust Ante-hoc Graph Explainer), a novel and flexible ante-hoc explainer designed to discover explanations for a broad class of graph neural networks using bilevel optimization. RAGE is able to efficiently identify explanations that contain the full information needed for prediction while still enabling humans to rank these explanations based on their influence. Our experiments, based on graph classification and regression, show that RAGE explanations are more robust than existing post-hoc and ante-hoc approaches and often achieve similar or better accuracy than state-of-the-art models. | ['Ambuj Singh', 'Arlei Silva', 'Mert Kosan'] | 2023-05-25 | null | null | null | null | ['graph-classification', 'bilevel-optimization'] | ['graphs', 'methodology'] | [ 2.98593342e-01 1.06239545e+00 -5.95664144e-01 -6.03496075e-01
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19899ceb-4776-42c9-84ed-f0bd86b3799d | hashtagwars-learning-a-sense-of-humor | 1612.03216 | null | http://arxiv.org/abs/1612.03216v2 | http://arxiv.org/pdf/1612.03216v2.pdf | #HashtagWars: Learning a Sense of Humor | In this work, we present a new dataset for computational humor, specifically
comparative humor ranking, which attempts to eschew the ubiquitous binary
approach to humor detection. The dataset consists of tweets that are humorous
responses to a given hashtag. We describe the motivation for this new dataset,
as well as the collection process, which includes a description of our
semi-automated system for data collection. We also present initial experiments
for this dataset using both unsupervised and supervised approaches. Our best
supervised system achieved 63.7% accuracy, suggesting that this task is much
more difficult than comparable humor detection tasks. Initial experiments
indicate that a character-level model is more suitable for this task than a
token-level model, likely due to a large amount of puns that can be captured by
a character-level model. | ['Anna Rumshisky', 'Peter Potash', 'Alexey Romanov'] | 2016-12-09 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-2.65361875e-01 -1.10306337e-01 -1.58734500e-01 -3.85105647e-02
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b3e813b0-b1da-4511-b16d-a8cbafb929d3 | deephuman-3d-human-reconstruction-from-a | 1903.06473 | null | http://arxiv.org/abs/1903.06473v2 | http://arxiv.org/pdf/1903.06473v2.pdf | DeepHuman: 3D Human Reconstruction from a Single Image | We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated from SMPL model as an additional input. One key
feature of our network is that it fuses different scales of image features into
the 3D space through volumetric feature transformation, which helps to recover
accurate surface geometry. The visible surface details are further refined
through a normal refinement network, which can be concatenated with the volume
generation network using our proposed volumetric normal projection layer. We
also contribute THuman, a 3D real-world human model dataset containing about
7000 models. The network is trained using training data generated from the
dataset. Overall, due to the specific design of our network and the diversity
in our dataset, our method enables 3D human model estimation given only a
single image and outperforms state-of-the-art approaches. | ['Yebin Liu', 'Zerong Zheng', 'Yixuan Wei', 'Tao Yu', 'Qionghai Dai'] | 2019-03-15 | deephuman-3d-human-reconstruction-from-a-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zheng_DeepHuman_3D_Human_Reconstruction_From_a_Single_Image_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zheng_DeepHuman_3D_Human_Reconstruction_From_a_Single_Image_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 2.34341174e-01 4.99650389e-01 2.79761165e-01 -3.74578327e-01
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00e16d20-7591-4ef7-b4e9-e3c4e44a48de | monet-tackle-state-momentum-via-noise | 2211.05503 | null | https://arxiv.org/abs/2211.05503v3 | https://arxiv.org/pdf/2211.05503v3.pdf | MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking | Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the last turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. Furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum and improving anti-noise ability. | ['Xiaodong He', 'Shuguang Cui', 'Wenye Li', 'Youzheng Wu', 'Haipeng Sun', 'Junwei Bao', 'Haoning Zhang'] | 2022-11-10 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.76430875e-01 2.75563955e-01 -4.32543427e-01 -3.62007618e-01
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188b138c-0629-454d-9195-6cf1a25264aa | a-deep-neural-networks-approach-for-pixel | 2001.03257 | null | https://arxiv.org/abs/2001.03257v1 | https://arxiv.org/pdf/2001.03257v1.pdf | A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images | Pavement conditions are a critical aspect of asset management and directly affect safety. This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection. The proposed approach can also be used for highway pavement conditions assessment during off-peak periods when there are few vehicles on the road. In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality and applicability for crack segmentation, from which an optimal height is determined. Drone images captured at the optimal height are then used to evaluate the crack segmentation performance of the U-Net model. Deep learning methods typically require a huge set of annotated training datasets for model development, which can be a major obstacle for their applications. An online annotated pavement image dataset is used together with the FMA data to train the U-Net model. The results show that U-Net performs well on the FMA testing data even with limited FMA training images, suggesting that it has good generalization ability and great potential to be used for both airport runways and highway pavements. | ['Tianzhu Ren', 'Yuanchang Xie', 'Liming Jiang'] | 2020-01-09 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-1.81769356e-01 -3.48007739e-01 1.09523669e-01 -3.06008875e-01
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bab61a8f-1ecf-460a-9d18-271ad2a7f285 | rumour-detection-and-analysis-on-twitter | 2304.01712 | null | https://arxiv.org/abs/2304.01712v1 | https://arxiv.org/pdf/2304.01712v1.pdf | Rumour Detection and Analysis on Twitter | In recent years people have become increasingly reliant on social media to read news and get information, and some social media users post unsubstantiated information to gain attention. Such information is known as rumours. Nowadays, rumour detection is receiving a growing amount of attention because of the pandemic of the New Coronavirus, which has led to a large number of rumours being spread. In this paper, a Natural Language Processing (NLP) system is built to predict rumours. The best model is applied to the COVID-19 tweets to conduct exploratory data analysis. The contribution of this study is twofold: (1) to compare rumours and facts using state-of-the-art natural language processing models in two dimensions: language structure and propagation route. (2) An analysis of how rumours differ from facts in terms of their lexical use and the emotions they imply. This study shows that linguistic structure is a better feature to distinguish rumours from facts compared to the propagation path. In addition, rumour tweets contain more vocabulary related to politics and negative emotions. | ['Yaohou Fan'] | 2023-04-04 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.52929884e-01 1.71890289e-01 -5.26420176e-01 -2.10169688e-01
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fc38a839-47ce-44bf-a7a9-ac2817e7d410 | common-knowledge-concept-recognition-for-seva | 2003.11687 | null | https://arxiv.org/abs/2003.11687v1 | https://arxiv.org/pdf/2003.11687v1.pdf | Common-Knowledge Concept Recognition for SEVA | We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering. The problem is formulated as a token classification task similar to named entity extraction. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain. Finally, we construct a simple knowledge graph using these extracted concepts along with some hyponym relations. | ['Hemant Purohit', 'Patrick Coronado', 'Jitin Krishnan', 'Huzefa Rangwala'] | 2020-03-26 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 2.80877173e-01 5.49814820e-01 -9.48116109e-02 -4.53360170e-01
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60b18796-26f8-4a74-be93-ff23ac0ced3d | generative-long-form-question-answering | 2211.08386 | null | https://arxiv.org/abs/2211.08386v1 | https://arxiv.org/pdf/2211.08386v1.pdf | Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness | In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). LFQA aims to generate an in-depth, paragraph-length answer for a given question, to help bridge the gap between real scenarios and the existing open-domain QA models which can only extract short-span answers. LFQA is quite challenging and under-explored. Few works have been done to build an effective LFQA system. It is even more challenging to generate a good-quality long-form answer relevant to the query and faithful to facts, since a considerable amount of redundant, complementary, or contradictory information will be contained in the retrieved documents. Moreover, no prior work has been investigated to generate succinct answers. We are among the first to research the LFQA task. We pioneered the research direction to improve the answer quality in terms of 1) query-relevance, 2) answer faithfulness, and 3) answer succinctness. | ['Dan Su'] | 2022-11-15 | null | null | null | null | ['long-form-question-answering'] | ['natural-language-processing'] | [-5.85839339e-02 5.22741914e-01 4.99330498e-02 -4.35718328e-01
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821bb2dc-296b-4a93-9e93-ddc1b0be2215 | u-gat-it-unsupervised-generative-attentional | 1907.10830 | null | https://arxiv.org/abs/1907.10830v4 | https://arxiv.org/pdf/1907.10830v4.pdf | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation | We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets. Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters. Our code and datasets are available at https://github.com/taki0112/UGATIT or https://github.com/znxlwm/UGATIT-pytorch. | ['Junho Kim', 'Kwanghee Lee', 'Hyeonwoo Kang', 'Minjae Kim'] | 2019-07-25 | null | https://openreview.net/forum?id=BJlZ5ySKPH | https://openreview.net/pdf?id=BJlZ5ySKPH | iclr-2020-1 | ['fundus-to-angiography-generation'] | ['computer-vision'] | [ 1.86243325e-01 3.88757209e-03 -2.01817945e-01 -3.31424743e-01
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b2c3eab1-1528-4044-bc2f-e1eecefa7b1e | simulating-2-1d-lattice-quantum | 2212.06835 | null | https://arxiv.org/abs/2212.06835v1 | https://arxiv.org/pdf/2212.06835v1.pdf | Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions | We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions. The gauge field is represented by a neural network which parameterizes a discretized flow-based transformation of the amplitude while the fermionic sign structure is represented by a neural net backflow. This approach directly represents the $U(1)$ degree of freedom without any truncation, obeys Guass's law by construction, samples autoregressively avoiding any equilibration time, and variationally simulates Gauge-Fermion systems with sign problems accurately. In this model, we investigate confinement and string breaking phenomena in different fermion density and hopping regimes. We study the phase transition from the charge crystal phase to the vacuum phase at zero density, and observe the phase seperation and the net charge penetration blocking effect under magnetic interaction at finite density. In addition, we investigate a magnetic phase transition due to the competition effect between the kinetic energy of fermions and the magnetic energy of the gauge field. With our method, we further note potential differences on the order of the phase transitions between a continuous $U(1)$ system and one with finite truncation. Our state-of-the-art neural network approach opens up new possibilities to study different gauge theories coupled to dynamical matter in higher dimensions. | ['Bryan K. Clark', 'Kaiwen Hu', 'Di Luo', 'Zhuo Chen'] | 2022-12-14 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.84301436e-03 -2.33380020e-01 -1.16438791e-01 1.37451261e-01
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91d38a0f-69d7-4fa5-bf5a-e5524c5d0cd1 | a-summary-of-adaptation-of-techniques-from | 1812.10851 | null | http://arxiv.org/abs/1812.10851v1 | http://arxiv.org/pdf/1812.10851v1.pdf | A Summary of Adaptation of Techniques from Search-based Optimal Multi-Agent Path Finding Solvers to Compilation-based Approach | In the multi-agent path finding problem (MAPF) we are given a set of agents
each with respective start and goal positions. The task is to find paths for
all agents while avoiding collisions aiming to minimize an objective function.
Two such common objective functions is the sum-of-costs and the makespan. Many
optimal solvers were introduced in the past decade - two prominent categories
of solvers can be distinguished: search-based solvers and compilation-based
solvers.
Search-based solvers were developed and tested for the sum-of-costs objective
while the most prominent compilation-based solvers that are built around
Boolean satisfiability (SAT) were designed for the makespan objective. Very
little was known on the performance and relevance of the compilation-based
approach on the sum-of-costs objective. In this paper we show how to close the
gap between these cost functions in the compilation-based approach. Moreover we
study applicability of various techniques developed for search-based solvers in
the compilation-based approach.
A part of this paper introduces a SAT-solver that is directly aimed to solve
the sum-of-costs objective function. Using both a lower bound on the
sum-of-costs and an upper bound on the makespan, we are able to have a
reasonable number of variables in our SAT encoding. We then further improve the
encoding by borrowing ideas from ICTS, a search-based solver. Experimental
evaluation on several domains show that there are many scenarios where our new
SAT-based methods outperforms the best variants of previous sum-of-costs search
solvers - the ICTS, CBS algorithms, and ICBS algorithms. | ['Pavel Surynek'] | 2018-12-28 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.98684433e-02 4.50773537e-01 -1.12377346e-01 -4.97772507e-02
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58d938c8-34a7-40d7-aac6-20934eac8bf6 | daedalus-at-semeval-2014-task-9-comparing | null | null | https://aclanthology.org/S14-2035 | https://aclanthology.org/S14-2035.pdf | DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter | null | ["Jos{\\'e} Carlos Gonz{\\'a}lez-Crist{\\'o}bal", "Janine Garc{\\'\\i}a-Morera", "Julio Villena-Rom{\\'a}n"] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3d7520cc-7050-44f1-809d-5a31a11b4b8a | even-your-teacher-needs-guidance-ground-truth | 2102.13088 | null | https://arxiv.org/abs/2102.13088v2 | https://arxiv.org/pdf/2102.13088v2.pdf | Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation | Knowledge distillation is classically a procedure where a neural network is trained on the output of another network along with the original targets in order to transfer knowledge between the architectures. The special case of self-distillation, where the network architectures are identical, has been observed to improve generalization accuracy. In this paper, we consider an iterative variant of self-distillation in a kernel regression setting, in which successive steps incorporate both model outputs and the ground-truth targets. This allows us to provide the first theoretical results on the importance of using the weighted ground-truth targets in self-distillation. Our focus is on fitting nonlinear functions to training data with a weighted mean square error objective function suitable for distillation, subject to $\ell_2$ regularization of the model parameters. We show that any such function obtained with self-distillation can be calculated directly as a function of the initial fit, and that infinite distillation steps yields the same optimization problem as the original with amplified regularization. Furthermore, we provide a closed form solution for the optimal choice of weighting parameter at each step, and show how to efficiently estimate this weighting parameter for deep learning and significantly reduce the computational requirements compared to a grid search. | ['Lars N. Andersen', 'Kenneth Borup'] | 2021-02-25 | null | http://proceedings.neurips.cc/paper/2021/hash/2adcefe38fbcd3dcd45908fbab1bf628-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2adcefe38fbcd3dcd45908fbab1bf628-Paper.pdf | neurips-2021-12 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 1.22466534e-01 5.38989067e-01 7.81304017e-02 -2.85697281e-01
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141553ec-5031-4b5c-a1c3-4e03ac04a363 | inductive-link-prediction-for-nodes-having | 2007.08053 | null | https://arxiv.org/abs/2007.08053v1 | https://arxiv.org/pdf/2007.08053v1.pdf | Inductive Link Prediction for Nodes Having Only Attribute Information | Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction. | ['Yixiang Fang', 'Xin Cao', 'Yu Hao', 'Xike Xie', 'Sibo Wang'] | 2020-07-16 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-8.54798034e-02 6.37720466e-01 -8.60651135e-01 -4.22436863e-01
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5909d3dc-13b7-4cfc-a6b2-6600b6344179 | multi-task-zero-shot-action-recognition-with | 1611.08663 | null | http://arxiv.org/abs/1611.08663v1 | http://arxiv.org/pdf/1611.08663v1.pdf | Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation | Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on auxiliary data. Reusing the learned mapping to
project target videos into an embedding space thus allows novel-classes to be
recognised by nearest neighbour inference. However, existing ZSL methods suffer
from auxiliary-target domain shift intrinsically induced by assuming the same
mapping for the disjoint auxiliary and target classes. This compromises the
generalisation accuracy of ZSL recognition on the target data. In this work, we
improve the ability of ZSL to generalise across this domain shift in both
model- and data-centric ways by formulating a visual-semantic mapping with
better generalisation properties and a dynamic data re-weighting method to
prioritise auxiliary data that are relevant to the target classes.
Specifically: (1) We introduce a multi-task visual-semantic mapping to improve
generalisation by constraining the semantic mapping parameters to lie on a
low-dimensional manifold, (2) We explore prioritised data augmentation by
expanding the pool of auxiliary data with additional instances weighted by
relevance to the target domain. The proposed new model is applied to the
challenging zero-shot action recognition problem to demonstrate its advantages
over existing ZSL models. | ['Xun Xu', 'Timothy M. Hospedales', 'Shaogang Gong'] | 2016-11-26 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 7.58691311e-01 3.09978127e-01 -3.65301669e-01 -4.26590949e-01
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00acb6e1-5748-48d0-8b6b-63a155d0b5d9 | rrnet-relational-reasoning-network-with | 2110.14223 | null | https://arxiv.org/abs/2110.14223v2 | https://arxiv.org/pdf/2110.14223v2.pdf | RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images | Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. | ['Sam Kwong', 'Yao Zhao', 'Jun Li', 'Leyuan Fang', 'Yumo Zhang', 'Runmin Cong'] | 2021-10-27 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.80909866e-01 -6.61989525e-02 -1.19984867e-02 -1.13641247e-01
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d216e965-4dff-4226-9f9b-5ab4e8d66b0f | full-explicit-consistency-constraints-in | 1805.02352 | null | https://arxiv.org/abs/1805.02352v7 | https://arxiv.org/pdf/1805.02352v7.pdf | Full explicit consistency constraints in uncalibrated multiple homography estimation | We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views. Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task. | ['Zygmunt L. Szpak', 'Wojciech Chojnacki'] | 2018-05-07 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 4.34968024e-01 7.99458008e-03 -1.74096555e-01 -3.38509053e-01
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834e42a4-48a8-403d-bcc4-bd89ec393a9c | improved-temporal-relation-classification | null | null | https://aclanthology.org/C12-1129 | https://aclanthology.org/C12-1129.pdf | Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations | null | ['Min-Yen Kan', 'Jun-Ping Ng'] | 2012-12-01 | improved-temporal-relation-classification-1 | https://aclanthology.org/C12-1129 | https://aclanthology.org/C12-1129.pdf | coling-2012-12 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0c7a8952-2d9c-4c85-9543-9cc1064f249e | multilingual-and-cross-lingual-complex-word | null | null | https://aclanthology.org/R17-1104 | https://aclanthology.org/R17-1104.pdf | Multilingual and Cross-Lingual Complex Word Identification | Complex Word Identification (CWI) is an important task in lexical simplification and text accessibility. Due to the lack of CWI datasets, previous works largely depend on Simple English Wikipedia and edit histories for obtaining {`}gold standard{'} annotations, which are of doubtable quality, and limited only to English. We collect complex words/phrases (CP) for English, German and Spanish, annotated by both native and non-native speakers, and propose language independent features that can be used to train multilingual and cross-lingual CWI models. We show that the performance of cross-lingual CWI systems (using a model trained on one language and applying it on the other languages) is comparable to the performance of monolingual CWI systems. | ['Sanja {\\v{S}}tajner', 'Martin Riedl', 'Chris Biemann', 'Seid Muhie Yimam'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['complex-word-identification'] | ['natural-language-processing'] | [ 7.87803531e-03 8.34698752e-02 -3.23144704e-01 -7.19874650e-02
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df14dbf5-2c81-4e87-9946-03a30cf6fba7 | spatio-temporal-attention-based-neural | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/5371 | https://ojs.aaai.org/index.php/AAAI/article/download/5371/5227 | Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection | Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns. | ['Liqing Zhang', 'Fangzhou Yang', 'Yiyi Zhang', 'Chencheng Shang', 'Sheng Xiang', 'Dawei Cheng'] | 2020-04-03 | null | null | null | aaai-2020-4 | ['fraud-detection'] | ['miscellaneous'] | [-2.65003026e-01 -6.96110487e-01 2.56939679e-02 -5.35432458e-01
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11ac510e-858a-425c-a083-eb91b8d194bf | tuning-legged-locomotion-controllers-via-safe | 2306.07092 | null | https://arxiv.org/abs/2306.07092v1 | https://arxiv.org/pdf/2306.07092v1.pdf | Tuning Legged Locomotion Controllers via Safe Bayesian Optimization | In this paper, we present a data-driven strategy to simplify the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system. This method substantially mitigates the risk of hazardous interactions with the robot by sample-efficiently optimizing parameters within a probably safe region. Additionally, we extend the applicability of our approach to incorporate the different gait parameters as contexts, leading to a safe, sample-efficient exploration algorithm capable of tuning a motion controller for diverse gait patterns. We validate our method through simulation and hardware experiments, where we demonstrate that the algorithm obtains superior performance on tuning a model-based motion controller for multiple gaits safely. | ['Stelian Coros', 'Andreas Krause', 'Jonas Hübotter', 'Bhavya Sukhija', 'Dongho Kang', 'Daniel Widmer'] | 2023-06-12 | null | null | null | null | ['efficient-exploration', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-8.85070637e-02 3.14939290e-01 -5.71003377e-01 2.44589642e-01
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16b7c669-c0bb-4e9f-b813-4f2e8a903546 | cell-abundance-aware-deep-learning-for-cell | 2102.11677 | null | https://arxiv.org/abs/2102.11677v1 | https://arxiv.org/pdf/2102.11677v1.pdf | Cell abundance aware deep learning for cell detection on highly imbalanced pathological data | Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to regularize Dice overlap loss function. The model was trained and evaluated on myeloma bone marrow trephine samples. Our model obtained a cell detection F1-score of 0.78, a 2% increase compared to baseline models, and it outperformed baseline models at detecting rare cell types. We found that scaling deep learning loss function by the abundance of cells improves cell detection performance. Our results demonstrate the importance of incorporating domain knowledge on deep learning methods for pathological data with class imbalance. | ['Yinyin Yuan', 'Kwee Yong', 'Manuel Rodriguez- Justo', 'Thien-An Tran', 'Lydia Lee', 'Dominic Patel', 'Catherine SY Lecat', 'Yeman Brhane Hagos'] | 2021-02-23 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-8.75973329e-03 -3.38042602e-02 -3.65563691e-01 -1.49649933e-01
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ff7973b5-6dd4-43a4-aafd-9fd7d70310f2 | cross-modal-representation-learning-for-zero | 2205.01657 | null | https://arxiv.org/abs/2205.01657v1 | https://arxiv.org/pdf/2205.01657v1.pdf | Cross-modal Representation Learning for Zero-shot Action Recognition | We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent. In zero-shot inference, we devise a simple semantic transfer scheme that embeds semantic relatedness information between seen and unseen classes to composite unseen visual prototypes. Accordingly, the discriminative features in the visual structure could be preserved and exploited to alleviate the typical zero-shot issues of information loss, semantic gap, and the hubness problem. Under a rigorous zero-shot setting of not pre-training on additional datasets, the experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will be made available. | ['Zicheng Liu', 'Lijuan Wang', 'Linjie Li', 'Kevin Lin', 'Chung-Ching Lin'] | 2022-05-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.15742201e-01 1.51650861e-01 -5.27238667e-01 -4.40076649e-01
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cb3e79c0-c1de-4f1a-9b68-647e2723e850 | learning-to-dehaze-with-polarization | null | null | http://proceedings.neurips.cc/paper/2021/hash/5fd0b37cd7dbbb00f97ba6ce92bf5add-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5fd0b37cd7dbbb00f97ba6ce92bf5add-Paper.pdf | Learning to dehaze with polarization | Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms. Single-image dehazing methods have shown their effectiveness in a large variety of scenes, however, they are based on handcrafted priors or learned features, which do not generalize well to real-world images. Polarization information can be used to relieve its ill-posedness, however, real-world images are still challenging since existing polarization-based methods usually assume that the transmitted light is not significantly polarized, and they require specific clues to estimate necessary physical parameters. In this paper, we propose a generalized physical formation model of hazy images and a robust polarization-based dehazing pipeline without the above assumption or requirement, along with a neural network tailored to the pipeline. Experimental results show that our approach achieves state-of-the-art performance on both synthetic data and real-world hazy images. | ['Boxin Shi', 'Chao Xu', 'Yufei Han', 'Minggui Teng', 'Chu Zhou'] | 2021-12-01 | null | https://openreview.net/forum?id=Ua9Vi0QqwD4 | https://openreview.net/pdf?id=Ua9Vi0QqwD4 | neurips-2021-12 | ['image-dehazing'] | ['computer-vision'] | [ 2.62164861e-01 -3.23283792e-01 3.78301471e-01 -3.00827473e-01
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4ed3f4e4-332b-40d0-921e-f4c24898acd1 | metaage-meta-learning-personalized-age | 2207.05288 | null | https://arxiv.org/abs/2207.05288v1 | https://arxiv.org/pdf/2207.05288v1.pdf | MetaAge: Meta-Learning Personalized Age Estimators | Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters. Specifically, we introduce a personalized estimator meta-learner, which takes identity features as the input and outputs the parameters of customized estimators. In this way, our method learns the meta knowledge without the above requirements and seamlessly transfers the learned meta knowledge to the test set, which enables us to leverage the existing large-scale age datasets without any additional annotations. Extensive experimental results on three benchmark datasets including MORPH II, ChaLearn LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge significantly boosts the performance of existing personalized methods and outperforms the state-of-the-art approaches. | ['Jie zhou', 'Jianjiang Feng', 'Abudukelimu Wuerkaixi', 'Jiwen Lu', 'Wanhua Li'] | 2022-07-12 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.48369321e-01 -1.73584759e-01 -5.02372384e-01 -5.51878989e-01
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a665d1aa-f400-4c62-b71d-fb6f0f455468 | learning-to-rank-retargeted-images | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Chen_Learning_to_Rank_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Learning_to_Rank_CVPR_2017_paper.pdf | Learning to Rank Retargeted Images | Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these scores to compare different results. Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images, in this paper we propose a learning-based OQA method that predicts the ranking of a set of retargeted images with the same source image. We show that this more manageable task helps achieve more consistent prediction to human preference and is sufficient for most application scenarios. To compute the ranking, we propose a simple yet efficient machine learning framework that uses a General Regression Neural Network (GRNN) to model a combination of seven elaborate OQA metrics. We then propose a simple scheme to transform the relative scores output from GRNN into a global ranking. We train our GRNN model using human preference data collected in the elaborate RetargetMe benchmark and evaluate our method based on the subjective study in RetargetMe. Moreover, we introduce a further subjective benchmark to evaluate the generalizability of different OQA methods. Experimental results demonstrate that our method outperforms eight representative OQA methods in ranking prediction and has better generalizability to different datasets.
| ['Yu-Kun Lai', 'Yong-Jin Liu', 'Yang Chen'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['image-retargeting'] | ['computer-vision'] | [ 3.42529267e-01 -3.47483367e-01 -2.43732408e-01 -5.88440776e-01
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cae082a4-dfe7-4149-921b-01a485bab9f5 | spatial-temporal-space-hand-in-hand-spatial | 2205.05264 | null | https://arxiv.org/abs/2205.05264v1 | https://arxiv.org/pdf/2205.05264v1.pdf | Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning | Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution(HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution(T-VSR) but ignore the reciprocal relations among them. Specifically, 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation with more clues; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMu-Net) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up-and-down projections, where the spatial and temporal features are fully fused and distilled, helping the high-quality video reconstruction. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMu-Net with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms state-of-the-art methods. | ['Zheng Wang', 'Junjun Jiang', 'Jing Xiao', 'Liang Liao', 'Kui Jiang', 'Mengshun Hu'] | 2022-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'video-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.11334482e-01 -1.80672020e-01 -3.45204920e-01 -1.41404271e-01
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15b81e5f-146a-47c0-b2b3-fda11f8fe62d | htnet-anchor-free-temporal-action | 2207.09662 | null | https://arxiv.org/abs/2207.09662v2 | https://arxiv.org/pdf/2207.09662v2.pdf | HTNet: Anchor-free Temporal Action Localization with Hierarchical Transformers | Temporal action localization (TAL) is a task of identifying a set of actions in a video, which involves localizing the start and end frames and classifying each action instance. Existing methods have addressed this task by using predefined anchor windows or heuristic bottom-up boundary-matching strategies, which are major bottlenecks in inference time. Additionally, the main challenge is the inability to capture long-range actions due to a lack of global contextual information. In this paper, we present a novel anchor-free framework, referred to as HTNet, which predicts a set of <start time, end time, class> triplets from a video based on a Transformer architecture. After the prediction of coarse boundaries, we refine it through a background feature sampling (BFS) module and hierarchical Transformers, which enables our model to aggregate global contextual information and effectively exploit the inherent semantic relationships in a video. We demonstrate how our method localizes accurate action instances and achieves state-of-the-art performance on two TAL benchmark datasets: THUMOS14 and ActivityNet 1.3. | ['Seong-Whan Lee', 'Gun-Hee Lee', 'Tae-Kyung Kang'] | 2022-07-20 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 4.96684849e-01 -3.03191721e-01 -5.42003572e-01 -2.75375217e-01
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a2f3c207-a108-43e7-a52a-993d42b031ad | dialogue-evaluation-with-offline | 2209.00876 | null | https://arxiv.org/abs/2209.00876v1 | https://arxiv.org/pdf/2209.00876v1.pdf | Dialogue Evaluation with Offline Reinforcement Learning | Task-oriented dialogue systems aim to fulfill user goals through natural language interactions. They are ideally evaluated with human users, which however is unattainable to do at every iteration of the development phase. Simulated users could be an alternative, however their development is nontrivial. Therefore, researchers resort to offline metrics on existing human-human corpora, which are more practical and easily reproducible. They are unfortunately limited in reflecting real performance of dialogue systems. BLEU for instance is poorly correlated with human judgment, and existing corpus-based metrics such as success rate overlook dialogue context mismatches. There is still a need for a reliable metric for task-oriented systems with good generalization and strong correlation with human judgements. In this paper, we propose the use of offline reinforcement learning for dialogue evaluation based on a static corpus. Such an evaluator is typically called a critic and utilized for policy optimization. We go one step further and show that offline RL critics can be trained on a static corpus for any dialogue system as external evaluators, allowing dialogue performance comparisons across various types of systems. This approach has the benefit of being corpus- and model-independent, while attaining strong correlation with human judgements, which we confirm via an interactive user trial. | ['Milica Gašić', 'Shutong Feng', 'Michael Heck', 'Carel van Niekerk', 'Hsien-Chin Lin', 'Christian Geishauser', 'Nurul Lubis'] | 2022-09-02 | null | https://aclanthology.org/2022.sigdial-1.46 | https://aclanthology.org/2022.sigdial-1.46.pdf | sigdial-acl-2022-9 | ['dialogue-evaluation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [-7.46009648e-02 3.23625177e-01 9.06199962e-02 -3.85527164e-01
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d8e25c88-ae0b-4d8b-8932-cb0548f570f5 | a-deep-learning-based-global-and-segmentation | 2302.06432 | null | https://arxiv.org/abs/2302.06432v2 | https://arxiv.org/pdf/2302.06432v2.pdf | A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification | Indoor scene classification has become an important task in perception modules and has been widely used in various applications. However, problems such as intra-category variability and inter-category similarity have been holding back the models' performance, which leads to the need for new types of features to obtain a more meaningful scene representation. A semantic segmentation mask provides pixel-level information about the objects available in the scene, which makes it a promising source of information to obtain a more meaningful local representation of the scene. Therefore, in this work, a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the object categories across the scene, designated by segmentation-based semantic features (SSFs), is proposed. These features represent, per object category, the pixel count, as well as the 2D average position and respective standard deviation values. Moreover, a two-branch network, GS2F2App, that exploits CNN-based global features extracted from RGB images and the segmentation-based features extracted from the proposed SSFs, is also proposed. GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-D and the NYU Depth V2, achieving state-of-the-art results on both datasets. | ['Urbano J. Nunes', 'Ana Lopes', 'Luís Garrote', 'Tiago Barros', 'Ricardo Pereira'] | 2023-02-13 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.21931893e-01 -4.37325627e-01 1.70858443e-01 -8.53803575e-01
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76ea162a-b211-4e4c-9813-9baa03af8837 | dynamic-and-context-dependent-stock-price | 2205.01639 | null | https://arxiv.org/abs/2205.01639v1 | https://arxiv.org/pdf/2205.01639v1.pdf | Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment | The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $\alpha_{t}$-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $\alpha_{t}$-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S\&P 500 universe as well as their news sentiment score. The results suggest that the $\alpha_{t}$-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks such as long short-term memory models. | ['Nicole Koenigstein'] | 2022-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.76989412e-01 -3.92832875e-01 -7.96531364e-02 -5.02030253e-01
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be24987b-11b5-40c3-bbff-c0f639fc446e | code-switched-inspired-losses-for-spoken | null | null | https://aclanthology.org/2021.emnlp-main.656 | https://aclanthology.org/2021.emnlp-main.656.pdf | Code-switched inspired losses for spoken dialog representations | Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (e.g in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (i.e multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings. | ['Chloé Clavel', 'Matthieu Labeau', 'Emile Chapuis', 'Pierre Colombo'] | null | null | null | null | emnlp-2021-11 | ['spoken-dialogue-systems'] | ['speech'] | [-2.14367777e-01 2.26452872e-01 2.73526385e-02 -5.21286368e-01
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2cd91399-0fd1-4e4c-bb9a-11a460211c06 | an-efficient-optical-flow-based-motion | 1811.08290 | null | http://arxiv.org/abs/1811.08290v2 | http://arxiv.org/pdf/1811.08290v2.pdf | An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes | Real-time motion detection in non-stationary scenes is a difficult task due
to dynamic background, changing foreground appearance and limited computational
resource. These challenges degrade the performance of the existing methods in
practical applications. In this paper, an optical flow based framework is
proposed to address this problem. By applying a novel strategy to utilize
optical flow, we enable our method being free of model constructing, training
or updating and can be performed efficiently. Besides, a dual judgment
mechanism with adaptive intervals and adaptive thresholds is designed to
heighten the system's adaptation to different situations. In experiment part,
we quantitatively and qualitatively validate the effectiveness and feasibility
of our method with videos in various scene conditions. The experimental results
show that our method adapts itself to different situations and outperforms the
state-of-the-art real-time methods, indicating the advantages of our optical
flow based method. | ['Jiagang Zhu', 'Wei Zou', 'Junjie Huang', 'Zheng Zhu'] | 2018-11-18 | null | null | null | null | ['motion-detection-in-non-stationary-scenes', 'motion-detection'] | ['computer-vision', 'computer-vision'] | [ 1.00859992e-01 -9.31075335e-01 -6.89371228e-02 -5.12670539e-02
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96a19b76-e3c1-4729-8b57-e1272146a8a9 | random-projections-of-mel-spectrograms-as-low | 1911.04660 | null | https://arxiv.org/abs/1911.04660v1 | https://arxiv.org/pdf/1911.04660v1.pdf | Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification | In this work, we analyse the random projections of Mel-spectrograms as low-level features for music genre classification. This approach was compared to handcrafted features, features learned using an auto-encoder and features obtained from a transfer learning setting. Tests in five different well-known, publicly available datasets show that random projections leads to results comparable to learned features and outperforms features obtained via transfer learning in a shallow learning scenario. Random projections do not require using extensive specialist knowledge and, simultaneously, requires less computational power for training than other projection-based low-level features. Therefore, they can be are a viable choice for usage in shallow learning content-based music genre classification. | ['Juliano Henrique Foleiss', 'Tiago Fernandes Tavares'] | 2019-11-12 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 0.3762427 -0.1359307 -0.25590622 -0.2766311 -0.94891334 -0.6885636
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70c6bd61-beb0-479a-9814-654eb064e4f4 | what-goes-on-inside-rumour-and-non-rumour | 2112.03003 | null | https://arxiv.org/abs/2112.03003v1 | https://arxiv.org/pdf/2112.03003v1.pdf | What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses | In recent years, the problem of rumours on online social media (OSM) has attracted lots of attention. Researchers have started investigating from two main directions. First is the descriptive analysis of rumours and secondly, proposing techniques to detect (or classify) rumours. In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isnt much emphasis on psycho-linguistics analyses of social media text. These kinds of analyses on rumour case studies are vital for drawing meaningful conclusions to mitigate misinformation. For our analysis, we explored the PHEME9 rumour dataset (consisting of 9 events), including source tweets (both rumour and non-rumour categories) and response tweets. We compared the rumour and nonrumour source tweets and then their corresponding reply (response) tweets to understand how they differ linguistically for every incident. Furthermore, we also evaluated if these features can be used for classifying rumour vs. non-rumour tweets through machine learning models. To this end, we employed various classical and ensemble-based approaches. To filter out the highly discriminative psycholinguistic features, we explored the SHAP AI Explainability tool. To summarise, this research contributes by performing an in-depth psycholinguistic analysis of rumours related to various kinds of events. | ['Alexander Gelbukh', 'Grigori Sidorov', 'Rajesh Sharma', 'Shakshi Sharma', 'Sabur Butt'] | 2021-11-09 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.14703575e-01 3.68774295e-01 -1.84601709e-01 -2.19135448e-01
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c3a694cb-c09c-4b8f-94d9-6f2a1aa2081a | evaluation-of-taxonomic-and-neural-embedding | 2209.15197 | null | https://arxiv.org/abs/2209.15197v1 | https://arxiv.org/pdf/2209.15197v1.pdf | Evaluation of taxonomic and neural embedding methods for calculating semantic similarity | Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity. | ['Yanqin Yin', 'Dongqiang Yang'] | 2022-09-30 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 1.07360221e-01 -3.18444133e-01 -2.47000352e-01 -4.24643904e-01
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fb32cd07-dca6-4b9f-bbfe-7369596beba3 | fairgen-fair-synthetic-data-generation | 2210.13023 | null | https://arxiv.org/abs/2210.13023v2 | https://arxiv.org/pdf/2210.13023v2.pdf | FairGen: Fair Synthetic Data Generation | With the rising adoption of Machine Learning across the domains like banking, pharmaceutical, ed-tech, etc, it has become utmost important to adopt responsible AI methods to ensure models are not unfairly discriminating against any group. Given the lack of clean training data, generative adversarial techniques are preferred to generate synthetic data with several state-of-the-art architectures readily available across various domains from unstructured data such as text, images to structured datasets modelling fraud detection and many more. These techniques overcome several challenges such as class imbalance, limited training data, restricted access to data due to privacy issues. Existing work focusing on generating fair data either works for a certain GAN architecture or is very difficult to tune across the GANs. In this paper, we propose a pipeline to generate fairer synthetic data independent of the GAN architecture. The proposed paper utilizes a pre-processing algorithm to identify and remove bias inducing samples. In particular, we claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples. Our experimental evaluation on two open-source datasets demonstrates how the proposed pipeline is generating fair data along with improved performance in some cases. | ['Himanshu Chaudhary', 'Tanmoy Bhowmik', 'Kamna Meena', 'Aakash Agarwal', 'Bhushan Chaudhari'] | 2022-10-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.74974036e-01 6.15864038e-01 3.15984921e-03 -6.00552619e-01
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236baa6a-4fdb-4a4a-80b5-cd392e31e0fa | extractive-text-summarization-using | 2212.10707 | null | https://arxiv.org/abs/2212.10707v1 | https://arxiv.org/pdf/2212.10707v1.pdf | Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection | Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification. | ['Kelton Augusto Pontara da Costa', 'João Paulo Papa', 'Vinícius Camargo da Silva'] | 2022-12-21 | null | null | null | null | ['additive-models', 'extractive-summarization', 'extractive-document-summarization'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 5.22926331e-01 7.88192272e-01 -3.21462274e-01 -4.76048410e-01
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e7d6edb0-588e-4e04-ade9-914e2bc1c428 | active-learning-for-natural-language | 2305.15040 | null | https://arxiv.org/abs/2305.15040v1 | https://arxiv.org/pdf/2305.15040v1.pdf | Active Learning for Natural Language Generation | The field of text generation suffers from a severe shortage of labeled data due to the extremely expensive and time consuming process involved in manual annotation. A natural approach for coping with this problem is active learning (AL), a well-known machine learning technique for improving annotation efficiency by selectively choosing the most informative examples to label. However, while AL has been well-researched in the context of text classification, its application to text generation remained largely unexplored. In this paper, we present a first systematic study of active learning for text generation, considering a diverse set of tasks and multiple leading AL strategies. Our results indicate that existing AL strategies, despite their success in classification, are largely ineffective for the text generation scenario, and fail to consistently surpass the baseline of random example selection. We highlight some notable differences between the classification and generation scenarios, and analyze the selection behaviors of existing AL strategies. Our findings motivate exploring novel approaches for applying AL to NLG tasks. | ['Liat Ein-Dor', 'Noam Slonim', 'Dafna Sheinwald', 'Michal Shmueli-Scheuer', 'Ariel Gera', 'Yotam Perlitz'] | 2023-05-24 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 9.27917302e-01 5.73074341e-01 -5.77291012e-01 -3.36495787e-01
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e349ea3e-548c-47bc-ab54-7a09dc87b919 | punctuation-prediction-with-transition-based | null | null | https://aclanthology.org/P13-1074 | https://aclanthology.org/P13-1074.pdf | Punctuation Prediction with Transition-based Parsing | null | ['Dong-dong Zhang', 'Nan Yang', 'Mu Li', 'Shuangzhi Wu'] | 2013-08-01 | null | null | null | acl-2013-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ab300f0f-6ed4-45cf-a0b4-6eebae8dde79 | retrodiction-as-delayed-recurrence-the-case | null | null | https://aclanthology.org/2021.alta-1.17 | https://aclanthology.org/2021.alta-1.17.pdf | Retrodiction as Delayed Recurrence: the Case of Adjectives in Italian and English | We address the question of how to account for both forward and backward dependencies in an online processing account of human language acquisition. We focus on descriptive adjectives in English and Italian, and show that the acquisition of adjectives in these languages likely relies on tracking both forward and backward regularities. Our simulations confirm that forward-predicting models like standard Recurrent Neural Networks (RNN) cannot account for this phenomenon due to the lack of backward prediction, but the addition of a small delay (as proposed in Turek et al., 2019) endows the RNN with the ability to not only predict but also retrodict. | ['Atiqah Khaliq', 'Francesca Zermiani', 'Raquel G. Alhama'] | null | null | null | null | alta-2021-12 | ['language-acquisition'] | ['natural-language-processing'] | [-1.01315416e-01 1.61306784e-01 3.27442661e-02 -3.96622181e-01
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51dfc6bf-aa59-45c2-83cb-c4afc8f8ca5c | modular-interactive-video-object-segmentation | 2103.07941 | null | https://arxiv.org/abs/2103.07941v3 | https://arxiv.org/pdf/2103.07941v3.pdf | Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion | We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-$k$ filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research. | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Ho Kei Cheng'] | 2021-03-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 3.10377032e-01 -4.09464054e-02 1.64688826e-01 -4.66641575e-01
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fbaf1c69-8f3c-448d-a982-9883015587dd | named-entity-recognition-as-dependency | 2005.07150 | null | https://arxiv.org/abs/2005.07150v3 | https://arxiv.org/pdf/2005.07150v3.pdf | Named Entity Recognition as Dependency Parsing | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points. | ['Juntao Yu', 'Bernd Bohnet', 'Massimo Poesio'] | 2020-05-14 | named-entity-recognition-as-dependency-1 | https://aclanthology.org/2020.acl-main.577 | https://aclanthology.org/2020.acl-main.577.pdf | acl-2020-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-4.54074353e-01 4.21432942e-01 -2.96453774e-01 -4.22253340e-01
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efaf5d55-376f-4586-8897-c7b20fa2c9c8 | trading-syntax-trees-for-wordpieces-target | 2305.11034 | null | https://arxiv.org/abs/2305.11034v1 | https://arxiv.org/pdf/2305.11034v1.pdf | Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement | State-of-the-art target-oriented opinion word extraction (TOWE) models typically use BERT-based text encoders that operate on the word level, along with graph convolutional networks (GCNs) that incorporate syntactic information extracted from syntax trees. These methods achieve limited gains with GCNs and have difficulty using BERT wordpieces. Meanwhile, BERT wordpieces are known to be effective at representing rare words or words with insufficient context information. To address this issue, this work trades syntax trees for BERT wordpieces by entirely removing the GCN component from the methods' architectures. To enhance TOWE performance, we tackle the issue of aspect representation loss during encoding. Instead of solely utilizing a sentence as the input, we use a sentence-aspect pair. Our relatively simple approach achieves state-of-the-art results on benchmark datasets and should serve as a strong baseline for further research. | ['Nikolaos Aletras', 'Kai Sun', 'Samuel Mensah'] | 2023-05-18 | null | null | null | null | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [ 2.37694785e-01 2.30510250e-01 -4.62723613e-01 -5.05074084e-01
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01cc11a1-57af-44fa-a36f-3fcb900bf8aa | unsupervised-object-localization-observing | 2212.07834 | null | https://arxiv.org/abs/2212.07834v2 | https://arxiv.org/pdf/2212.07834v2.pdf | Unsupervised Object Localization: Observing the Background to Discover Objects | Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image with no supervision is a very hard task; what are the desired objects, when to separate them into parts, how many are there, and of what classes? The answers to these questions depend on the tasks and datasets of evaluation. In this work, we take a different approach and propose to look for the background instead. This way, the salient objects emerge as a by-product without any strong assumption on what an object should be. We propose FOUND, a simple model made of a single $conv1\times1$ initialized with coarse background masks extracted from self-supervised patch-based representations. After fast training and refining these seed masks, the model reaches state-of-the-art results on unsupervised saliency detection and object discovery benchmarks. Moreover, we show that our approach yields good results in the unsupervised semantic segmentation retrieval task. The code to reproduce our results is available at https://github.com/valeoai/FOUND. | ['Patrick Pérez', 'Éloi Zablocki', 'Antonin Vobecky', 'Gilles Puy', 'Chloé Sekkat', 'Oriane Siméoni'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-object-localization', 'object-discovery', 'saliency-detection', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.80500251e-01 2.20612094e-01 -2.31620431e-01 -3.23872149e-01
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4a93b5f3-a525-4d2b-a77d-684fdd2e0486 | facial-de-occlusion-network-for-virtual | 2210.12622 | null | https://arxiv.org/abs/2210.12622v1 | https://arxiv.org/pdf/2210.12622v1.pdf | Facial De-occlusion Network for Virtual Telepresence Systems | To see what is not in the image is one of the broader missions of computer vision. Technology to inpaint images has made significant progress with the coming of deep learning. This paper proposes a method to tackle occlusion specific to human faces. Virtual presence is a promising direction in communication and recreation for the future. However, Virtual Reality (VR) headsets occlude a significant portion of the face, hindering the photo-realistic appearance of the face in the virtual world. State-of-the-art image inpainting methods for de-occluding the eye region does not give usable results. To this end, we propose a working solution that gives usable results to tackle this problem enabling the use of the real-time photo-realistic de-occluded face of the user in VR settings. | ['Avinash Sharma', 'Ashwath Shetty', 'Surabhi Gupta'] | 2022-10-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 1.74126178e-01 4.21107948e-01 4.01541770e-01 -1.66977987e-01
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7acd8623-2a81-4fe0-a702-ff2fd0594435 | introduction-of-medical-imaging-modalities | 2306.01022 | null | https://arxiv.org/abs/2306.01022v2 | https://arxiv.org/pdf/2306.01022v2.pdf | Introduction of Medical Imaging Modalities | The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system. | ['Md Monjur Hossain Bhuiyan', 'Dr. Kishor Datta Gupta', 'Dr. Md Azim Ullah', 'Ismail Hossain', 'MD Abdullah Al Nasim', 'S. K. M Shadekul Islam'] | 2023-06-01 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 4.32516575e-01 -1.41406953e-01 -4.54196215e-01 2.93420833e-02
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95cff414-6e5e-4a1b-8eed-2fc8c52ee0e5 | recognizing-birds-from-sound-the-2018 | 1804.07177 | null | http://arxiv.org/abs/1804.07177v1 | http://arxiv.org/pdf/1804.07177v1.pdf | Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System | Reliable identification of bird species in recorded audio files would be a
transformative tool for researchers, conservation biologists, and birders. In
recent years, artificial neural networks have greatly improved the detection
quality of machine learning systems for bird species recognition. We present a
baseline system using convolutional neural networks. We publish our code base
as reference for participants in the 2018 LifeCLEF bird identification task and
discuss our experiments and potential improvements. | ['Maximilian Eibl', 'Thomas Wilhelm-Stein', 'Danny Kowerko', 'Stefan Kahl', 'Holger Klinck'] | 2018-04-19 | null | null | null | null | ['bird-audio-detection'] | ['audio'] | [-7.02876970e-02 -6.24532878e-01 -1.46262556e-01 -3.99535954e-01
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1bda5e03-6b4b-412b-aa11-931c75aaa0ab | face-frontalization-for-alignment-and | 1502.00852 | null | http://arxiv.org/abs/1502.00852v1 | http://arxiv.org/pdf/1502.00852v1.pdf | Face frontalization for Alignment and Recognition | Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a novel method for joint face landmark localization and frontal face
reconstruction (pose correction) using a small set of frontal images only. By
observing that the frontal facial image is the one with the minimum rank from
all different poses we formulate an appropriate model which is able to jointly
recover the facial landmarks as well as the frontalized version of the face. To
this end, a suitable optimization problem, involving the minimization of the
nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is
assessed in frontal face reconstruction (pose correction), face landmark
localization, and pose-invariant face recognition and verification by
conducting experiments on $6$ facial images databases. The experimental results
demonstrate the effectiveness of the proposed method. | ['Stefanos Zafeiriou', 'Christos Sagonas', 'Yannis Panagakis', 'Maja Pantic'] | 2015-02-03 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.93545315e-02 -8.53264630e-02 2.92347688e-02 -7.84838200e-01
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3a615272-e8c9-44e4-950d-2ca2055fe840 | a-novel-framework-based-on-medical-concept-1 | null | null | https://aclanthology.org/2022.findings-acl.110 | https://aclanthology.org/2022.findings-acl.110.pdf | A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge | Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Recent studies employ deep neural networks and the external knowledge to tackle it. However, such approaches lack interpretability which is a vital issue in medical application. Moreover, due to the lengthy and noisy clinical notes, such approaches fail to achieve satisfactory results. Therefore, in this paper, we propose a novel framework based on medical concept driven attention to incorporate external knowledge for explainable medical code prediction. In specific, both the clinical notes and Wikipedia documents are aligned into topic space to extract medical concepts using topic modeling. Then, the medical concept-driven attention mechanism is applied to uncover the medical code related concepts which provide explanations for medical code prediction. Experimental results on the benchmark dataset show the superiority of the proposed framework over several state-of-the-art baselines. | ['Deyu Zhou', 'Junxi Liu', 'Chenchen Ye', 'Linhai Zhang', 'Tao Wang'] | null | null | null | null | findings-acl-2022-5 | ['medical-code-prediction'] | ['medical'] | [ 1.21991411e-01 4.15697157e-01 -2.65199006e-01 -4.38840538e-01
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266d4077-cf38-4455-a7e9-adaf692db892 | identity-aware-attribute-recognition-via-real | 2008.05255 | null | https://arxiv.org/abs/2008.05255v1 | https://arxiv.org/pdf/2008.05255v1.pdf | Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds | With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by both simulations with real datasets and system implementation in a real testbed. Evaluation results show that the performance of the proposed algorithm with distributed inference framework is promising, by reaching the accuracies of attribute recognition and person identification up to 92.9% and 96.6% respectively, and significantly reducing the inference delay by at least 40.6% compared with existing methods. | ['HuiZhi Liang', 'Qiufen Xia', 'Pan Zhou', 'Zichuan Xu', 'Jiankang Ren', 'Jiangkai Wu'] | 2020-08-12 | null | null | null | null | ['pedestrian-attribute-recognition', 'person-identification'] | ['computer-vision', 'computer-vision'] | [-4.2736191e-01 -3.7676629e-01 2.3427966e-01 -4.8572138e-01
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034202eb-5057-41f8-a8bb-2d115d8686d9 | exploration-of-reinforcement-learning-for | 2004.00801 | null | https://arxiv.org/abs/2004.00801v1 | https://arxiv.org/pdf/2004.00801v1.pdf | Exploration of Reinforcement Learning for Event Camera using Car-like Robots | We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible to achieve much faster control of robots compared with the existing vision-based reinforcement-learning applications using standard cameras. To handle a stream of events for reinforcement learning, we introduced an image-like feature and demonstrated the feasibility of training an agent in a simulator for two tasks: fast collision avoidance and obstacle tracking. Finally, we set up a robot with an event camera in the real world and then transferred the agent trained in the simulator, resulting in successful fast avoidance of randomly thrown objects. Incorporating event camera into reinforcement learning opens new possibilities for various robotics applications that require swift control, such as autonomous vehicles and drones, through end-to-end learning approaches. | ['Shintaro Shiba', 'Riku Arakawa'] | 2020-04-02 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [-2.03865439e-01 5.13005443e-02 3.07291806e-01 -1.63480788e-01
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574bc250-3bf0-41cc-9dc8-c03dd357f03b | qlora-efficient-finetuning-of-quantized-llms | 2305.14314 | null | https://arxiv.org/abs/2305.14314v1 | https://arxiv.org/pdf/2305.14314v1.pdf | QLoRA: Efficient Finetuning of Quantized LLMs | We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training. | ['Luke Zettlemoyer', 'Ari Holtzman', 'Artidoro Pagnoni', 'Tim Dettmers'] | 2023-05-23 | null | null | null | null | ['chatbot', 'instruction-following', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.74370217e-01 -4.48304117e-01 -3.43655556e-01 -2.58754641e-01
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e6c7e89c-411f-4630-8569-e30d7f32b4cd | hardware-conditioned-policies-for-multi-robot | 1811.09864 | null | http://arxiv.org/abs/1811.09864v2 | http://arxiv.org/pdf/1811.09864v2.pdf | Hardware Conditioned Policies for Multi-Robot Transfer Learning | Deep reinforcement learning could be used to learn dexterous robotic policies
but it is challenging to transfer them to new robots with vastly different
hardware properties. It is also prohibitively expensive to learn a new policy
from scratch for each robot hardware due to the high sample complexity of
modern state-of-the-art algorithms. We propose a novel approach called
\textit{Hardware Conditioned Policies} where we train a universal policy
conditioned on a vector representation of robot hardware. We considered robots
in simulation with varied dynamics, kinematic structure, kinematic lengths and
degrees-of-freedom. First, we use the kinematic structure directly as the
hardware encoding and show great zero-shot transfer to completely novel robots
not seen during training. For robots with lower zero-shot success rate, we also
demonstrate that fine-tuning the policy network is significantly more
sample-efficient than training a model from scratch. In tasks where knowing the
agent dynamics is important for success, we learn an embedding for robot
hardware and show that policies conditioned on the encoding of hardware tend to
generalize and transfer well. The code and videos are available on the project
webpage: https://sites.google.com/view/robot-transfer-hcp. | ['Tao Chen', 'Abhinav Gupta', 'Adithyavairavan Murali'] | 2018-11-24 | hardware-conditioned-policies-for-multi-robot-1 | http://papers.nips.cc/paper/8145-hardware-conditioned-policies-for-multi-robot-transfer-learning | http://papers.nips.cc/paper/8145-hardware-conditioned-policies-for-multi-robot-transfer-learning.pdf | neurips-2018-12 | ['transfer-reinforcement-learning', 'industrial-robots'] | ['methodology', 'robots'] | [-7.96141177e-02 2.79468656e-01 -1.23457171e-01 -8.95364732e-02
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146ec243-1537-46e9-9a45-73cffacbcfc2 | a-stacking-gated-neural-architecture-for | null | null | https://aclanthology.org/D16-1246 | https://aclanthology.org/D16-1246.pdf | A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification | null | ['Hai Zhao', 'Zhisong Zhang', 'Lianhui Qin'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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56af8049-7c71-448c-9604-31098e9cf557 | cprune-compiler-informed-model-pruning-for | 2207.01260 | null | https://arxiv.org/abs/2207.01260v2 | https://arxiv.org/pdf/2207.01260v2.pdf | CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution | Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers have focused on either making a lightweight deep neural network (DNN) model using model pruning or generating an efficient code using compiler optimization. Surprisingly, we found that the straightforward integration between model compression and compiler auto-tuning often does not produce the most efficient model for a target device. We propose CPrune, a compiler-informed model pruning for efficient target-aware DNN execution to support an application with a required target accuracy. CPrune makes a lightweight DNN model through informed pruning based on the structural information of subgraphs built during the compiler tuning process. Our experimental results show that CPrune increases the DNN execution speed up to 2.73x compared to the state-of-the-art TVM auto-tune while satisfying the accuracy requirement. | ['TaeHo Kim', 'Sangtae Ha', 'Jemin Lee', 'Yongin Kwon'] | 2022-07-04 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 2.17021629e-01 1.04432650e-01 -6.93604469e-01 -6.16899133e-01
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2.14937344e-01 -3.93402427e-01 1.61165804e-01 -2.36146733e-01] | [8.542539596557617, 3.0457961559295654] |
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