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1fb322c0-eb24-4ecb-b7d8-d9ba7446f73f | mid-space-independent-deformable-image | null | null | https://doi.org/10.1016/j.neuroimage.2017.02.055 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5432428/pdf/nihms859177.pdf | Mid-space-independent deformable image registration | Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric – that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images. | ['B. Fischl', 'M. R. Sabuncu', 'M. Reuter', 'J. E. Iglesias', 'I. Aganj'] | 2017-05-15 | null | null | null | neuroimage-2017-5 | ['deformable-medical-image-registration'] | ['medical'] | [ 1.71216041e-01 2.04971030e-01 4.04199958e-02 -5.66077352e-01
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b45fc68c-a416-41ab-9453-3f56012e1dba | supervised-and-unsupervised-deep-learning | 2304.14922 | null | https://arxiv.org/abs/2304.14922v1 | https://arxiv.org/pdf/2304.14922v1.pdf | Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction | Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. Most of the previous work is focused at seizure detection, we pivot our focus to seizure prediction problem. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives. | ['Shehroz S. Khan', 'Milos R. Popovic', 'Zakary Georgis-Yap'] | 2023-04-24 | null | null | null | null | ['seizure-prediction', 'seizure-detection'] | ['medical', 'medical'] | [ 4.21787910e-02 -6.19441122e-02 3.22944105e-01 -3.83736610e-01
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d35e64f3-b69d-411e-a055-35773912c96b | make-a-choice-knowledge-base-question | 2305.13972 | null | https://arxiv.org/abs/2305.13972v1 | https://arxiv.org/pdf/2305.13972v1.pdf | Make a Choice! Knowledge Base Question Answering with In-Context Learning | Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB). Due to the large scale of KB, annotated data is impossible to cover all fact schemas in KB, which poses a challenge to the generalization ability of methods that require a sufficient amount of annotated data. Recently, LLMs have shown strong few-shot performance in many NLP tasks. We expect LLM can help existing methods improve their generalization ability, especially in low-resource situations. In this paper, we present McL-KBQA, a framework that incorporates the few-shot ability of LLM into the KBQA method via ICL-based multiple choice and then improves the effectiveness of the QA tasks. Experimental results on two KBQA datasets demonstrate the competitive performance of McL-KBQA with strong improvements in generalization. We expect to explore a new way to QA tasks from KBQA in conjunction with LLM, how to generate answers normatively and correctly with strong generalization. | ['Wenliang Chen', 'Wenbiao Shao', 'Yuehe Chen', 'Chuanyuan Tan'] | 2023-05-23 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.32567981e-01 6.58236921e-01 -7.84578100e-02 -4.77563024e-01
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cd4924c3-0095-430e-a712-06ea604d8f16 | boxinst-high-performance-instance | 2012.02310 | null | https://arxiv.org/abs/2012.02310v1 | https://arxiv.org/pdf/2012.02310v1.pdf | BoxInst: High-Performance Instance Segmentation with Box Annotations | We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP of 21.1% in Hsu et al. (2019) to 31.6% on the COCO dataset). Our core idea is to redesign the loss of learning masks in instance segmentation, with no modification to the segmentation network itself. The new loss functions can supervise the mask training without relying on mask annotations. This is made possible with two loss terms, namely, 1) a surrogate term that minimizes the discrepancy between the projections of the ground-truth box and the predicted mask; 2) a pairwise loss that can exploit the prior that proximal pixels with similar colors are very likely to have the same category label. Experiments demonstrate that the redesigned mask loss can yield surprisingly high-quality instance masks with only box annotations. For example, without using any mask annotations, with a ResNet-101 backbone and 3x training schedule, we achieve 33.2% mask AP on COCO test-dev split (vs. 39.1% of the fully supervised counterpart). Our excellent experiment results on COCO and Pascal VOC indicate that our method dramatically narrows the performance gap between weakly and fully supervised instance segmentation. Code is available at: https://git.io/AdelaiDet | ['Hao Chen', 'Xinlong Wang', 'Chunhua Shen', 'Zhi Tian'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.pdf | cvpr-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.40803611e-01 5.28363168e-01 -1.86119422e-01 -5.76783895e-01
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b84cfae6-4a2a-42d8-856c-7c12422dc8b0 | scenic-language-based-scene-generation | 1809.09310 | null | https://arxiv.org/abs/1809.09310v2 | https://arxiv.org/pdf/1809.09310v2.pdf | Scenic: A Language for Scenario Specification and Scene Generation | We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods. | ['Sanjit A. Seshia', 'Alberto L. Sangiovanni-Vincentelli', 'Shromona Ghosh', 'Xiangyu Yue', 'Tommaso Dreossi', 'Daniel J. Fremont'] | 2018-09-25 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 1.32195652e-01 2.46194243e-01 2.94132829e-01 -5.96696973e-01
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358443f4-ec91-42aa-a624-bb4034562d26 | gpt4tools-teaching-large-language-model-to | 2305.18752 | null | https://arxiv.org/abs/2305.18752v1 | https://arxiv.org/pdf/2305.18752v1.pdf | GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction | This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools. | ['Ying Shan', 'Xiu Li', 'Yixiao Ge', 'Sijie Zhao', 'Yanwei Li', 'Lin Song', 'Rui Yang'] | 2023-05-30 | null | null | null | null | ['instruction-following', 'prompt-engineering'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.41206121e-02 -1.94941238e-01 2.79502175e-03 -1.94829613e-01
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26154f00-e461-4264-b7ef-9ec23e2bebab | translatotron-3-speech-to-speech-translation | 2305.17547 | null | https://arxiv.org/abs/2305.17547v2 | https://arxiv.org/pdf/2305.17547v2.pdf | Translatotron 3: Speech to Speech Translation with Monolingual Data | This paper presents Translatotron 3, a novel approach to train a direct speech-to-speech translation model from monolingual speech-text datasets only in a fully unsupervised manner. Translatotron 3 combines masked autoencoder, unsupervised embedding mapping, and back-translation to achieve this goal. Experimental results in speech-to-speech translation tasks between Spanish and English show that Translatotron 3 outperforms a baseline cascade system, reporting 18.14 BLEU points improvement on the synthesized Unpaired-Conversational dataset. In contrast to supervised approaches that necessitate real paired data, which is unavailable, or specialized modeling to replicate para-/non-linguistic information, Translatotron 3 showcases its capability to retain para-/non-linguistic such as pauses, speaking rates, and speaker identity. Audio samples can be found in our website http://google-research.github.io/lingvo-lab/translatotron3 | ['Chulayuth Asawaroengchai', 'Michelle Tadmor Ramanovich', 'Heiga Zen', 'Yifan Ding', 'Alon Levkovitch', 'Eliya Nachmani'] | 2023-05-27 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [-1.07127585e-01 3.64083856e-01 -2.70360619e-01 -5.02988100e-01
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7b27de5d-eeae-46fe-a10c-1243c17cf0fc | differentiable-dynamic-programming-for | 1802.03676 | null | http://arxiv.org/abs/1802.03676v2 | http://arxiv.org/pdf/1802.03676v2.pdf | Differentiable Dynamic Programming for Structured Prediction and Attention | Dynamic programming (DP) solves a variety of structured combinatorial
problems by iteratively breaking them down into smaller subproblems. In spite
of their versatility, DP algorithms are usually non-differentiable, which
hampers their use as a layer in neural networks trained by backpropagation. To
address this issue, we propose to smooth the max operator in the dynamic
programming recursion, using a strongly convex regularizer. This allows to
relax both the optimal value and solution of the original combinatorial
problem, and turns a broad class of DP algorithms into differentiable
operators. Theoretically, we provide a new probabilistic perspective on
backpropagating through these DP operators, and relate them to inference in
graphical models. We derive two particular instantiations of our framework, a
smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm
for time-series alignment. We showcase these instantiations on two structured
prediction tasks and on structured and sparse attention for neural machine
translation. | ['Mathieu Blondel', 'Arthur Mensch'] | 2018-02-11 | differentiable-dynamic-programming-for-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2114 | http://proceedings.mlr.press/v80/mensch18a/mensch18a.pdf | icml-2018-7 | ['time-series-alignment'] | ['time-series'] | [ 7.66893923e-01 5.68434477e-01 -3.66418928e-01 -4.62632686e-01
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20e09e5d-502c-480e-af4d-2800ae01d2cd | modular-decomposition-of-protein-structure | 1809.06632 | null | http://arxiv.org/abs/1809.06632v1 | http://arxiv.org/pdf/1809.06632v1.pdf | Modular decomposition of protein structure using community detection | As the number of solved protein structures increases, the opportunities for
meta-analysis of this dataset increase too. Protein structures are known to be
formed of domains; structural and functional subunits that are often repeated
across sets of proteins. These domains generally form compact, globular
regions, and are therefore often easily identifiable by inspection, yet the
problem of automatically fragmenting the protein into these compact
substructures remains computationally challenging. Existing domain
classification methods focus on finding subregions of protein structure that
are conserved, rather than finding a decomposition which spans the full protein
structure. However, such a decomposition would find ready application in
coarse-graining molecular dynamics, analysing the protein's topology, in de
novo protein design and in fitting electron microscopy maps. Here, we present a
tool for performing this modular decomposition using the Infomap community
detection algorithm. The protein structure is abstracted into a network in
which its amino acids are the nodes, and where the edges are generated using a
simple proximity test. Infomap can then be used to identify highly
intra-connected regions of the protein. We perform this decomposition
systematically across 4000 distinct protein structures, taken from the Protein
Data Bank. The decomposition obtained correlates well with existing PFAM
sequence classifications, but has the advantage of spanning the full protein,
with the potential for novel domains. The coarse-grained network formed by the
communities can also be used as a proxy for protein topology at the
single-chain level; we demonstrate that grouping these proteins by their
coarse-grained network results in a functionally significant classification. | [] | 2018-09-18 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.43417042e-01 1.43546879e-01 -2.48087779e-01 -1.19486555e-01
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adefb078-acfc-4eb5-9949-9891b9b293f4 | learning-to-solve-combinatorial-graph | 2205.14105 | null | https://arxiv.org/abs/2205.14105v1 | https://arxiv.org/pdf/2205.14105v1.pdf | Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration | From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in this setting as it can adapt to specific problem structures and does not require pre-solved instances for these, often NP-hard, problems. However, state-of-the-art (SOTA) approaches typically suffer from severe scalability issues, primarily due to their reliance on expensive graph neural networks (GNNs) at each decision step. We introduce ECORD; a novel RL algorithm that alleviates this expense by restricting the GNN to a single pre-processing step, before entering a fast-acting exploratory phase directed by a recurrent unit. Experimentally, ECORD achieves a new SOTA for RL algorithms on the Maximum Cut problem, whilst also providing orders of magnitude improvement in speed and scalability. Compared to the nearest competitor, ECORD reduces the optimality gap by up to 73% on 500 vertex graphs with a decreased wall-clock time. Moreover, ECORD retains strong performance when generalising to larger graphs with up to 10000 vertices. | ['Alexandre Laterre', 'Christopher W. F. Parsonson', 'Thomas D. Barrett'] | 2022-05-27 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.51544410e-01 3.27461213e-01 -2.46393785e-01 1.74262315e-01
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d3ee40de-dd4d-4325-abe1-ab7387ae96e0 | clinical-longformer-and-clinical-bigbird | 2201.11838 | null | https://arxiv.org/abs/2201.11838v3 | https://arxiv.org/pdf/2201.11838v3.pdf | Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences | Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when performed on clinical named entity recognition and natural language inference tasks. One of the core limitations of these transformers is the substantial memory consumption due to their full self-attention mechanism. To overcome this, long sequence transformer models, e.g. Longformer and BigBird, were proposed with the idea of sparse attention mechanism to reduce the memory usage from quadratic to the sequence length to a linear scale. These models extended the maximum input sequence length from 512 to 4096, which enhanced the ability of modeling long-term dependency and consequently achieved optimal results in a variety of tasks. Inspired by the success of these long sequence transformer models, we introduce two domain enriched language models, namely Clinical-Longformer and Clinical-BigBird, which are pre-trained from large-scale clinical corpora. We evaluate both pre-trained models using 10 baseline tasks including named entity recognition, question answering, and document classification tasks. The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT as well as other short-sequence transformers in all downstream tasks. We have made our source code available at [https://github.com/luoyuanlab/Clinical-Longformer] the pre-trained models available for public download at: [https://huggingface.co/yikuan8/Clinical-Longformer]. | ['Yuan Luo', 'Hanyin Wang', 'Faraz S. Ahmad', 'Ramsey M. Wehbe', 'Yikuan Li'] | 2022-01-27 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-1.91652149e-01 1.62634403e-01 -1.87101811e-01 -2.82390624e-01
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c6e3c70d-e695-4cce-8fa1-14dc0b4a8307 | doubly-attentive-decoder-for-multi-modal | 1702.01287 | null | http://arxiv.org/abs/1702.01287v1 | http://arxiv.org/pdf/1702.01287v1.pdf | Doubly-Attentive Decoder for Multi-modal Neural Machine Translation | We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set. | ['Iacer Calixto', 'Qun Liu', 'Nick Campbell'] | 2017-02-04 | doubly-attentive-decoder-for-multi-modal-1 | https://aclanthology.org/P17-1175 | https://aclanthology.org/P17-1175.pdf | acl-2017-7 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.13247204e-01 2.92505801e-01 -2.50591576e-01 -3.32754523e-01
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1cd0cb36-7879-4286-9d45-5b8439408a0f | adversarial-autoencoders-for-generating-3d | 1811.07605 | null | http://arxiv.org/abs/1811.07605v3 | http://arxiv.org/pdf/1811.07605v3.pdf | Adversarial Autoencoders for Compact Representations of 3D Point Clouds | Deep generative architectures provide a way to model not only images but also
complex, 3-dimensional objects, such as point clouds. In this work, we present
a novel method to obtain meaningful representations of 3D shapes that can be
used for challenging tasks including 3D points generation, reconstruction,
compression, and clustering. Contrary to existing methods for 3D point cloud
generation that train separate decoupled models for representation learning and
generation, our approach is the first end-to-end solution that allows to
simultaneously learn a latent space of representation and generate 3D shape out
of it. Moreover, our model is capable of learning meaningful compact binary
descriptors with adversarial training conducted on a latent space. To achieve
this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D
input and create 3D output. Thanks to our end-to-end training regime, the
resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either
binary or continuous latent space that covers a much wider portion of training
data distribution. Finally, our quantitative evaluation shows that 3dAAE
provides state-of-the-art results for 3D points clustering and 3D object
retrieval. | ['Tomasz Trzciński', 'Rafał Nowak', 'Piotr Klukowski', 'Maciej Zięba', 'Maciej Zamorski', 'Wojciech Stokowiec', 'Karol Kurach'] | 2018-11-19 | null | null | null | null | ['point-cloud-generation', 'generating-3d-point-clouds', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.02902785e-01 1.26220912e-01 2.31479272e-01 -9.47928801e-02
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20033521-52d3-4602-9583-e63ec5ad0d14 | autoregressive-unsupervised-image | 2007.08247 | null | https://arxiv.org/abs/2007.08247v1 | https://arxiv.org/pdf/2007.08247v1.pdf | Autoregressive Unsupervised Image Segmentation | In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspiration from autoregressive generative models that predict the current pixel from past pixels in a raster-scan ordering created with masked convolutions, we propose to use different orderings over the inputs using various forms of masked convolutions to construct different views of the data. For a given input, the model produces a pair of predictions with two valid orderings, and is then trained to maximize the mutual information between the two outputs. These outputs can either be low-dimensional features for representation learning or output clusters corresponding to semantic labels for clustering. While masked convolutions are used during training, in inference, no masking is applied and we fall back to the standard convolution where the model has access to the full input. The proposed method outperforms current state-of-the-art on unsupervised image segmentation. It is simple and easy to implement, and can be extended to other visual tasks and integrated seamlessly into existing unsupervised learning methods requiring different views of the data. | ['Céline Hudelot', 'Yassine Ouali', 'Myriam Tami'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/160_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520137.pdf | eccv-2020-8 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 7.59416640e-01 4.34237510e-01 4.17184383e-02 -8.64348114e-01
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4d546baf-be26-4559-a6b5-ade8790514b5 | evaluation-metrics-for-cnns-compression | 2305.10616 | null | https://arxiv.org/abs/2305.10616v2 | https://arxiv.org/pdf/2305.10616v2.pdf | Evaluation Metrics for DNNs Compression | There is a lot of research effort into developing different techniques for neural networks compression. However, the community lacks standardised evaluation metrics, which are key to identifying the most suitable compression technique for different applications. This paper reviews existing neural network compression evaluation metrics and implements them into a standardisation framework called NetZIP. We introduce two novel metrics to cover existing gaps of evaluation in the literature: 1) Compression and Hardware Agnostic Theoretical Speed (CHATS) and 2) Overall Compression Success (OCS). We demonstrate the use of NetZIP using two case studies focusing on object classification and object detection. | ['Samuel Budgett', 'Kerstin Eder', 'Phil Reiter', 'Hamid Asgari', 'Dieter Balemans', 'Abanoub Ghobrial'] | 2023-05-18 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.12458563e-01 -1.91259608e-01 -5.67631662e-01 -5.14590979e-01
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e84d9f18-7de8-41e6-9d5a-13f5e845eb10 | large-scale-cell-level-quality-of-service | 2212.14071 | null | https://arxiv.org/abs/2212.14071v2 | https://arxiv.org/pdf/2212.14071v2.pdf | Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques | This study presents a general machine learning framework to estimate the traffic-measurement-level experience rate at given throughput values in the form of a Key Performance Indicator for the cells on base stations across various cities, using busy-hour counter data, and several technical parameters together with the network topology. Relying on feature engineering techniques, scores of additional predictors are proposed to enhance the effects of raw correlated counter values over the corresponding targets, and to represent the underlying interactions among groups of cells within nearby spatial locations effectively. An end-to-end regression modeling is applied on the transformed data, with results presented on unseen cities of varying sizes. | ['Mahiye Uluyağmur Öztürk', 'Ufuk Uyan', 'M. Tuğberk İşyapar'] | 2022-12-28 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.91499782e-01 -7.28899762e-02 -2.68462121e-01 -4.85114485e-01
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e4ad4c9d-50b7-4651-bbc1-21b44565d35d | boost-rs-boosted-embeddings-for-recommender | 2109.14766 | null | https://arxiv.org/abs/2109.14766v1 | https://arxiv.org/pdf/2109.14766v1.pdf | Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction | Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Recommender systems (RS), which are currently unexplored for the enzyme-substrate interaction prediction problem, can be utilized to provide enzyme recommendations for substrates, and vice versa. The performance of Collaborative-Filtering (CF) recommender systems however hinges on the quality of embedding vectors of users and items (enzymes and substrates in our case). Importantly, enhancing CF embeddings with heterogeneous auxiliary data, specially relational data (e.g., hierarchical, pairwise, or groupings), remains a challenge. We propose an innovative general RS framework, termed Boost-RS, that enhances RS performance by "boosting" embedding vectors through auxiliary data. Specifically, Boost-RS is trained and dynamically tuned on multiple relevant auxiliary learning tasks Boost-RS utilizes contrastive learning tasks to exploit relational data. To show the efficacy of Boost-RS for the enzyme-substrate prediction interaction problem, we apply the Boost-RS framework to several baseline CF models. We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning. We also show that Boost-RS outperforms similarity-based models. Ablation studies and visualization of learned representations highlight the importance of using contrastive learning on some of the auxiliary data in boosting the embedding vectors. | ['Soha Hassoun', 'Li-Ping Liu', 'Xinmeng Li'] | 2021-09-28 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.11917931e-01 -9.44441929e-02 -3.64144176e-01 -2.07848221e-01
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f3a4e2ea-0e41-49ac-b980-390158873252 | aei-actors-environment-interaction-with | 2110.11474 | null | https://arxiv.org/abs/2110.11474v2 | https://arxiv.org/pdf/2110.11474v2.pdf | AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation | Humans typically perceive the establishment of an action in a video through the interaction between an actor and the surrounding environment. An action only starts when the main actor in the video begins to interact with the environment, while it ends when the main actor stops the interaction. Despite the great progress in temporal action proposal generation, most existing works ignore the aforementioned fact and leave their model learning to propose actions as a black-box. In this paper, we make an attempt to simulate that ability of a human by proposing Actor Environment Interaction (AEI) network to improve the video representation for temporal action proposals generation. AEI contains two modules, i.e., perception-based visual representation (PVR) and boundary-matching module (BMM). PVR represents each video snippet by taking human-human relations and humans-environment relations into consideration using the proposed adaptive attention mechanism. Then, the video representation is taken by BMM to generate action proposals. AEI is comprehensively evaluated in ActivityNet-1.3 and THUMOS-14 datasets, on temporal action proposal and detection tasks, with two boundary-matching architectures (i.e., CNN-based and GCN-based) and two classifiers (i.e., Unet and P-GCN). Our AEI robustly outperforms the state-of-the-art methods with remarkable performance and generalization for both temporal action proposal generation and temporal action detection. | ['Minh-Triet Tran', 'Ngan Le', 'Kris Kitani', 'Sang Truong', 'Kashu Yamazaki', 'Hyekang Joo', 'Khoa Vo'] | 2021-10-21 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 5.60006201e-01 -9.60293785e-03 -2.43927628e-01 -1.55315027e-01
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5b85b45d-4ec5-4533-b11c-3789bbcc9a44 | diff-id-an-explainable-identity-difference | 2303.18174 | null | https://arxiv.org/abs/2303.18174v1 | https://arxiv.org/pdf/2303.18174v1.pdf | Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection | Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions. | ['Wenzhi Chen', 'Shouling Ji', 'Yang Xiang', 'Zonghui Wang', 'Senbo Yan', 'Yuxuan Duan', 'Xuhong Zhang', 'Chuer Yu'] | 2023-03-30 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 4.17027861e-01 -2.23065689e-01 1.79832295e-01 -4.32814658e-01
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fe0e012f-f012-4754-a003-7c9ec8e545a7 | endpoints-weight-fusion-for-class-incremental | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | Endpoints Weight Fusion for Class Incremental Semantic Segmentation | Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions. In addition, we analyze the relation between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving the state-of-the-art performance. | ['Ming-Ming Cheng', 'Joost Van de Weijer', 'Xialei Liu', 'Jiekang Feng', 'Chang-Bin Zhang', 'Jia-Wen Xiao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'class-incremental-semantic-segmentation', 'incremental-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.97962010e-01 2.64511444e-02 -2.78431207e-01 -2.70390868e-01
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a1bd76b9-3451-4b5a-b801-152b784f6792 | whole-slide-mitosis-detection-in-he-breast | 1808.05896 | null | http://arxiv.org/abs/1808.05896v1 | http://arxiv.org/pdf/1808.05896v1.pdf | Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks | Manual counting of mitotic tumor cells in tissue sections constitutes one of
the strongest prognostic markers for breast cancer. This procedure, however, is
time-consuming and error-prone. We developed a method to automatically detect
mitotic figures in breast cancer tissue sections based on convolutional neural
networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained
histological tissue sections is hampered by: (1) noisy and expensive reference
standards established by pathologists, (2) lack of generalization due to
staining variation across laboratories, and (3) high computational requirements
needed to process gigapixel whole-slide images (WSIs). In this paper, we
present a method to train and evaluate CNNs to specifically solve these issues
in the context of mitosis detection in breast cancer WSIs. First, by combining
image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides
and registration, we built a reference standard for mitosis detection in entire
H&E WSIs requiring minimal manual annotation effort. Second, we designed a data
augmentation strategy that creates diverse and realistic H&E stain variations
by modifying the hematoxylin and eosin color channels directly. Using it during
training combined with network ensembling resulted in a stain invariant mitosis
detector. Third, we applied knowledge distillation to reduce the computational
requirements of the mitosis detection ensemble with a negligible loss of
performance. The system was trained in a single-center cohort and evaluated in
an independent multicenter cohort from The Cancer Genome Atlas on the three
tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a
performance within the top-3 best methods for most of the tasks of the
challenge. | ['Jeroen van der Laak', 'Carla Wauters', 'Irene Otte-Holler', 'Willem Vreuls', 'Suzanne Mol', 'Rob van de Loo', 'Geert Litjens', 'Nico Karssemeijer', 'Maschenka Balkenhol', 'David Tellez', 'Rob Vogels', 'Peter Bult', 'Francesco Ciompi'] | 2018-08-17 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 2.64254183e-01 1.55511647e-01 -1.70848832e-01 -1.05139256e-01
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4cd5be3e-0320-40f3-8644-a39fe1ac0c10 | improving-video-instance-segmentation-via | 2107.13155 | null | https://arxiv.org/abs/2107.13155v2 | https://arxiv.org/pdf/2107.13155v2.pdf | Improving Video Instance Segmentation via Temporal Pyramid Routing | Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are mainly based on single-frame features or single-scale features of multiple frames, where either temporal information or multi-scale information is ignored. To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames. Specifically, TPR contains two novel components, including Dynamic Aligned Cell Routing (DACR) and Cross Pyramid Routing (CPR), where DACR is designed for aligning and gating pyramid features across temporal dimension, while CPR transfers temporally aggregated features across scale dimension. Moreover, our approach is a light-weight and plug-and-play module and can be easily applied to existing instance segmentation methods. Extensive experiments on three datasets including YouTube-VIS (2019, 2021) and Cityscapes-VPS demonstrate the effectiveness and efficiency of the proposed approach on several state-of-the-art video instance and panoptic segmentation methods. Codes will be publicly available at \url{https://github.com/lxtGH/TemporalPyramidRouting}. | ['DaCheng Tao', 'Henghui Ding', 'Yibo Yang', 'Yunhai Tong', 'Guangliang Cheng', 'Kuiyuan Yang', 'Hao He', 'Xiangtai Li'] | 2021-07-28 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.04853922e-01 -4.87848341e-01 -4.19494063e-01 -2.55144715e-01
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c09d8ee2-f086-484d-a9b0-7ccc35a12c51 | superpixelwise-low-rank-approximation-based | null | null | https://ieeexplore.ieee.org/document/10136223 | https://ieeexplore.ieee.org/document/10136223 | Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification | Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, among which only one of them is valid; this is also known as partial label learning) during the labeling process. Accordingly, how to learn from such data with ambiguous labels is a problem of great practical importance. In this letter, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification. SLAP is mainly composed of two phases: disambiguating the training labels and acquiring the predictive model. Specifically, in the first phase, we propose a superpixelwise LRA-based model, preparing the affinity graph for the subsequent label propagation process while extracting the discriminative representation to enhance the following classification task of the second phase. Then to disambiguate the training labels, label propagation propagates the labeling information via the affinity graph of training pixels. In the second phase, we take advantage of the resulting disambiguated training labels and the discriminative representations to enhance the classification performance. The extensive experiments validate the advantage of the proposed SLAP method over state-of-the-art methods. | ['Shujun Yang; Yu Zhang; Yao Ding; Danfeng Hong'] | 2023-05-25 | superpixelwise-low-rank-approximation-based-1 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10136223 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10136223 | journal-grsl-2023-5 | ['partial-label-learning'] | ['methodology'] | [ 8.64506721e-01 -1.25609515e-02 -1.99329048e-01 -4.11953002e-01
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268fbc7f-32c1-4d68-8ce7-9036daca1963 | ita-image-text-alignments-for-multi-modal-1 | null | null | https://openreview.net/forum?id=AC8P4mj14AM | https://openreview.net/pdf?id=AC8P4mj14AM | ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition | Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representations. However, it is difficult to model such interactions as image and text representations are trained separately on the data of their respective modality and are not aligned in the same space. As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized. ITA first aligns the image into regional object tags, image-level captions and optical characters as visual contexts, concatenates them with the input texts as a new cross-modal input, and then feeds it into a pretrained textual embedding model. This makes it easier for the attention module of a pretrained textual embedding model to model the interaction between the two modalities since they are both represented in the textual space. ITA further aligns the output distributions predicted from the cross-modal input and textual input views so that the MNER model can be more practical and robust to noises from images. In our experiments, we show that ITA models can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets, even without image information. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [ 1.17766187e-01 -2.29735095e-02 -1.54013529e-01 -3.71045113e-01
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92fdd178-8d1f-4d4f-9cf9-2b2c523242a4 | deep-rbfnet-point-cloud-feature-learning | 1812.04302 | null | http://arxiv.org/abs/1812.04302v2 | http://arxiv.org/pdf/1812.04302v2.pdf | Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions | Three-dimensional object recognition has recently achieved great progress
thanks to the development of effective point cloud-based learning frameworks,
such as PointNet and its extensions. However, existing methods rely heavily on
fully connected layers, which introduce a significant amount of parameters,
making the network harder to train and prone to overfitting problems. In this
paper, we propose a simple yet effective framework for point set feature
learning by leveraging a nonlinear activation layer encoded by Radial Basis
Function (RBF) kernels. Unlike PointNet variants, that fail to recognize local
point patterns, our approach explicitly models the spatial distribution of
point clouds by aggregating features from sparsely distributed RBF kernels. A
typical RBF kernel, e.g. Gaussian function, naturally penalizes long-distance
response and is only activated by neighboring points. Such localized response
generates highly discriminative features given different point distributions.
In addition, our framework allows the joint optimization of kernel distribution
and its receptive field, automatically evolving kernel configurations in an
end-to-end manner. We demonstrate that the proposed network with a single RBF
layer can outperform the state-of-the-art Pointnet++ in terms of classification
accuracy for 3D object recognition tasks. Moreover, the introduction of
nonlinear mappings significantly reduces the number of network parameters and
computational cost, enabling significantly faster training and a deployable
point cloud recognition solution on portable devices with limited resources. | ['Chao Chen', 'Weikai Chen', 'Jun Xing', 'Xiaoguang Han', 'Guanbin Li', 'Yajie Zhao', 'Hao Li'] | 2018-12-11 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-3.58553469e-01 -4.71190363e-01 9.22296718e-02 -4.46290940e-01
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368d166a-9569-443b-955a-427fb99ce3b9 | local-to-global-information-communication-for | 2302.08481 | null | https://arxiv.org/abs/2302.08481v1 | https://arxiv.org/pdf/2302.08481v1.pdf | Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search | Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that utilize a simplified search space with cell-sharing way, we introduce a new search space where a lightweight model can be more effectively searched by replacing the cell-sharing manner with cell-independent one. Based on this, the communication of local to global information is achieved through two well-designed modules. For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells. For global information aggregation, we propose a novel dense-connected fusion module (cell) which aggregates long-range multi-level features in the network automatically. In addition, a latency-oriented constraint is endowed into the search process to balance the accuracy and latency. We name the proposed framework as Local-to-Global Information Communication Network Search (LGCNet). Extensive experiments on Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU with the speed of 115.2 FPS on Titan Xp. | ['Peiwen Lin', 'Shuchang Lyu', 'Ting-Bing Xu', 'Peng Sun', 'Guangliang Cheng'] | 2023-02-16 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-1.54033735e-01 -3.22283134e-02 -1.36004150e-01 -4.44084883e-01
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c8a34143-d331-49f6-b2d8-e7640a3f7413 | spatial-feature-calibration-and-temporal | 2104.05606 | null | https://arxiv.org/abs/2104.05606v1 | https://arxiv.org/pdf/2104.05606v1.pdf | Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation | Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level instance segmentation, ignoring the temporal correlation between adjacent frames. To address these issues, we propose a simple yet effective one-stage video instance segmentation framework by spatial calibration and temporal fusion, namely STMask. To ensure spatial feature calibration with ground-truth bounding boxes, we first predict regressed bounding boxes around ground-truth bounding boxes, and extract features from them for frame-level instance segmentation. To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our framework to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses. Experiments on the YouTube-VIS valid set show that the proposed STMask with ResNet-50/-101 backbone obtains 33.5 % / 36.8 % mask AP, while achieving 28.6 / 23.4 FPS on video instance segmentation. The code is released online https://github.com/MinghanLi/STMask. | ['Lei Zhang', 'Lida Li', 'Shuai Li', 'Minghan Li'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.84045613e-01 -9.37623307e-02 -3.62638831e-01 -3.42673898e-01
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97e64bcc-8f2a-4df6-808b-b805559b420c | challenges-of-using-real-world-sensory-inputs | 2306.09281 | null | https://arxiv.org/abs/2306.09281v1 | https://arxiv.org/pdf/2306.09281v1.pdf | Challenges of Using Real-World Sensory Inputs for Motion Forecasting in Autonomous Driving | Motion forecasting plays a critical role in enabling robots to anticipate future trajectories of surrounding agents and plan accordingly. However, existing forecasting methods often rely on curated datasets that are not faithful to what real-world perception pipelines can provide. In reality, upstream modules that are responsible for detecting and tracking agents, and those that gather road information to build the map, can introduce various errors, including misdetections, tracking errors, and difficulties in being accurate for distant agents and road elements. This paper aims to uncover the challenges of bringing motion forecasting models to this more realistic setting where inputs are provided by perception modules. In particular, we quantify the impacts of the domain gap through extensive evaluation. Furthermore, we design synthetic perturbations to better characterize their consequences, thus providing insights into areas that require improvement in upstream perception modules and guidance toward the development of more robust forecasting methods. | ['Patrick Pérez', 'Matthieu Cord', 'Mickaël Chen', 'Éloi Zablocki', 'Loïck Chambon', 'Yihong Xu'] | 2023-06-15 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [ 8.36547185e-03 1.37346327e-01 2.00286210e-01 -3.61923426e-01
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69e14027-93d7-4de8-9ef4-0bb6f63d7f03 | robustness-of-edited-neural-networks | 2303.00046 | null | https://arxiv.org/abs/2303.00046v1 | https://arxiv.org/pdf/2303.00046v1.pdf | Robustness of edited neural networks | Successful deployment in uncertain, real-world environments requires that deep learning models can be efficiently and reliably modified in order to adapt to unexpected issues. However, the current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there is growing interest in model editing, which enables computationally inexpensive, interpretable, post-hoc model modifications. While many model editing techniques are promising, research on the properties of edited models is largely limited to evaluation of validation accuracy. The robustness of edited models is an important and yet mostly unexplored topic. In this paper, we employ recently developed techniques from the field of deep learning robustness to investigate both how model editing affects the general robustness of a model, as well as the robustness of the specific behavior targeted by the edit. We find that edits tend to reduce general robustness, but that the degree of degradation depends on the editing algorithm chosen. In particular, robustness is best preserved by more constrained techniques that modify less of the model. Motivated by these observations, we introduce two new model editing algorithms, direct low-rank model editing and 1-layer interpolation (1-LI), which each exhibit strong generalization performance. | ['Henry Kvinge', 'Jonathan Tu', 'Cody Nizinski', 'Charles Godfrey', 'Davis Brown'] | 2023-02-28 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 1.93495214e-01 -7.21927807e-02 3.53501514e-02 -4.36130196e-01
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73dc8b37-898f-4c9c-a895-d77fb310b9fc | deep-occupancy-predictive-representations-for | 2303.04218 | null | https://arxiv.org/abs/2303.04218v1 | https://arxiv.org/pdf/2303.04218v1.pdf | Deep Occupancy-Predictive Representations for Autonomous Driving | Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance to motion planning, our proposed architecture encodes the probabilistic occupancy map as a proxy for obtaining pre-trained state representations. By leveraging a map-aware graph formulation of the environment, our agent-centric encoder generalizes to arbitrary road networks and traffic situations. We show that our approach significantly improves the downstream performance of a reinforcement learning agent operating in urban traffic environments. | ['Matthias Althoff', 'Lars Frederik Peiss', 'Eivind Meyer'] | 2023-03-07 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.60361168e-02 4.70885158e-01 -6.50073111e-01 -3.67098838e-01
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9d5c4d0f-6a71-4d3d-b05f-e2de25804bea | long-term-hourly-scenario-generation-for | 2306.16427 | null | https://arxiv.org/abs/2306.16427v1 | https://arxiv.org/pdf/2306.16427v1.pdf | Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power combining Variational Autoencoders with Radial Basis Function Kernels | Accurate generation of realistic future scenarios of renewable energy generation is crucial for long-term planning and operation of electrical systems, especially considering the increasing focus on sustainable energy and the growing penetration of renewable generation in energy matrices. These predictions enable power system operators and energy planners to effectively manage the variability and intermittency associated with renewable generation, allowing for better grid stability, improved energy management, and enhanced decision-making processes. In this paper, we propose an innovative method for generating long-term hourly scenarios for wind and solar power generation, taking into consideration the correlation between these two energy sources. To achieve this, we combine the capabilities of a Variational Autoencoder (VAE) with the additional benefits of incorporating the Radial Basis Function (RBF) kernel in our artificial neural network architecture. By incorporating them, we aim to obtain a latent space with improved regularization properties. To evaluate the effectiveness of our proposed method, we conduct experiments in a representative study scenario, utilizing real-world wind and solar power generation data from the Brazil system. We compare the scenarios generated by our model with the observed data and with other sets of scenarios produced by a conventional VAE architecture. Our experimental results demonstrate that the proposed method can generate long-term hourly scenarios for wind and solar power generation that are highly correlated, accurately capturing the temporal and spatial characteristics of these energy sources. Taking advantage of the benefits of RBF in obtaining a well-regularized latent space, our approach offers improved accuracy and robustness in generating long-term hourly scenarios for renewable energy generation. | ['Julio Alberto Silva Dias'] | 2023-06-27 | null | null | null | null | ['decision-making', 'energy-management'] | ['reasoning', 'time-series'] | [-2.99890101e-01 -4.25273627e-01 2.59564221e-02 1.86485171e-01
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01034960-b9b9-4444-9103-21ce000038bf | knowledge-based-end-to-end-memory-networks | 1804.08204 | null | http://arxiv.org/abs/1804.08204v1 | http://arxiv.org/pdf/1804.08204v1.pdf | Knowledge-based end-to-end memory networks | End-to-end dialog systems have become very popular because they hold the
promise of learning directly from human to human dialog interaction. Retrieval
and Generative methods have been explored in this area with mixed results. A
key element that is missing so far, is the incorporation of a-priori knowledge
about the task at hand. This knowledge may exist in the form of structured or
unstructured information. As a first step towards this direction, we present a
novel approach, Knowledge based end-to-end memory networks (KB-memN2N), which
allows special handling of named entities for goal-oriented dialog tasks. We
present results on two datasets, DSTC6 challenge dataset and dialog bAbI tasks. | ['Jatin Ganhotra', 'Lazaros Polymenakos'] | 2018-04-23 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-1.78407803e-01 6.07574046e-01 6.45125583e-02 -6.82174027e-01
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3487eba1-d682-4f98-856f-4c7e555bf41b | deep-conditional-transformation-models-for | 2210.11366 | null | https://arxiv.org/abs/2210.11366v2 | https://arxiv.org/pdf/2210.11366v2.pdf | Deep conditional transformation models for survival analysis | An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classifiers. Parametric, distribution-free approaches which make full use of survival time and censoring status have not received much attention. We present deep conditional transformation models (DCTMs) for survival outcomes as a unifying approach to parametric and semiparametric survival analysis. DCTMs allow the specification of non-linear and non-proportional hazards for both tabular and non-tabular data and extend to all types of censoring and truncation. On real and semi-synthetic data, we show that DCTMs compete with state-of-the-art DL approaches to survival analysis. | ['Thomas J. Fuchs', 'Torsten Hothorn', 'Ida Häggström', 'Lucas Kook', 'Gabriele Campanella'] | 2022-10-20 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.09248832e-01 -9.93649736e-02 -8.01742196e-01 -8.21885586e-01
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1c4ecbfc-6feb-4318-96b8-6f5efc6dd3d6 | ppo-ue-proximal-policy-optimization-via | 2212.06343 | null | https://arxiv.org/abs/2212.06343v1 | https://arxiv.org/pdf/2212.06343v1.pdf | PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration | Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware explorations (UEs) based on a ratio uncertainty level. The proposed PPO-UE is designed to improve convergence speed and performance with an optimized ratio uncertainty level. Through extensive sensitivity analysis by varying the ratio uncertainty level, our proposed PPO-UE considerably outperforms the baseline PPO in Roboschool continuous control tasks. | ['Jin-Hee Cho', 'Dong H. Jeong', 'Feng Chen', 'Lance M. Kaplan', 'Audun Jøsang', 'Zhen Guo', 'Qisheng Zhang'] | 2022-12-13 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-5.30164480e-01 2.30358690e-02 -5.07057786e-01 1.90996781e-01
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21a4e771-3c15-4941-828f-84f6e6df73c1 | heterogeneous-graph-learning-for-visual | 1910.11475 | null | https://arxiv.org/abs/1910.11475v1 | https://arxiv.org/pdf/1910.11475v1.pdf | Heterogeneous Graph Learning for Visual Commonsense Reasoning | Visual commonsense reasoning task aims at leading the research field into solving cognition-level reasoning with the ability of predicting correct answers and meanwhile providing convincing reasoning paths, resulting in three sub-tasks i.e., Q->A, QA->R and Q->AR. It poses great challenges over the proper semantic alignment between vision and linguistic domains and knowledge reasoning to generate persuasive reasoning paths. Existing works either resort to a powerful end-to-end network that cannot produce interpretable reasoning paths or solely explore intra-relationship of visual objects (homogeneous graph) while ignoring the cross-domain semantic alignment among visual concepts and linguistic words. In this paper, we propose a new Heterogeneous Graph Learning (HGL) framework for seamlessly integrating the intra-graph and inter-graph reasoning in order to bridge vision and language domain. Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement. Moreover, our HGL integrates a contextual voting module to exploit a long-range visual context for better global reasoning. Experiments on the large-scale Visual Commonsense Reasoning benchmark demonstrate the superior performance of our proposed modules on three tasks (improving 5% accuracy on Q->A, 3.5% on QA->R, 5.8% on Q->AR) | ['Weijiang Yu', 'Xiaodan Liang', 'Weihao Yu', 'Jingwen Zhou', 'Nong Xiao'] | 2019-10-25 | heterogeneous-graph-learning-for-visual-1 | http://papers.nips.cc/paper/8544-heterogeneous-graph-learning-for-visual-commonsense-reasoning | http://papers.nips.cc/paper/8544-heterogeneous-graph-learning-for-visual-commonsense-reasoning.pdf | neurips-2019-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.06804979e-02 3.85248572e-01 -6.61513209e-02 -3.65476817e-01
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47e07a04-b993-4fe7-b410-1732b4a0ead7 | automatic-annotation-and-evaluation-of-error | null | null | https://aclanthology.org/P17-1074 | https://aclanthology.org/P17-1074.pdf | Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction | Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as {``}Good{''} or {``}Acceptable{''} in at least 95{\%} of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time. | ['Ted Briscoe', 'Mariano Felice', 'Christopher Bryant'] | 2017-07-01 | null | null | null | acl-2017-7 | ['annotated-code-search', 'table-annotation', 'table-annotation', 'news-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 3.88397336e-01 4.49661642e-01 6.06540322e-01 -7.36672401e-01
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5465fbd4-16bc-4ecf-ab0f-34ec15ebfff7 | accurate-temporal-action-proposal-generation | 2003.04145 | null | https://arxiv.org/abs/2003.04145v1 | https://arxiv.org/pdf/2003.04145v1.pdf | Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network | Accurate temporal action proposals play an important role in detecting actions from untrimmed videos. The existing approaches have difficulties in capturing global contextual information and simultaneously localizing actions with different durations. To this end, we propose a Relation-aware pyramid Network (RapNet) to generate highly accurate temporal action proposals. In RapNet, a novel relation-aware module is introduced to exploit bi-directional long-range relations between local features for context distilling. This embedded module enhances the RapNet in terms of its multi-granularity temporal proposal generation ability, given predefined anchor boxes. We further introduce a two-stage adjustment scheme to refine the proposal boundaries and measure their confidence in containing an action with snippet-level actionness. Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks demonstrate our RapNet generates superior accurate proposals over the existing state-of-the-art methods. | ['Yufeng Yuan', 'Guanshuo Wang', 'Xi Zhou', 'Jiani Li', 'Zhixiang Shi', 'Shiming Ge', 'Jialin Gao'] | 2020-03-09 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 3.68049502e-01 -3.70244026e-01 -5.41747093e-01 -3.42473537e-01
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9095fa24-a9a9-44b8-9ff7-8c87f3f7bdbe | poisson-image-deconvolution-by-a-plug-and | 2010.09321 | null | https://arxiv.org/abs/2010.09321v3 | https://arxiv.org/pdf/2010.09321v3.pdf | Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme | This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics. The efficiency of the proposed algorithm is evaluated for Poisson image deconvolution, which is very common for imaging applications, such as, for example, limited photon acquisition. Numerical results show the superiority of the proposed scheme compared to recent state-of-the-art techniques, for both low and high signal-to-noise-ratio scenarios. This performance gain is mostly explained by the flexibility of the embedded quantum denoiser for different types of noise affecting the observations. | ['Denis Kouamé', 'Bertrand Georgeot', 'Adrian Basarab', 'Sayantan Dutta'] | 2020-10-19 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.72331315e-01 -4.37603891e-03 5.85614800e-01 -1.96493119e-01
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94024c38-4bfb-4ece-990e-3b919cc0d9e0 | similarity-contrastive-estimation-for-image | 2212.11187 | null | https://arxiv.org/abs/2212.11187v1 | https://arxiv.org/pdf/2212.11187v1.pdf | Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning | Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise. However, several instances in a dataset are drawn from the same distribution and share underlying semantic information. A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise. To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE). Our training objective is a soft contrastive one that brings the positives closer and estimates a continuous distribution to push or pull negative instances based on their learned similarities. We validate empirically our approach on both image and video representation learning. We show that SCE performs competitively with the state of the art on the ImageNet linear evaluation protocol for fewer pretraining epochs and that it generalizes to several downstream image tasks. We also show that SCE reaches state-of-the-art results for pretraining video representation and that the learned representation can generalize to video downstream tasks. | ['Romain Hérault', 'Astrid Orcesi', 'Jaonary Rabarisoa', 'Julien Denize'] | 2022-12-21 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.53858137e-01 -8.70324895e-02 -3.26665640e-01 -5.19976735e-01
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08b86f05-e328-4209-afd3-94f7ccb9e089 | replication-study-development-and-validation | 1803.04337 | null | http://arxiv.org/abs/1803.04337v3 | http://arxiv.org/pdf/1803.04337v3.pdf | Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs | Replication studies are essential for validation of new methods, and are
crucial to maintain the high standards of scientific publications, and to use
the results in practice. We have attempted to replicate the main method in
'Development and validation of a deep learning algorithm for detection of
diabetic retinopathy in retinal fundus photographs' published in JAMA 2016;
316(22). We re-implemented the method since the source code is not available,
and we used publicly available data sets. The original study used non-public
fundus images from EyePACS and three hospitals in India for training. We used a
different EyePACS data set from Kaggle. The original study used the benchmark
data set Messidor-2 to evaluate the algorithm's performance. We used the same
data set. In the original study, ophthalmologists re-graded all images for
diabetic retinopathy, macular edema, and image gradability. There was one
diabetic retinopathy grade per image for our data sets, and we assessed image
gradability ourselves. Hyper-parameter settings were not described in the
original study. But some of these were later published. We were not able to
replicate the original study. Our algorithm's area under the receiver operating
curve (AUC) of 0.94 on the Kaggle EyePACS test set and 0.80 on Messidor-2 did
not come close to the reported AUC of 0.99 in the original study. This may be
caused by the use of a single grade per image, different data, or different not
described hyper-parameter settings. This study shows the challenges of
replicating deep learning, and the need for more replication studies to
validate deep learning methods, especially for medical image analysis.
Our source code and instructions are available at:
https://github.com/mikevoets/jama16-retina-replication | ['Kajsa Møllersen', 'Mike Voets', 'Lars Ailo Bongo'] | 2018-03-12 | null | null | null | null | ['diabetic-retinopathy-detection', 'mitosis-detection'] | ['medical', 'medical'] | [-3.17046762e-01 -1.46863073e-01 -1.70490950e-01 -3.95478189e-01
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46fc1abb-52ce-4083-bdc4-ab2011d3c1a9 | thompson-sampling-regret-bounds-for | 2304.13593 | null | https://arxiv.org/abs/2304.13593v1 | https://arxiv.org/pdf/2304.13593v1.pdf | Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards | In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by Neu et al. and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual information of the environment parameters and the history. Then, we introduce new bounds on the lifted information ratio that hold for sub-Gaussian rewards, thus generalizing the results from Neu et al. which analysis requires binary rewards. Finally, we provide explicit regret bounds for the special cases of unstructured bounded contextual bandits, structured bounded contextual bandits with Laplace likelihood, structured Bernoulli bandits, and bounded linear contextual bandits. | ['Mikael Skoglund', 'Tobias J. Oechtering', 'Borja Rodríguez-Gálvez', 'Amaury Gouverneur'] | 2023-04-26 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 2.87980020e-01 2.57870913e-01 -7.78723776e-01 -3.02009493e-01
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b9a85bca-9eb0-45b4-8dc6-fa3f91a30d5d | semi-local-3d-lane-detection-and-uncertainty | 2003.05257 | null | https://arxiv.org/abs/2003.05257v1 | https://arxiv.org/pdf/2003.05257v1.pdf | Semi-Local 3D Lane Detection and Uncertainty Estimation | We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes. This combination allows our method to generalize to complex lane topologies, curvatures and surface geometries. Additionally, our method is the first to output a learning based uncertainty estimation for the lane detection task. The efficacy of our method is demonstrated in extensive experiments achieving state-of-the-art results for camera-based 3D lane detection, while also showing our ability to generalize to complex topologies, curvatures and road geometries as well as to different cameras. We also demonstrate how our uncertainty estimation aligns with the empirical error statistics indicating that it is well calibrated and truly reflects the detection noise. | ['Max Bluvstein', 'Netalee Efrat', 'Shaul Oron', 'Noa Garnett', 'Dan Levi', 'Bat El Shlomo'] | 2020-03-11 | null | null | null | null | ['3d-lane-detection'] | ['computer-vision'] | [-1.98821247e-01 1.87556654e-01 -3.97131056e-01 -5.54781020e-01
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4b0c5154-20bf-40cf-95b4-8a5f03c02daa | transformer-capsule-model-for-intent | null | null | https://www.aaai.org/Papers/AAAI/2020GB/SA-ObuchowskiA.549.pdf | https://www.aaai.org/Papers/AAAI/2020GB/SA-ObuchowskiA.549.pdf | Transformer-Capsule Model for Intent Detection | Intent recognition is one of the most crucial tasks in NLUsystems, which are nowadays especially important for design-ing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU net-work implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu , and WebApp. | ['Michał Lew', 'Aleksander Obuchowski'] | 2020-02-07 | null | null | null | thirty-fourth-aaai-conference-on-artificial | ['intent-recognition'] | ['natural-language-processing'] | [-4.37033266e-01 -1.76582008e-03 -6.88585401e-01 -4.03649032e-01
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9ba29fc7-c3a2-4244-b526-bdef82580444 | recurrent-neural-networks-for-multivariate | 1606.01865 | null | http://arxiv.org/abs/1606.01865v2 | http://arxiv.org/pdf/1606.01865v2.pdf | Recurrent Neural Networks for Multivariate Time Series with Missing Values | Multivariate time series data in practical applications, such as health care,
geoscience, and biology, are characterized by a variety of missing values. In
time series prediction and other related tasks, it has been noted that missing
values and their missing patterns are often correlated with the target labels,
a.k.a., informative missingness. There is very limited work on exploiting the
missing patterns for effective imputation and improving prediction performance.
In this paper, we develop novel deep learning models, namely GRU-D, as one of
the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a
state-of-the-art recurrent neural network. It takes two representations of
missing patterns, i.e., masking and time interval, and effectively incorporates
them into a deep model architecture so that it not only captures the long-term
temporal dependencies in time series, but also utilizes the missing patterns to
achieve better prediction results. Experiments of time series classification
tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic
datasets demonstrate that our models achieve state-of-the-art performance and
provides useful insights for better understanding and utilization of missing
values in time series analysis. | ['David Sontag', 'Sanjay Purushotham', 'Kyunghyun Cho', 'Zhengping Che', 'Yan Liu'] | 2016-06-06 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [ 2.08478063e-01 -6.11170530e-01 -3.84714663e-01 -4.59805518e-01
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af4d0194-25ab-4772-84f4-e8bc7cca4a70 | regularized-densely-connected-pyramid-network | 2008.12416 | null | https://arxiv.org/abs/2008.12416v2 | https://arxiv.org/pdf/2008.12416v2.pdf | Regularized Densely-connected Pyramid Network for Salient Instance Segmentation | Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing \sArt competitors by 6.3\% (58.6\% vs. 52.3\%) in terms of the AP metric.The code is available at https://github.com/yuhuan-wu/RDPNet. | ['Ming-Ming Cheng', 'Le Zhang', 'Yu-Huan Wu', 'Wang Gao', 'Yun Liu'] | 2020-08-28 | null | null | null | null | ['salient-object-detection'] | ['computer-vision'] | [ 3.85996222e-01 4.73401159e-01 -4.48166996e-01 -5.73583126e-01
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73a51bfe-0803-4ed5-9ee8-ba7a63f3ca9c | speech-denoising-with-deep-feature-losses | 1806.10522 | null | http://arxiv.org/abs/1806.10522v1 | http://arxiv.org/pdf/1806.10522v1.pdf | Speech Denoising with Deep Feature Losses | We present an end-to-end deep learning approach to denoising speech signals
by processing the raw waveform directly. Given input audio containing speech
corrupted by an additive background signal, the system aims to produce a
processed signal that contains only the speech content. Recent approaches have
shown promising results using various deep network architectures. In this
paper, we propose to train a fully-convolutional context aggregation network
using a deep feature loss. That loss is based on comparing the internal feature
activations in a different network, trained for acoustic environment detection
and domestic audio tagging. Our approach outperforms the state-of-the-art in
objective speech quality metrics and in large-scale perceptual experiments with
human listeners. It also outperforms an identical network trained using
traditional regression losses. The advantage of the new approach is
particularly pronounced for the hardest data with the most intrusive background
noise, for which denoising is most needed and most challenging. | ['Vladlen Koltun', 'Francois G. Germain', 'Qifeng Chen'] | 2018-06-27 | null | null | null | null | ['audio-tagging', 'speech-denoising'] | ['audio', 'speech'] | [ 3.48961800e-01 -3.37514617e-02 8.00522685e-01 -4.09512043e-01
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e12b7320-e74a-4b29-bc55-6d97cd3fa5e9 | patch-craft-self-supervised-training-for | 2211.09919 | null | https://arxiv.org/abs/2211.09919v1 | https://arxiv.org/pdf/2211.09919v1.pdf | Patch-Craft Self-Supervised Training for Correlated Image Denoising | Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise. | ['Michael Elad', 'Gregory Vaksman'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.pdf | cvpr-2023-1 | ['patch-matching'] | ['computer-vision'] | [ 6.51424587e-01 -4.43711102e-01 1.74850181e-01 -2.37647697e-01
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6bb321f7-3fb3-4247-b525-977173f64ec5 | vstar-a-video-grounded-dialogue-dataset-for | 2305.18756 | null | https://arxiv.org/abs/2305.18756v1 | https://arxiv.org/pdf/2305.18756v1.pdf | VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions | Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues. Most existing benchmarks treat both modalities the same as a frame-independent visual understanding task, while neglecting the intrinsic attributes in multimodal dialogues, such as scene and topic transitions. In this paper, we present Video-grounded Scene&Topic AwaRe dialogue (VSTAR) dataset, a large scale video-grounded dialogue understanding dataset based on 395 TV series. Based on VSTAR, we propose two benchmarks for video-grounded dialogue understanding: scene segmentation and topic segmentation, and one benchmark for video-grounded dialogue generation. Comprehensive experiments are performed on these benchmarks to demonstrate the importance of multimodal information and segments in video-grounded dialogue understanding and generation. | ['Dongyan Zhao', 'Yueqian Wang', 'Jinpeng Li', 'Xueliang Zhao', 'Zilong Zheng', 'Yuxuan Wang'] | 2023-05-30 | null | null | null | null | ['scene-segmentation', 'dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 5.12777388e-01 5.67331076e-01 2.46066079e-02 -6.94532335e-01
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1b1ad75b-18bf-4e58-b05f-163ef5cdfbd4 | language-independent-neuro-symbolic-semantic | 2305.04460 | null | https://arxiv.org/abs/2305.04460v1 | https://arxiv.org/pdf/2305.04460v1.pdf | Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding | Recent works on form understanding mostly employ multimodal transformers or large-scale pre-trained language models. These models need ample data for pre-training. In contrast, humans can usually identify key-value pairings from a form only by looking at layouts, even if they don't comprehend the language used. No prior research has been conducted to investigate how helpful layout information alone is for form understanding. Hence, we propose a unique entity-relation graph parsing method for scanned forms called LAGNN, a language-independent Graph Neural Network model. Our model parses a form into a word-relation graph in order to identify entities and relations jointly and reduce the time complexity of inference. This graph is then transformed by deterministic rules into a fully connected entity-relation graph. Our model simply takes into account relative spacing between bounding boxes from layout information to facilitate easy transfer across languages. To further improve the performance of LAGNN, and achieve isomorphism between entity-relation graphs and word-relation graphs, we use integer linear programming (ILP) based inference. Code is publicly available at https://github.com/Bhanu068/LAGNN | ['Fatemeh Shiri', 'Lizhen Qu', 'Bhanu Prakash Voutharoja'] | 2023-05-08 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 1.50621325e-01 2.63427943e-01 -3.17442358e-01 -5.02554953e-01
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3debb294-4ed0-44aa-ba7f-9c07bd2bc3ee | c-2-sp-net-joint-compression-and | 2110.13674 | null | https://arxiv.org/abs/2110.13674v2 | https://arxiv.org/pdf/2110.13674v2.pdf | C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction | Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between sensors and processing apparatus and related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computation resource, especially for wearable and implantable medical devices. Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction. In this paper, we propose C$^2$SP-Net, to jointly solve compression, prediction, and reconstruction with a single neural network. A plug-and-play in-sensor compression matrix is constructed to reduce transmission bandwidth requirement. The compressed signal can be used for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated under various compression ratios. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.35 % in prediction accuracy with a compression ratio ranging from 1/2 to 1/16. | ['Mohamad Sawan', 'Jie Yang', 'Ziyu Wang', 'Yi Shi', 'Di wu'] | 2021-10-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 7.94461250e-01 -7.75843337e-02 2.03237180e-02 -2.50103265e-01
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4d5e9d2f-deb2-4347-829d-8d4133df4e7a | algo-synthesizing-algorithmic-programs-with | 2305.14591 | null | https://arxiv.org/abs/2305.14591v1 | https://arxiv.org/pdf/2305.14591v1.pdf | ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers | Large language models (LLMs) excel at implementing code from functionality descriptions, but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm. Moreover, LLM-generated programs lack guaranteed correctness and require human verification. To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the creation and verify their correctness. ALGO first generates a probably correct but possibly slow reference oracle by prompting an LLM to exhaustively enumerate all the combinations of relevant variables. This oracle is then utilized to guide an arbitrary search strategy in exploring the algorithm space and to verify the algorithms synthesized. Our study shows that the LLM-generated oracles are correct for 88% of the cases. With the oracles as verifiers, ALGO can be integrated with any existing code generation model in a model-agnostic manner to enhance its performance. Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT, the current state-of-the-art model on CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code Interpreter on unseen problems. | ['Lei LI', 'William Yang Wang', 'Jingtao Xia', 'Danqing Wang', 'Kexun Zhang'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-7.15298131e-02 1.02260299e-01 -3.30024600e-01 -2.73548096e-01
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a60e4bf7-73cd-42f1-8e07-2f50b3281309 | active-deep-learning-for-classification-of | 1611.10031 | null | http://arxiv.org/abs/1611.10031v1 | http://arxiv.org/pdf/1611.10031v1.pdf | Active Deep Learning for Classification of Hyperspectral Images | Active deep learning classification of hyperspectral images is considered in
this paper. Deep learning has achieved success in many applications, but
good-quality labeled samples are needed to construct a deep learning network.
It is expensive getting good labeled samples in hyperspectral images for remote
sensing applications. An active learning algorithm based on a weighted
incremental dictionary learning is proposed for such applications. The proposed
algorithm selects training samples that maximize two selection criteria, namely
representative and uncertainty. This algorithm trains a deep network
efficiently by actively selecting training samples at each iteration. The
proposed algorithm is applied for the classification of hyperspectral images,
and compared with other classification algorithms employing active learning. It
is shown that the proposed algorithm is efficient and effective in classifying
hyperspectral images. | ['HUI ZHANG', 'Peng Liu', 'Kie B. Eom'] | 2016-11-30 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 6.28955841e-01 -7.25203380e-02 -5.03000677e-01 -6.31256521e-01
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4cb5f111-d439-4cc6-a9f5-59004e37a527 | online-adaptive-image-reconstruction-onair | 1809.01817 | null | https://arxiv.org/abs/1809.01817v3 | https://arxiv.org/pdf/1809.01817v3.pdf | Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models | Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction. This paper proposes a framework for online (or time-sequential) adaptive reconstruction of dynamic image sequences from linear (typically undersampled) measurements. We model the spatiotemporal patches of the underlying dynamic image sequence as sparse in a dictionary, and we simultaneously estimate the dictionary and the images sequentially from streaming measurements. Multiple constraints on the adapted dictionary are also considered such as a unitary matrix, or low-rank dictionary atoms that provide additional efficiency or robustness. The proposed online algorithms are memory efficient and involve simple updates of the dictionary atoms, sparse coefficients, and images. Numerical experiments demonstrate the usefulness of the proposed methods in inverse problems such as video reconstruction or inpainting from noisy, subsampled pixels, and dynamic magnetic resonance image reconstruction from very limited measurements. | ['Brian E. Moore', 'Saiprasad Ravishankar', 'Raj Rao Nadakuditi', 'Jeffrey A. Fessler'] | 2018-09-06 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 7.06556976e-01 -1.95116460e-01 -1.91600531e-01 -1.81446239e-01
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bdbcb51c-a413-4c74-beb9-fcdb3cd981c7 | transfer-reward-learning-for-policy-gradient | 1909.03622 | null | https://arxiv.org/abs/1909.03622v1 | https://arxiv.org/pdf/1909.03622v1.pdf | Transfer Reward Learning for Policy Gradient-Based Text Generation | Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning paradigm. Hence, policy gradient methods are used since the gradient can be computed without requiring a differentiable objective. However, we argue that current n-gram overlap based measures that are used as rewards can be improved by using model-based rewards transferred from tasks that directly compare the similarity of sentence pairs. These reward models either output a score of sentence-level syntactic and semantic similarity between entire predicted and target sentences as the expected return, or for intermediate phrases as segmented accumulative rewards. We demonstrate that using a \textit{Transferable Reward Learner} leads to improved results on semantical evaluation measures in policy-gradient models for image captioning tasks. Our InferSent actor-critic model improves over a BLEU trained actor-critic model on MSCOCO when evaluated on a Word Mover's Distance similarity measure by 6.97 points, also improving on a Sliding Window Cosine Similarity measure by 10.48 points. Similar performance improvements are also obtained on the smaller Flickr-30k dataset, demonstrating the general applicability of the proposed transfer learning method. | ['Danushka Bollegala', "James O' Neill"] | 2019-09-09 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.82668185e-01 3.47999692e-01 -3.25758189e-01 -6.98185802e-01
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ae108f02-19a2-4008-9ae5-1e2afb164e72 | see-eye-to-eye-a-lidar-agnostic-3d-detection | 2111.09450 | null | https://arxiv.org/abs/2111.09450v2 | https://arxiv.org/pdf/2111.09450v2.pdf | See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation | Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brought about advances in mechanical, solid-state, and recently, adjustable scan pattern lidars. For the latter, existing works often require fine-tuning the model each time scan patterns are adjusted, which is infeasible. We explicitly deal with the sampling discrepancy by proposing a novel unsupervised multi-target domain adaptation framework, SEE, for transferring the performance of state-of-the-art 3D detectors across both fixed and flexible scan pattern lidars without requiring fine-tuning of models by end-users. Our approach interpolates the underlying geometry and normalizes the scan pattern of objects from different lidars before passing them to the detection network. We demonstrate the effectiveness of SEE on public datasets, achieving state-of-the-art results, and additionally provide quantitative results on a novel high-resolution lidar to prove the industry applications of our framework. | ['Eduardo Nebot', 'Stewart Worrall', 'Mao Shan', 'Julie Stephany Berrio', 'Darren Tsai'] | 2021-11-17 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.78270930e-01 -2.69672126e-01 -2.40835816e-01 -7.64416277e-01
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a59cb9b0-d1cb-4cba-a2c6-a42e61ff151f | disn-deep-implicit-surface-network-for-high | 1905.10711 | null | https://arxiv.org/abs/1905.10711v4 | https://arxiv.org/pdf/1905.10711v4.pdf | DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction | Reconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Network which can generate a high-quality detail-rich 3D mesh from an 2D image by predicting the underlying signed distance fields. In addition to utilizing global image features, DISN predicts the projected location for each 3D point on the 2D image, and extracts local features from the image feature maps. Combining global and local features significantly improves the accuracy of the signed distance field prediction, especially for the detail-rich areas. To the best of our knowledge, DISN is the first method that constantly captures details such as holes and thin structures present in 3D shapes from single-view images. DISN achieves the state-of-the-art single-view reconstruction performance on a variety of shape categories reconstructed from both synthetic and real images. Code is available at https://github.com/xharlie/DISN The supplementary can be found at https://xharlie.github.io/images/neurips_2019_supp.pdf | ['Radomir Mech', 'Duygu Ceylan', 'Weiyue Wang', 'Qiangeng Xu', 'Ulrich Neumann'] | 2019-05-26 | disn-deep-implicit-surface-network-for-high-1 | http://papers.nips.cc/paper/8340-disn-deep-implicit-surface-network-for-high-quality-single-view-3d-reconstruction | http://papers.nips.cc/paper/8340-disn-deep-implicit-surface-network-for-high-quality-single-view-3d-reconstruction.pdf | neurips-2019-12 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-5.50829507e-02 9.38018337e-02 -4.82227504e-02 -5.07118523e-01
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d7421c7f-1f90-4f3c-94e3-f1e0b719ba63 | sample-efficient-learning-of-novel-visual | 2306.09482 | null | https://arxiv.org/abs/2306.09482v1 | https://arxiv.org/pdf/2306.09482v1.pdf | Sample-Efficient Learning of Novel Visual Concepts | Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided. Unlike humans who excel at such tasks, these models often fail to leverage known relationships between entities in order to draw conclusions about such objects. In this work, we show that incorporating a symbolic knowledge graph into a state-of-the-art recognition model enables a new approach for effective few-shot classification. In our proposed neuro-symbolic architecture and training methodology, the knowledge graph is augmented with additional relationships extracted from a small set of examples, improving its ability to recognize novel objects by considering the presence of interconnected entities. Unlike existing few-shot classifiers, we show that this enables our model to incorporate not only objects but also abstract concepts and affordances. The existence of the knowledge graph also makes this approach amenable to interpretability through analysis of the relationships contained within it. We empirically show that our approach outperforms current state-of-the-art few-shot multi-label classification methods on the COCO dataset and evaluate the addition of abstract concepts and affordances on the Visual Genome dataset. | ['Katia Sycara', 'Joseph Campbell', 'Simon Stepputtis', 'Sarthak Bhagat'] | 2023-06-15 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.60424548e-01 2.93260068e-01 -1.96127892e-01 -2.79899925e-01
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8b75b963-5ead-4715-a558-aef0313d44a8 | remoteclip-a-vision-language-foundation-model | 2306.11029 | null | https://arxiv.org/abs/2306.11029v1 | https://arxiv.org/pdf/2306.11029v1.pdf | RemoteCLIP: A Vision Language Foundation Model for Remote Sensing | General-purpose foundation models have become increasingly important in the field of artificial intelligence. While self-supervised learning (SSL) and Masked Image Modeling (MIM) have led to promising results in building such foundation models for remote sensing, these models primarily learn low-level features, require annotated data for fine-tuning, and not applicable for retrieval and zero-shot applications due to the lack of language understanding. In response to these limitations, we propose RemoteCLIP, the first vision-language foundation model for remote sensing that aims to learn robust visual features with rich semantics, as well as aligned text embeddings for seamless downstream application. To address the scarcity of pre-training data, we leverage data scaling, converting heterogeneous annotations based on Box-to-Caption (B2C) and Mask-to-Box (M2B) conversion, and further incorporating UAV imagery, resulting a 12xlarger pretraining dataset. RemoteCLIP can be applied to a variety of downstream tasks, including zero-shot image classification, linear probing, k-NN classification, few-shot classification, image-text retrieval, and object counting. Evaluations on 16 datasets, including a newly introduced RemoteCount benchmark to test the object counting ability, show that RemoteCLIP consistently outperforms baseline foundation models across different model scales. Impressively, RemoteCLIP outperform previous SoTA by 9.14% mean recall on RSICD dataset and by 8.92% on RSICD dataset. For zero-shot classification, our RemoteCLIP outperform CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets. | ['Jun Zhou', 'Jiale Zhu', 'Xiaocong Zhou', 'Zhangqingyun Guan', 'Delong Chen', 'Fan Liu'] | 2023-06-19 | null | null | null | null | ['object-counting', 'classification-1'] | ['computer-vision', 'methodology'] | [ 4.39079225e-01 -2.83590406e-01 -4.19401854e-01 -4.79348332e-01
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3be2c56a-8128-4422-9e20-dc51cdd52e7b | deep-metric-color-embeddings-for-splicing | 2206.10737 | null | https://arxiv.org/abs/2206.10737v1 | https://arxiv.org/pdf/2206.10737v1.pdf | Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images | One common task in image forensics is to detect spliced images, where multiple source images are composed to one output image. Most of the currently best performing splicing detectors leverage high-frequency artifacts. However, after an image underwent strong compression, most of the high frequency artifacts are not available anymore. In this work, we explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild, subject to strong compression and downsampling. Our proposal is to model the color formation of an image. The color formation largely depends on variations at the scale of scene objects, and is hence much less dependent on high-frequency artifacts. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. Large distances in the embedding space indicate that two image regions either stem from different scenes or different cameras. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling. We confirm in two further experiments the dual nature of the metric space, namely to both characterize the acquisition camera and the scene illuminant color. As such, this work resides at the intersection of physics-based and statistical forensics with benefits from both sides. | ['Christian Riess', 'Benjamin Hadwiger'] | 2022-06-21 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.17625016e-01 -3.80385667e-01 3.41729999e-01 1.28054051e-02
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3ed02215-91f7-4d10-a17b-761eacae09d0 | ntire-2021-depth-guided-image-relighting | 2104.13365 | null | https://arxiv.org/abs/2104.13365v1 | https://arxiv.org/pdf/2104.13365v1.pdf | NTIRE 2021 Depth Guided Image Relighting Challenge | Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper. | ['Radu Timofte', 'Sabine Susstrunk', 'Ruofan Zhou', 'Majed El Helou'] | 2021-04-27 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 6.03480101e-01 -9.67408717e-02 1.56833246e-01 -6.46439075e-01
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ff97a4a1-d5ac-4092-bb32-9a018f80bfe9 | nuclei-segmentation-via-a-deep-panoptic-model | null | null | https://www.researchgate.net/publication/334843766_Nuclei_Segmentation_via_a_Deep_Panoptic_Model_with_Semantic_Feature_Fusion | https://www.ijcai.org/proceedings/2019/0121.pdf | Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion | Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis. Due to the high variability of nuclei appearances and numerous overlapping objects, this task still remains challenging.
eep learning based semantic and instance segmentation models have been proposed to address the challenges, but these methods tend to concentrate on either the global or local features and hence still suffer from information loss. In this work, we propose a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch
to integrate global and local features. Furthermore, we design a feature map fusion mechanism in the instance branch and a new mask generator to prevent information loss. Experimental results on three different histopathology datasets demonstrate that our method outperforms the state-of-the-art nuclei segmentation methods and popular semantic and instance segmentation models by a large margin. | ['Weidong Cai', 'Lauren O’Donnell', 'Fan Zhang', 'Yang song', 'Donghao Zhang', 'Dongnan Liu', 'Chaoyi Zhang'] | 2019-08-10 | null | null | null | international-joint-conference-on-artificial | ['nuclear-segmentation'] | ['medical'] | [ 2.78478861e-01 1.54391646e-01 -2.40062132e-01 -3.71614814e-01
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52b38477-07ae-4389-b4b0-8f9ef4b97036 | interpreting-neural-cwi-classifiers-weights | null | null | https://aclanthology.org/2020.bea-1.17 | https://aclanthology.org/2020.bea-1.17.pdf | Interpreting Neural CWI Classifiers' Weights as Vocabulary Size | Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read. Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult. This paper analyzes neural CWI classifiers and shows that some of their parameters can be interpreted as vocabulary size. We present a novel formalization of vocabulary size measurement methods that are practiced in the applied linguistics field as a kind of neural classifier. We also contribute to building a novel dataset for validating vocabulary testing and readability via crowdsourcing. | ['Yo Ehara'] | 2020-07-01 | null | null | null | ws-2020-7 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.78119645e-01 3.26821893e-01 -1.69251397e-01 -4.52624857e-01
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59233b51-f074-4077-8c76-fac3010ab578 | accomontage2-a-complete-harmonization-and | 2209.00353 | null | https://arxiv.org/abs/2209.00353v1 | https://arxiv.org/pdf/2209.00353v1.pdf | AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System | We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody. Following AccoMontage, this study focuses on generating piano arrangements for popular/folk songs and it carries on the generalized template-based retrieval method. The novelties of this study are twofold. First, we invent a harmonization module (which AccoMontage does not have). This module generates structured and coherent full-length chord progression by optimizing and balancing three loss terms: a micro-level loss for note-wise dissonance, a meso-level loss for phrase-template matching, and a macro-level loss for full piece coherency. Second, we develop a graphical user interface which allows users to select different styles of chord progression and piano texture. Currently, chord progression styles include Pop, R&B, and Dark, while piano texture styles include several levels of voicing density and rhythmic complexity. Experimental results show that both our harmonization and arrangement results significantly outperform the baselines. Lastly, we release AccoMontage2 as an online application as well as the organized chord progression templates as a public dataset. | ['Gus Xia', 'Jingwei Zhao', 'Haochen Hu', 'Li Yi'] | 2022-09-01 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.30616892e-02 -4.81319457e-01 -6.71066642e-02 5.26843518e-02
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6aa9ff76-edc5-4759-a0da-417c385efa25 | content-aware-directed-propagation-network | 2107.13144 | null | https://arxiv.org/abs/2107.13144v3 | https://arxiv.org/pdf/2107.13144v3.pdf | Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention | Convolutional neural networks (CNNs) have been not only widespread but also achieved noticeable results on numerous applications including image classification, restoration, and generation. Although the weight-sharing property of convolutions makes them widely adopted in various tasks, its content-agnostic characteristic can also be considered a major drawback. To solve this problem, in this paper, we propose a novel operation, called pixel adaptive kernel attention (PAKA). PAKA provides directivity to the filter weights by multiplying spatially varying attention from learnable features. The proposed method infers pixel-adaptive attention maps along the channel and spatial directions separately to address the decomposed model with fewer parameters. Our method is trainable in an end-to-end manner and applicable to any CNN-based models. In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM). We demonstrate the superiority of our HPM by presenting state-of-the-art performance on semantic segmentation compared to the conventional information aggregation modules. We validate the proposed method through additional ablation studies and visualizing the effect of PAKA providing directivity to the weights of convolutions. We also show the generalizability of the proposed method by applying it to multi-modal tasks especially color-guided depth map super-resolution. | ['Sung-Jea Ko', 'Seung-Won Jung', 'Yoon-Jae Yeo', 'Min-Cheol Sagong'] | 2021-07-28 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 3.96595716e-01 1.13462672e-01 9.60630924e-02 -3.95502687e-01
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f7965b37-9fd7-4229-8e69-b76542b7a142 | shift-variance-in-scene-text-detection | 2208.09231 | null | https://arxiv.org/abs/2208.09231v1 | https://arxiv.org/pdf/2208.09231v1.pdf | Shift Variance in Scene Text Detection | Theory of convolutional neural networks suggests the property of shift equivariance, i.e., that a shifted input causes an equally shifted output. In practice, however, this is not always the case. This poses a great problem for scene text detection for which a consistent spatial response is crucial, irrespective of the position of the text in the scene. Using a simple synthetic experiment, we demonstrate the inherent shift variance of a state-of-the-art fully convolutional text detector. Furthermore, using the same experimental setting, we show how small architectural changes can lead to an improved shift equivariance and less variation of the detector output. We validate the synthetic results using a real-world training schedule on the text detection network. To quantify the amount of shift variability, we propose a metric based on well-established text detection benchmarks. While the proposed architectural changes are not able to fully recover shift equivariance, adding smoothing filters can substantially improve shift consistency on common text datasets. Considering the potentially large impact of small shifts, we propose to extend the commonly used text detection metrics by the metric described in this work, in order to be able to quantify the consistency of text detectors. | ['Philipp Härtinger', 'Jan-Hendrik Neudeck', 'Markus Glitzner'] | 2022-08-19 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.18031490e-01 -2.88493931e-01 5.13998747e-01 -2.01581344e-01
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974ad4ed-6b33-43a4-bffd-e645362ee703 | efficient-evolutionary-methods-for-game-agent | 1901.00723 | null | http://arxiv.org/abs/1901.00723v1 | http://arxiv.org/pdf/1901.00723v1.pdf | Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best | This paper introduces a simple and fast variant of Planet Wars as a test-bed
for statistical planning based Game AI agents, and for noisy hyper-parameter
optimisation. Planet Wars is a real-time strategy game with simple rules but
complex game-play. The variant introduced in this paper is designed for speed
to enable efficient experimentation, and also for a fixed action space to
enable practical inter-operability with General Video Game AI agents. If we
treat the game as a win-loss game (which is standard), then this leads to
challenging noisy optimisation problems both in tuning agents to play the game,
and in tuning game parameters. Here we focus on the problem of tuning an agent,
and report results using the recently developed N-Tuple Bandit Evolutionary
Algorithm and a number of other optimisers, including Sequential Model-based
Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit
Evolutionary offers competitive performance as well as insight into the effects
of combinations of parameter choices. | ['Diego Perez-Liebana', 'John Woodward', 'Vanessa Volz', 'Simon M. Lucas', 'Raluca D. Gaina', 'Jialin Liu', 'Ivan Bravi'] | 2019-01-03 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 6.57302365e-02 -2.57500075e-02 5.60724847e-02 1.60518289e-01
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8fba9231-af1e-4529-b373-3ff9c0d72598 | dense-pose-transfer | 1809.01995 | null | http://arxiv.org/abs/1809.01995v1 | http://arxiv.org/pdf/1809.01995v1.pdf | Dense Pose Transfer | In this work we integrate ideas from surface-based modeling with neural
synthesis: we propose a combination of surface-based pose estimation and deep
generative models that allows us to perform accurate pose transfer, i.e.
synthesize a new image of a person based on a single image of that person and
the image of a pose donor. We use a dense pose estimation system that maps
pixels from both images to a common surface-based coordinate system, allowing
the two images to be brought in correspondence with each other. We inpaint and
refine the source image intensities in the surface coordinate system, prior to
warping them onto the target pose. These predictions are fused with those of a
convolutional predictive module through a neural synthesis module allowing for
training the whole pipeline jointly end-to-end, optimizing a combination of
adversarial and perceptual losses. We show that dense pose estimation is a
substantially more powerful conditioning input than landmark-, or mask-based
alternatives, and report systematic improvements over state of the art
generators on DeepFashion and MVC datasets. | ['Riza Alp Guler', 'Natalia Neverova', 'Iasonas Kokkinos'] | 2018-09-06 | dense-pose-transfer-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Natalia_Neverova_Two_Stream__ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Natalia_Neverova_Two_Stream__ECCV_2018_paper.pdf | eccv-2018-9 | ['pose-transfer'] | ['computer-vision'] | [ 5.67779958e-01 4.44848865e-01 3.81614476e-01 -4.72571433e-01
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621a2a08-966e-4fab-acbb-ecfadc63a5a6 | cross-lingual-transfer-with-maml-on-trees | null | null | https://aclanthology.org/2021.adaptnlp-1.8 | https://aclanthology.org/2021.adaptnlp-1.8.pdf | Cross-Lingual Transfer with MAML on Trees | In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks that have a hierarchical structure. Our research extends a meta-learning model, MAML, by exploiting hierarchical task relationships. Our algorithm, TreeMAML, adapts the model to each task with a few gradient steps, but the adaptation follows the hierarchical tree structure: in each step, gradients are pooled across tasks clusters and subsequent steps follow down the tree. We also implement a clustering algorithm that generates the tasks tree without previous knowledge of the task structure, allowing us to make use of implicit relationships between the tasks. We show that TreeMAML successfully trains natural language processing models for cross-lingual Natural Language Inference by taking advantage of the language phylogenetic tree. This result is useful since most languages in the world are under-resourced and the improvement on cross-lingual transfer allows the internationalization of NLP models. | ['Alberto Bernacchia', 'Da-Shan Shiu', 'Ye Tian', 'Feng-Ting Liao', 'Tim Nieradzik', 'Jamie McGowan', 'Federica Freddi', 'Jezabel Garcia'] | null | null | null | null | eacl-adaptnlp-2021-4 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.60426751e-01 1.38225913e-01 -2.81531632e-01 -6.48521185e-01
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6e4e09ef-bac6-49b2-b4eb-ac0d6e43d79d | pl-eesr-perceptual-loss-based-end-to-end | 2110.00940 | null | https://arxiv.org/abs/2110.00940v1 | https://arxiv.org/pdf/2110.00940v1.pdf | PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction | Speech enhancement aims to improve the perceptual quality of the speech signal by suppression of the background noise. However, excessive suppression may lead to speech distortion and speaker information loss, which degrades the performance of speaker embedding extraction. To alleviate this problem, we propose an end-to-end deep learning framework, dubbed PL-EESR, for robust speaker representation extraction. This framework is optimized based on the feedback of the speaker identification task and the high-level perceptual deviation between the raw speech signal and its noisy version. We conducted speaker verification tasks in both noisy and clean environment respectively to evaluate our system. Compared to the baseline, our method shows better performance in both clean and noisy environments, which means our method can not only enhance the speaker relative information but also avoid adding distortions. | ['Haizhou Li', 'Ville Hautamaki', 'Kong Aik Lee', 'Yi Ma'] | 2021-10-03 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.07168369e-01 -2.58610159e-01 4.00209844e-01 -4.84594405e-01
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bc4e55bb-7a2f-4bb9-8b50-79518a1c2c4a | data-driven-smart-ponzi-scheme-detection | 2108.09305 | null | https://arxiv.org/abs/2108.09305v1 | https://arxiv.org/pdf/2108.09305v1.pdf | Data-driven Smart Ponzi Scheme Detection | A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Experimental results show that this method is significantly better than the existing smart Ponzi scheme detection methods. | ['Feiyang Wang', 'Kai Lei', 'Weijing Wu', 'Yuzhi Liang'] | 2021-08-20 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [ 8.60468149e-02 -2.12565307e-02 -2.70379633e-01 5.55047505e-02
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e0f44449-e437-455c-8873-43f98c36d458 | particle-swarm-optimization-for-time-series | 1501.07399 | null | http://arxiv.org/abs/1501.07399v1 | http://arxiv.org/pdf/1501.07399v1.pdf | Particle swarm optimization for time series motif discovery | Efficiently finding similar segments or motifs in time series data is a
fundamental task that, due to the ubiquity of these data, is present in a wide
range of domains and situations. Because of this, countless solutions have been
devised but, to date, none of them seems to be fully satisfactory and flexible.
In this article, we propose an innovative standpoint and present a solution
coming from it: an anytime multimodal optimization algorithm for time series
motif discovery based on particle swarms. By considering data from a variety of
domains, we show that this solution is extremely competitive when compared to
the state-of-the-art, obtaining comparable motifs in considerably less time
using minimal memory. In addition, we show that it is robust to different
implementation choices and see that it offers an unprecedented degree of
flexibility with regard to the task. All these qualities make the presented
solution stand out as one of the most prominent candidates for motif discovery
in long time series streams. Besides, we believe the proposed standpoint can be
exploited in further time series analysis and mining tasks, widening the scope
of research and potentially yielding novel effective solutions. | ['Joan Serrà', 'Josep Lluis Arcos'] | 2015-01-29 | null | null | null | null | ['time-series-streams'] | ['time-series'] | [ 2.40573093e-01 -4.76485938e-01 -9.69036296e-02 9.64714810e-02
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f26db4d3-e51d-49f4-af3c-9a7fd1b10c70 | image-stitching-with-perspective-preserving | 1605.05019 | null | http://arxiv.org/abs/1605.05019v1 | http://arxiv.org/pdf/1605.05019v1.pdf | Image stitching with perspective-preserving warping | Image stitching algorithms often adopt the global transformation, such as
homography, and work well for planar scenes or parallax free camera motions.
However, these conditions are easily violated in practice. With casual camera
motions, variable taken views, large depth change, or complex structures, it is
a challenging task for stitching these images. The global transformation model
often provides dreadful stitching results, such as misalignments or projective
distortions, especially perspective distortion. To this end, we suggest a
perspective-preserving warping for image stitching, which spatially combines
local projective transformations and similarity transformation. By weighted
combination scheme, our approach gradually extrapolates the local projective
transformations of the overlapping regions into the non-overlapping regions,
and thus the final warping can smoothly change from projective to similarity.
The proposed method can provide satisfactory alignment accuracy as well as
reduce the projective distortions and maintain the multi-perspective view.
Experiments on a variety of challenging images confirm the efficiency of the
approach. | ['Tian-Zhu Xiang', 'Gui-Song Xia', 'Liangpei Zhang'] | 2016-05-17 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.27512139e-01 -5.40556371e-01 -2.73684226e-02 -2.62970813e-02
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b91d7c17-f783-472b-b1ab-e7ba9f3fd235 | restormer-efficient-transformer-for-high | 2111.09881 | null | https://arxiv.org/abs/2111.09881v2 | https://arxiv.org/pdf/2111.09881v2.pdf | Restormer: Efficient Transformer for High-Resolution Image Restoration | Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and inadaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images. In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks, including image deraining, single-image motion deblurring, defocus deblurring (single-image and dual-pixel data), and image denoising (Gaussian grayscale/color denoising, and real image denoising). The source code and pre-trained models are available at https://github.com/swz30/Restormer. | ['Ming-Hsuan Yang', 'Fahad Shahbaz Khan', 'Munawar Hayat', 'Salman Khan', 'Aditya Arora', 'Syed Waqas Zamir'] | 2021-11-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['color-image-denoising', 'single-image-deraining', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.77649218e-01 -3.42428744e-01 1.42988414e-01 -2.23927230e-01
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336319d8-0399-4c64-b245-ff4b8c51dd13 | open-category-human-object-interaction-pre | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.pdf | Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework | Human-object interaction (HOI) has long been plagued by the conflict between limited supervised data and a vast number of possible interaction combinations in real life. Current methods trained from closed-set data predict HOIs as fixed-dimension logits, which restricts their scalability to open-set categories. To address this issue, we introduce OpenCat, a language modeling framework that reformulates HOI prediction as sequence generation. By converting HOI triplets into a token sequence through a serialization scheme, our model is able to exploit the open-set vocabulary of the language modeling framework to predict novel interaction classes with a high degree of freedom. In addition, inspired by the great success of vision-language pre-training, we collect a large amount of weakly-supervised data related to HOI from image-caption pairs, and devise several auxiliary proxy tasks, including soft relational matching and human-object relation prediction, to pre-train our model. Extensive experiments show that our OpenCat significantly boosts HOI performance, particularly on a broad range of rare and unseen categories. | ['Qin Jin', 'Boshen Xu', 'Sipeng Zheng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.79403239e-01 2.00441986e-01 -2.71946549e-01 -6.25736296e-01
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9e016c52-8346-42ff-b29a-420cb50027cd | latent-shift-latent-diffusion-with-temporal | 2304.08477 | null | https://arxiv.org/abs/2304.08477v2 | https://arxiv.org/pdf/2304.08477v2.pdf | Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation | We propose Latent-Shift -- an efficient text-to-video generation method based on a pretrained text-to-image generation model that consists of an autoencoder and a U-Net diffusion model. Learning a video diffusion model in the latent space is much more efficient than in the pixel space. The latter is often limited to first generating a low-resolution video followed by a sequence of frame interpolation and super-resolution models, which makes the entire pipeline very complex and computationally expensive. To extend a U-Net from image generation to video generation, prior work proposes to add additional modules like 1D temporal convolution and/or temporal attention layers. In contrast, we propose a parameter-free temporal shift module that can leverage the spatial U-Net as is for video generation. We achieve this by shifting two portions of the feature map channels forward and backward along the temporal dimension. The shifted features of the current frame thus receive the features from the previous and the subsequent frames, enabling motion learning without additional parameters. We show that Latent-Shift achieves comparable or better results while being significantly more efficient. Moreover, Latent-Shift can generate images despite being finetuned for T2V generation. | ['Xi Yin', 'Jiebo Luo', 'Jia-Bin Huang', 'Sonal Gupta', 'Harry Yang', 'Songyang Zhang', 'Jie An'] | 2023-04-17 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 4.51467991e-01 1.43704802e-01 3.83296050e-02 -6.18389845e-02
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32c583fe-6585-4932-9fdf-b9cc1a8d7767 | blockwise-principal-component-analysis-for | 2305.06042 | null | https://arxiv.org/abs/2305.06042v1 | https://arxiv.org/pdf/2305.06042v1.pdf | Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction | Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increasing size of datasets. To address this issue, we propose a Blockwise principal component analysis Imputation (BPI) framework for dimensionality reduction and imputation of monotone missing data. The framework conducts Principal Component Analysis (PCA) on the observed part of each monotone block of the data and then imputes on merging the obtained principal components using a chosen imputation technique. BPI can work with various imputation techniques and can significantly reduce imputation time compared to conducting dimensionality reduction after imputation. This makes it a practical and efficient approach for large datasets with monotone missing data. Our experiments validate the improvement in speed. In addition, our experiments also show that while applying MICE imputation directly on missing data may not yield convergence, applying BPI with MICE for the data may lead to convergence. | ['Binh T. Nguyen', 'Pål Halvorsen', 'Michael A. Riegler', 'Steven A. Hicks', 'Thu Nguyen', 'Hoang Thien Ly', 'Mai Anh Vu', 'Tu T. Do'] | 2023-05-10 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.95790425e-01 -3.85570794e-01 -1.12718306e-01 -4.47656393e-01
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c3579d0a-5505-4e5e-b379-0871c955a5b2 | evaluation-of-deep-convolutional-generative | 2009.01181 | null | https://arxiv.org/abs/2009.01181v1 | https://arxiv.org/pdf/2009.01181v1.pdf | Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images | Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations. Training deep neural network models on such datasets to accurately classify the medical condition does not yield desired results and often over-fits the data on majority class samples. In order to address this issue, data augmentation is often performed on training data by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. These augmentation techniques are not guaranteed to be advantageous in domains with limited data, especially medical image data, and could lead to further overfitting. In this work, we performed data augmentation on the Chest X-rays dataset through generative modeling (deep convolutional generative adversarial network) which creates artificial instances retaining similar characteristics to the original data and evaluation of the model resulted in Fr\'echet Distance of Inception (FID) score of 1.289. | ['Sagar Kora Venu'] | 2020-09-02 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.90021789e-01 2.75671959e-01 -1.94271188e-02 -4.74356443e-01
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1fce4013-e227-485d-9d36-99f060fab184 | ensemble-nonlinear-model-predictive-control | 2303.10393 | null | https://arxiv.org/abs/2303.10393v1 | https://arxiv.org/pdf/2303.10393v1.pdf | Ensemble Nonlinear Model Predictive Control for Residential Solar-Battery Energy Management | In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange. Providing a schedule of this bidirectional power dispatch can facilitate the operational planning for the grid operator and bring additional benefits to the prosumers with some economic incentives. However, the major obstacle to achieving this win-win situation is the difficulty in 1) predicting the nonlinear behaviors of battery degradation under unknown operating conditions and 2) addressing the highly uncertain generation/load patterns, in a computationally viable way. This paper thus establishes a robust short-term dispatch framework for residential prosumers equipped with rooftop solar photovoltaic panels and household batteries. The objective is to achieve the minimum-cost operation under the dynamic distribution energy market environment with stipulated dispatch rules. A general nonlinear optimization problem is formulated, taking into consideration the operating costs due to electricity trading, battery degradation, and various operating constraints. The optimization problem is solved in real-time using a proposed ensemble nonlinear model predictive control-based economic dispatch strategy, where the uncertainty in the forecast has been addressed adequately albeit with limited local data. The effectiveness of the proposed algorithm has been validated using real-world prosumer datasets. | ['Changfu Zou', 'Chih Feng Lee', 'Daniel E. Quevedo', 'D. Mahinda Vilathgamuwa', 'Yang Li'] | 2023-03-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.79692411e-01 -3.15885633e-01 -1.77915189e-02 -2.67335288e-02
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203627d0-d946-43bc-86c3-226bba4c2b4f | ive-got-a-construction-looks-funny | null | null | https://aclanthology.org/2020.udw-1.16 | https://aclanthology.org/2020.udw-1.16.pdf | I’ve got a construction looks funny – representing and recovering non-standard constructions in UD | The UD framework defines guidelines for a crosslingual syntactic analysis in the framework of dependency grammar, with the aim of providing a consistent treatment across languages that not only supports multilingual NLP applications but also facilitates typological studies. Until now, the UD framework has mostly focussed on bilexical grammatical relations. In the paper, we propose to add a constructional perspective and discuss several examples of spoken-language constructions that occur in multiple languages and challenge the current use of basic and enhanced UD relations. The examples include cases where the surface relations are deceptive, and syntactic amalgams that either involve unconnected subtrees or structures with multiply-headed dependents. We argue that a unified treatment of constructions across languages will increase the consistency of the UD annotations and thus the quality of the treebanks for linguistic analysis. | ['Ines Rehbein', 'Josef Ruppenhofer'] | null | null | null | null | udw-coling-2020-12 | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.89199191e-01 1.91476464e-01 -3.11533421e-01 -5.04122257e-01
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df45156e-7512-4b4d-8cf6-35b1146fd2dc | towards-resolving-the-challenge-of-long-tail | 2011.03822 | null | https://arxiv.org/abs/2011.03822v1 | https://arxiv.org/pdf/2011.03822v1.pdf | Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection | Existing methods for object detection in UAV images ignored an important challenge - imbalanced class distribution in UAV images - which leads to poor performance on tail classes. We systematically investigate existing solutions to long-tail problems and unveil that re-balancing methods that are effective on natural image datasets cannot be trivially applied to UAV datasets. To this end, we rethink long-tailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images. The key components in DSHNet include Class-Biased Samplers (CBS) and Bilateral Box Heads (BBH), which are developed to cope with tail classes and head classes in a dual-path manner. Without bells and whistles, DSHNet significantly boosts the performance of tail classes on different detection frameworks. Moreover, DSHNet significantly outperforms base detectors and generic approaches for long-tail problems on VisDrone and UAVDT datasets. It achieves new state-of-the-art performance when combining with image cropping methods. Code is available at https://github.com/we1pingyu/DSHNet | ['Chen Chen', 'Taojiannan Yang', 'Weiping Yu'] | 2020-11-07 | null | null | null | null | ['head-detection', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 1.52784614e-02 -4.76819992e-01 -1.76289797e-01 -3.96604240e-02
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372af700-189c-4f63-b904-0b3085a3578a | graph-neural-network-for-fraud-detection-via | null | null | https://ieeexplore.ieee.org/abstract/document/9204584/ | https://ieeexplore.ieee.org/abstract/document/9204584/ | Graph Neural Network for Fraud Detection via Spatial-Temporal Attention | Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. 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 fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterward, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-world card transaction dataset. The result shows that STAGN 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 detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncovering fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system. | ['Liqing Zhang', 'Ying Zhang', 'Xiaoyang Wang', 'Dawei Cheng'] | 2020-09-23 | null | null | null | tkde-2020-9 | ['fraud-detection'] | ['miscellaneous'] | [-3.95065248e-01 -5.85911393e-01 5.89968190e-02 -3.89973134e-01
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e6044117-7455-4390-abe4-1953f92298fd | short-term-memory-convolutions | 2302.04331 | null | https://arxiv.org/abs/2302.04331v1 | https://arxiv.org/pdf/2302.04331v1.pdf | Short-Term Memory Convolutions | The real-time processing of time series signals is a critical issue for many real-life applications. The idea of real-time processing is especially important in audio domain as the human perception of sound is sensitive to any kind of disturbance in perceived signals, especially the lag between auditory and visual modalities. The rise of deep learning (DL) models complicated the landscape of signal processing. Although they often have superior quality compared to standard DSP methods, this advantage is diminished by higher latency. In this work we propose novel method for minimization of inference time latency and memory consumption, called Short-Term Memory Convolution (STMC) and its transposed counterpart. The main advantage of STMC is the low latency comparable to long short-term memory (LSTM) networks. Furthermore, the training of STMC-based models is faster and more stable as the method is based solely on convolutional neural networks (CNNs). In this study we demonstrate an application of this solution to a U-Net model for a speech separation task and GhostNet model in acoustic scene classification (ASC) task. In case of speech separation we achieved a 5-fold reduction in inference time and a 2-fold reduction in latency without affecting the output quality. The inference time for ASC task was up to 4 times faster while preserving the original accuracy. | ['Artur Szumaczuk', 'Bartłomiej Jasik', 'Paweł Daniluk', 'Krzysztof Arendt', 'Grzegorz Stefański'] | 2023-02-08 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification', 'speech-separation'] | ['audio', 'computer-vision', 'speech'] | [ 3.62319261e-01 -2.52480060e-01 6.92514718e-01 -2.34443277e-01
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eef98170-471c-451c-ac18-d5378aa799c8 | decision-making-under-uncertainty-an | 1911.00946 | null | https://arxiv.org/abs/1911.00946v3 | https://arxiv.org/pdf/1911.00946v3.pdf | Decision Making under Uncertainty: An Experimental Study in Market Settings | We implement nonparametric revealed-preference tests of subjective expected utility theory and its generalizations. We find that a majority of subjects' choices are consistent with the maximization of some utility function. They respond to price changes in the direction subjective expected utility theory predicts, but not to a degree that makes them consistent with the theory. Maxmin expected utility a dds no explanatory power. The degree of deviations from the theory is uncorrelated with demographic characteristics. Our findings are essentially the same in laboratory data with a student population and in a panel survey with a general sample of the U.S. population. | ['Kota Saito', 'Taisuke Imai', 'Federico Echenique'] | 2019-11-03 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-3.60448599e-01 1.10896744e-01 -7.67855227e-01 -7.66889155e-01
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7b718387-1978-48c9-b2d2-2dd9fc030dfc | a-deep-multiscale-framework-for-video | null | null | https://ieeexplore.ieee.org/abstract/document/10086041 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10086041 | A Deep Multiscale Framework for Video Watermarking | Video watermarking embeds a message into a cover
video in an imperceptible manner, which can be retrieved
even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus
cannot simultaneously handle a broad spectrum of distortions. To this end, we propose a robust deep learning-based
solution for video watermarking that is end-to-end trainable. Our model consists of a novel multiscale design where
the watermarks are distributed across multiple spatialtemporal scales. It gains robustness against various distortions through a differentiable distortion layer, whereas
non-differentiable distortions, such as popular video compression standards, are modeled by a differentiable proxy.
Extensive evaluations on a wide variety of distortions show
that our method outperforms traditional video watermarking methods as well as deep image watermarking models by
a large margin. We further demonstrate the practicality of
our method on a realistic video-editing application. | ['and Feng Yang', 'Peyman Milanfar1', 'Ce Liu1', 'Huiwen Chang1', 'Yinxiao Li1', 'Xiyang Luo1'] | 2023-03-28 | null | null | null | ieee-transactions-on-image-processing-2023-3 | ['video-editing'] | ['computer-vision'] | [ 4.63501573e-01 -3.70755285e-01 -5.40536404e-01 2.90844589e-01
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0f6e1e0e-0015-402c-8667-c67c7eb773a7 | single-stage-instance-shadow-detection-with | null | null | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | Single-Stage Instance Shadow Detection with Bidirectional Relation Learning | Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to form the final shadow-object pairs. In this paper, we present a new single-stage fully-convolutional network architecture with a bidirectional relation learning module to directly learn the relations of shadow and object instances in an end-to-end manner. Compared with the prior work, our method actively explores the internal relationship between shadows and objects to learn a better pairing between them, thus improving the overall performance for instance shadow detection. We evaluate our method on the benchmark dataset for instance shadow detection, both quantitatively and visually. The experimental results demonstrate that our method clearly outperforms the state-of-the-art method. | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Tianyu Wang'] | 2021-06-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['shadow-detection'] | ['computer-vision'] | [ 4.13534760e-01 4.01160896e-01 -5.69225550e-02 -9.33037877e-01
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c85122b2-eecf-4606-9572-a470d7087baa | a-survey-of-loss-functions-for-semantic | 2006.14822 | null | https://arxiv.org/abs/2006.14822v4 | https://arxiv.org/pdf/2006.14822v4.pdf | A survey of loss functions for semantic segmentation | Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc. In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model. Furthermore, we have also introduced a new log-cosh dice loss function and compared its performance on the NBFS skull-segmentation open-source data-set with widely used loss functions. We also showcased that certain loss functions perform well across all data-sets and can be taken as a good baseline choice in unknown data distribution scenarios. Our code is available at Github: https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions. | ['Shruti Jadon'] | 2020-06-26 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-1.99909374e-01 2.38796119e-02 -2.55416274e-01 -7.06335664e-01
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4f78c002-075a-45e4-9c17-464d3eb1285e | data-augmentation-for-cross-domain-named | 2109.01758 | null | https://arxiv.org/abs/2109.01758v1 | https://arxiv.org/pdf/2109.01758v1.pdf | Data Augmentation for Cross-Domain Named Entity Recognition | Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In contrast, we study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains. | ['Thamar Solorio', 'Leonardo Neves', 'Gustavo Aguilar', 'Shuguang Chen'] | 2021-09-04 | null | https://aclanthology.org/2021.emnlp-main.434 | https://aclanthology.org/2021.emnlp-main.434.pdf | emnlp-2021-11 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 4.07312751e-01 9.62709561e-02 -1.62577420e-01 -7.53246725e-01
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eef447b3-1bd0-40be-8de9-b4869ec27a2c | unsupervised-object-segmentation-in-video-by | 1704.05674 | null | http://arxiv.org/abs/1704.05674v1 | http://arxiv.org/pdf/1704.05674v1.pdf | Unsupervised object segmentation in video by efficient selection of highly probable positive features | We address an essential problem in computer vision, that of unsupervised
object segmentation in video, where a main object of interest in a video
sequence should be automatically separated from its background. An efficient
solution to this task would enable large-scale video interpretation at a high
semantic level in the absence of the costly manually labeled ground truth. We
propose an efficient unsupervised method for generating foreground object
soft-segmentation masks based on automatic selection and learning from highly
probable positive features. We show that such features can be selected
efficiently by taking into consideration the spatio-temporal, appearance and
motion consistency of the object during the whole observed sequence. We also
emphasize the role of the contrasting properties between the foreground object
and its background. Our model is created in two stages: we start from pixel
level analysis, on top of which we add a regression model trained on a
descriptor that considers information over groups of pixels and is both
discriminative and invariant to many changes that the object undergoes
throughout the video. We also present theoretical properties of our
unsupervised learning method, that under some mild constraints is guaranteed to
learn a correct discriminative classifier even in the unsupervised case. Our
method achieves competitive and even state of the art results on the
challenging Youtube-Objects and SegTrack datasets, while being at least one
order of magnitude faster than the competition. We believe that the competitive
performance of our method in practice, along with its theoretical properties,
constitute an important step towards solving unsupervised discovery in video. | ['Marius Leordeanu', 'Emanuela Haller'] | 2017-04-19 | unsupervised-object-segmentation-in-video-by-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Haller_Unsupervised_Object_Segmentation_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Haller_Unsupervised_Object_Segmentation_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 6.07826769e-01 -5.77259734e-02 -1.16508588e-01 -3.26165140e-01
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5e4434e3-1702-4305-adb3-d8b60eb33e40 | image-restoration-using-joint-statistical | 1405.3173 | null | http://arxiv.org/abs/1405.3173v1 | http://arxiv.org/pdf/1405.3173v1.pdf | Image Restoration Using Joint Statistical Modeling in Space-Transform Domain | This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm. | ['Siwei Ma', 'Debin Zhao', 'Wen Gao', 'Ruiqin Xiong', 'Jian Zhang'] | 2014-05-11 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 4.14391786e-01 -5.64788520e-01 4.31192890e-02 -2.57556111e-01
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1a1d7e8f-cbd2-4b76-a2f1-6fd81e235565 | remote-sensing-image-change-detection-with | 2307.02007 | null | https://arxiv.org/abs/2307.02007v1 | https://arxiv.org/pdf/2307.02007v1.pdf | Remote Sensing Image Change Detection with Graph Interaction | Modern remote sensing image change detection has witnessed substantial advancements by harnessing the potent feature extraction capabilities of CNNs and Transforms.Yet,prevailing change detection techniques consistently prioritize extracting semantic features related to significant alterations,overlooking the viability of directly interacting with bitemporal image features.In this letter,we propose a bitemporal image graph Interaction network for remote sensing change detection,namely BGINet-CD. More specifically,by leveraging the concept of non-local operations and mapping the features obtained from the backbone network to the graph structure space,we propose a unified self-focus mechanism for bitemporal images.This approach enhances the information coupling between the two temporal images while effectively suppressing task-irrelevant interference,Based on a streamlined backbone architecture,namely ResNet18,our model demonstrates superior performance compared to other state-of-the-art methods (SOTA) on the GZ CD dataset. Moreover,the model exhibits an enhanced trade-off between accuracy and computational efficiency,further improving its overall effectiveness | ['Chenglong Liu'] | 2023-07-05 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.91601968e-01 -2.20484853e-01 1.46726936e-01 -2.00990379e-01
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0c2584ab-fb25-4f8a-9df9-9277a06c3be0 | spectral-feature-scaling-method-for | 1805.07006 | null | http://arxiv.org/abs/1805.07006v1 | http://arxiv.org/pdf/1805.07006v1.pdf | Spectral feature scaling method for supervised dimensionality reduction | Spectral dimensionality reduction methods enable linear separations of
complex data with high-dimensional features in a reduced space. However, these
methods do not always give the desired results due to irregularities or
uncertainties of the data. Thus, we consider aggressively modifying the scales
of the features to obtain the desired classification. Using prior knowledge on
the labels of partial samples to specify the Fiedler vector, we formulate an
eigenvalue problem of a linear matrix pencil whose eigenvector has the feature
scaling factors. The resulting factors can modify the features of entire
samples to form clusters in the reduced space, according to the known labels.
In this study, we propose new dimensionality reduction methods supervised using
the feature scaling associated with the spectral clustering. Numerical
experiments show that the proposed methods outperform well-established
supervised methods for toy problems with more samples than features, and are
more robust regarding clustering than existing methods. Also, the proposed
methods outperform existing methods regarding classification for real-world
problems with more features than samples of gene expression profiles of cancer
diseases. Furthermore, the feature scaling tends to improve the clustering and
classification accuracies of existing unsupervised methods, as the proportion
of training data increases. | ['Tetsuya Sakurai', 'Momo Matsuda', 'Keiichi Morikuni'] | 2018-05-18 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.69521475e-01 -1.39908165e-01 -1.98530734e-01 -3.43278915e-01
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535da6f8-0320-459f-b8fc-65642582d9ac | attanet-attention-augmented-network-for-fast | 2103.05930 | null | https://arxiv.org/abs/2103.05930v1 | https://arxiv.org/pdf/2103.05930v1.pdf | AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing | Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics while keeping the efficiency high. AttaNet consists of two primary modules: Strip Attention Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging images with low segmentation accuracy, there are a significantly larger amount of vertical strip areas than horizontal ones, SAM utilizes a striping operation to reduce the complexity of encoding global context in the vertical direction drastically while keeping most of contextual information, compared to the non-local approaches. Moreover, AFM follows a cross-level aggregation strategy to limit the computation, and adopts an attention strategy to weight the importance of different levels of features at each pixel when fusing them, obtaining an efficient multi-level representation. We have conducted extensive experiments on two semantic segmentation benchmarks, and our network achieves different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9% mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on ADE20K as well. | ['Rui Huang', 'Kangfu Mei', 'Qi Song'] | 2021-03-10 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 3.65678251e-01 -3.43157426e-02 7.06862332e-03 -3.51455301e-01
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d217ca04-61f4-43bd-8338-d61fa7b9d1ab | diffusionseg-adapting-diffusion-towards | 2303.09813 | null | https://arxiv.org/abs/2303.09813v1 | https://arxiv.org/pdf/2303.09813v1.pdf | DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery | Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level visual knowledge and high-level semantic relations. In this paper, we propose to exploit such knowledgeable diffusion models for mainstream discriminative tasks, i.e., unsupervised object discovery: saliency segmentation and object localization. However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use. Besides, the lack of explicitly labeled data significantly limits performance in unsupervised settings. To tackle these issues, we introduce DiffusionSeg, one novel synthesis-exploitation framework containing two-stage strategies. To alleviate data insufficiency, we synthesize abundant images, and propose a novel training-free AttentionCut to obtain masks in the first synthesis stage. In the second exploitation stage, to bridge the structural gap, we use the inversion technique, to map the given image back to diffusion features. These features can be directly used by downstream architectures. Extensive experiments and ablation studies demonstrate the superiority of adapting diffusion for unsupervised object discovery. | ['Yanfeng Wang', 'Ya zhang', 'Yu Wang', 'Jinxiang Liu', 'Fei Zhang', 'Chen Ju', 'Yuhuan Yang', 'Chaofan Ma'] | 2023-03-17 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 5.41516066e-01 2.33263075e-01 -3.65439594e-01 -3.98974299e-01
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46f36ec6-4e63-4d2d-b75b-1c2c3909d6e8 | on-unifying-multi-view-self-representations | 1610.07126 | null | http://arxiv.org/abs/1610.07126v3 | http://arxiv.org/pdf/1610.07126v3.pdf | On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization | In this paper, we address the multi-view subspace clustering problem. Our
method utilizes the circulant algebra for tensor, which is constructed by
stacking the subspace representation matrices of different views and then
rotating, to capture the low rank tensor subspace so that the refinement of the
view-specific subspaces can be achieved, as well as the high order correlations
underlying multi-view data can be explored.} By introducing a recently proposed
tensor factorization, namely tensor-Singular Value Decomposition (t-SVD)
\cite{kilmer13}, we can impose a new type of low-rank tensor constraint on the
rotated tensor to capture the complementary information from multiple views.
Different from traditional unfolding based tensor norm, this low-rank tensor
constraint has optimality properties similar to that of matrix rank derived
from SVD, so the complementary information among views can be explored more
efficiently and thoroughly. The established model, called t-SVD based
Multi-view Subspace Clustering (t-SVD-MSC), falls into the applicable scope of
augmented Lagrangian method, and its minimization problem can be efficiently
solved with theoretical convergence guarantee and relatively low computational
complexity. Extensive experimental testing on eight challenging image dataset
shows that the proposed method has achieved highly competent objective
performance compared to several state-of-the-art multi-view clustering methods. | ['Yanyun Qu', 'DaCheng Tao', 'Lei Zhang', 'Yuan Xie', 'Yan Liu', 'Wensheng Zhang'] | 2016-10-23 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.61566007e-01 -6.32817328e-01 -9.98321325e-02 1.08845115e-01
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d45b089b-5193-4742-ac16-b9e5656fa09f | multi-level-gated-recurrent-neural-network-1 | 1910.01822 | null | https://arxiv.org/abs/1910.01822v1 | https://arxiv.org/pdf/1910.01822v1.pdf | Multi-level Gated Recurrent Neural Network for Dialog Act Classification | In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals with it by applying classifiers with rich features. Most of the current neural network models still omit the sequential information in the conversation. Henceforth, we apply a novel multi-level gated recurrent neural network (GRNN) with non-textual information to predict the DA tag. Our model not only utilizes textual information, but also makes use of non-textual and contextual information. In comparison, our model has shown significant improvement over previous works on Switchboard Dialog Act (SWDA) task by over 6%. | ['Yunfang Wu', 'Wei Li'] | 2019-10-04 | multi-level-gated-recurrent-neural-network | https://aclanthology.org/C16-1185 | https://aclanthology.org/C16-1185.pdf | coling-2016-12 | ['dialog-act-classification'] | ['natural-language-processing'] | [ 4.20682341e-01 3.01468790e-01 1.04157984e-01 -6.88073397e-01
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2bc496f2-3df8-4dc4-af72-6a9d097fbffc | content-authentication-for-neural-imaging | 1812.01516 | null | http://arxiv.org/abs/1812.01516v2 | http://arxiv.org/pdf/1812.01516v2.pdf | Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels | Forensic analysis of digital photo provenance relies on intrinsic traces left
in the photograph at the time of its acquisition. Such analysis becomes
unreliable after heavy post-processing, such as down-sampling and
re-compression applied upon distribution in the Web. This paper explores
end-to-end optimization of the entire image acquisition and distribution
workflow to facilitate reliable forensic analysis at the end of the
distribution channel. We demonstrate that neural imaging pipelines can be
trained to replace the internals of digital cameras, and jointly optimized for
high-fidelity photo development and reliable provenance analysis. In our
experiments, the proposed approach increased image manipulation detection
accuracy from 45% to over 90%. The findings encourage further research towards
building more reliable imaging pipelines with explicit provenance-guaranteeing
properties. | ['Pawel Korus', 'Nasir Memon'] | 2018-12-04 | content-authentication-for-neural-imaging-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Korus_Content_Authentication_for_Neural_Imaging_Pipelines_End-To-End_Optimization_of_Photo_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Korus_Content_Authentication_for_Neural_Imaging_Pipelines_End-To-End_Optimization_of_Photo_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-manipulation-detection'] | ['computer-vision'] | [ 3.99144083e-01 -4.73183393e-02 1.35354236e-01 -3.93144518e-01
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17a12218-f0aa-47d7-8895-a239f1eb8c74 | an-audio-visual-attention-based-multimodal | 2203.05178 | null | https://arxiv.org/abs/2203.05178v1 | https://arxiv.org/pdf/2203.05178v1.pdf | An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection | DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity is hard to be discriminated in such fake talking face videos. Together with the lack of attention on audio stream as the prior knowledge, the detection failure of fake talking face generation also becomes inevitable. Inspired by the decision-making mechanism of human multisensory perception system, which enables the auditory information to enhance post-sensory visual evidence for informed decisions output, in this study, a fake talking face detection framework FTFDNet is proposed by incorporating audio and visual representation to achieve more accurate fake talking face videos detection. Furthermore, an audio-visual attention mechanism (AVAM) is proposed to discover more informative features, which can be seamlessly integrated into any audio-visual CNN architectures by modularization. With the additional AVAM, the proposed FTFDNet is able to achieve a better detection performance on the established dataset (FTFDD). The evaluation of the proposed work has shown an excellent performance on the detection of fake talking face videos, which is able to arrive at a detection rate above 97%. | ['Yanning Zhang', 'Yufei zha', 'Wei Huang', 'Lei Xie', 'Peng Zhang', 'Ganglai Wang'] | 2022-03-10 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.40171829e-02 8.10625181e-02 1.29819706e-01 1.11808181e-02
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4e15ca2e-f275-43d1-bcdd-1b5ad51a9c86 | codeattack-code-based-adversarial-attacks-for | 2206.00052 | null | https://arxiv.org/abs/2206.00052v3 | https://arxiv.org/pdf/2206.00052v3.pdf | CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models | Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., they are primarily concerned with the human understanding of the code. They are not robust to changes in the input and thus, are potentially susceptible to adversarial attacks in the natural channel. We propose, CodeAttack, a simple yet effective black-box attack model that uses code structure to generate effective, efficient, and imperceptible adversarial code samples and demonstrates the vulnerabilities of the state-of-the-art PL models to code-specific adversarial attacks. We evaluate the transferability of CodeAttack on several code-code (translation and repair) and code-NL (summarization) tasks across different programming languages. CodeAttack outperforms state-of-the-art adversarial NLP attack models to achieve the best overall drop in performance while being more efficient, imperceptible, consistent, and fluent. The code can be found at https://github.com/reddy-lab-code-research/CodeAttack. | ['Chandan K. Reddy', 'Akshita Jha'] | 2022-05-31 | null | null | null | null | ['code-translation'] | ['computer-code'] | [-1.72960646e-02 2.51499504e-01 -1.11621618e-01 8.41332823e-02
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a6ebcb0f-6ef6-4b6f-b3b6-1a8bc970b95e | holistic-instance-level-human-parsing | 1709.03612 | null | http://arxiv.org/abs/1709.03612v1 | http://arxiv.org/pdf/1709.03612v1.pdf | Holistic, Instance-Level Human Parsing | Object parsing -- the task of decomposing an object into its semantic parts
-- has traditionally been formulated as a category-level segmentation problem.
Consequently, when there are multiple objects in an image, current methods
cannot count the number of objects in the scene, nor can they determine which
part belongs to which object. We address this problem by segmenting the parts
of objects at an instance-level, such that each pixel in the image is assigned
a part label, as well as the identity of the object it belongs to. Moreover, we
show how this approach benefits us in obtaining segmentations at coarser
granularities as well. Our proposed network is trained end-to-end given
detections, and begins with a category-level segmentation module. Thereafter, a
differentiable Conditional Random Field, defined over a variable number of
instances for every input image, reasons about the identity of each part by
associating it with a human detection. In contrast to other approaches, our
method can handle the varying number of people in each image and our holistic
network produces state-of-the-art results in instance-level part and human
segmentation, together with competitive results in category-level part
segmentation, all achieved by a single forward-pass through our neural network. | ['Philip H. S. Torr', 'Anurag Arnab', 'Qizhu Li'] | 2017-09-11 | null | null | null | null | ['multi-human-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 5.97210288e-01 4.08773363e-01 -1.36401486e-02 -3.29286605e-01
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7.66218364e-01 1.07862544e+00 4.62679178e-01 1.14571676e-01
4.78617281e-01 -2.32676137e-02 4.91852909e-01 -1.02752948e+00
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3.55570555e-01 -4.17225748e-01 -3.36289674e-01 3.74814391e-01] | [9.431353569030762, 0.4975510835647583] |
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