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4b5e6486-78fc-47d6-8080-3c52e5df9da5 | learning-where-to-fixate-on-foveated-images | 1811.06868 | null | https://arxiv.org/abs/1811.06868v2 | https://arxiv.org/pdf/1811.06868v2.pdf | Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations | We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communication bits to conserve power, and the cloud upon receiving the edge inputs returns a classification label. To deal with fine-grained classification, we adopt the perspective of sequential fixation with a foveated field-of-view to model cloud-edge interactions. We propose a novel deep reinforcement learning-based foveation model, DRIFT, that sequentially generates and recognizes mixed-acuity images.Training of DRIFT requires only image-level category labels and encourages fixations to contain task-relevant information, while maintaining data efficiency. Specifically, wetrain a foveation actor network with a novel Deep Deterministic Policy Gradient by Conditioned Critic and Coaching (DDPGC3) algorithm. In addition, we propose to shape the reward to provide informative feedback after each fixation to better guide RL training. We demonstrate the effectiveness of DRIFT on this task by evaluating on five fine-grained classification benchmark datasets, and show that the proposed approach achieves state-of-the-art performance with over 3X reduction in transmitted pixels. | ['Venkatesh Saligrama', 'Stan Sclaroff', 'Hanxiao Wang', 'Vitaly Ablavsky'] | 2018-11-16 | cost-aware-fine-grained-recognition-for-iots | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Cost-Aware_Fine-Grained_Recognition_for_IoTs_Based_on_Sequential_Fixations_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Cost-Aware_Fine-Grained_Recognition_for_IoTs_Based_on_Sequential_Fixations_ICCV_2019_paper.pdf | iccv-2019-10 | ['foveation'] | ['computer-vision'] | [ 3.18050563e-01 4.92364615e-02 -2.99606800e-01 -1.52334601e-01
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a86be8e6-6afe-4cd4-9ca7-5822d61b04db | dual-projection-generative-adversarial | 2108.09016 | null | https://arxiv.org/abs/2108.09016v2 | https://arxiv.org/pdf/2108.09016v2.pdf | Dual Projection Generative Adversarial Networks for Conditional Image Generation | Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been established as powerful generative models capable of generating high-fidelity imagery. A challenge of training such a model lies in properly infusing class information into its generator and discriminator. For the discriminator, class conditioning can be achieved by either (1) directly incorporating labels as input or (2) involving labels in an auxiliary classification loss. In this paper, we show that the former directly aligns the class-conditioned fake-and-real data distributions $P(\text{image}|\text{class})$ ({\em data matching}), while the latter aligns data-conditioned class distributions $P(\text{class}|\text{image})$ ({\em label matching}). Although class separability does not directly translate to sample quality and becomes a burden if classification itself is intrinsically difficult, the discriminator cannot provide useful guidance for the generator if features of distinct classes are mapped to the same point and thus become inseparable. Motivated by this intuition, we propose a Dual Projection GAN (P2GAN) model that learns to balance between {\em data matching} and {\em label matching}. We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence. Experiments on a synthetic Mixture of Gaussian (MoG) dataset and a variety of real-world datasets including CIFAR100, ImageNet, and VGGFace2 demonstrate the efficacy of our proposed models. | ['Dimitris Metaxas', 'Asim Kadav', 'Ruijiang Gao', 'Yu Tian', 'Anastasis Stathopoulos', 'Martin Renqiang Min', 'Ligong Han'] | 2021-08-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Han_Dual_Projection_Generative_Adversarial_Networks_for_Conditional_Image_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Han_Dual_Projection_Generative_Adversarial_Networks_for_Conditional_Image_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 7.19596267e-01 3.82513613e-01 -6.23224750e-02 -4.22079474e-01
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8dc30177-95ce-4999-8d1f-a5446f207e40 | contextual-object-detection-with-multimodal | 2305.18279 | null | https://arxiv.org/abs/2305.18279v1 | https://arxiv.org/pdf/2305.18279v1.pdf | Contextual Object Detection with Multimodal Large Language Models | Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection -- understanding visible objects within different human-AI interactive contexts. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction. Our ContextDET involves three key submodels: (i) a visual encoder for extracting visual representations, (ii) a pre-trained LLM for multimodal context decoding, and (iii) a visual decoder for predicting bounding boxes given contextual object words. The new generate-then-detect framework enables us to detect object words within human vocabulary. Extensive experiments show the advantages of ContextDET on our proposed CODE benchmark, open-vocabulary detection, and referring image segmentation. Github: https://github.com/yuhangzang/ContextDET. | ['Chen Change Loy', 'Kaiyang Zhou', 'Jun Han', 'Wei Li', 'Yuhang Zang'] | 2023-05-29 | null | null | null | null | ['image-captioning', 'cloze-test'] | ['computer-vision', 'natural-language-processing'] | [ 3.11700732e-01 -1.52268503e-02 -4.50848043e-02 -3.23284686e-01
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3c1384c8-53d9-4ef7-b716-beb9f732835f | fairness-aware-counterfactuals-for-subgroups | 2306.14978 | null | https://arxiv.org/abs/2306.14978v1 | https://arxiv.org/pdf/2306.14978v1.pdf | Fairness Aware Counterfactuals for Subgroups | In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refined notions of subgroup fairness. We aim to (a) formulate different aspects of the difficulty of individuals in certain subgroups to achieve recourse, i.e. receive the desired outcome, either at the micro level, considering members of the subgroup individually, or at the macro level, considering the subgroup as a whole, and (b) introduce notions of subgroup fairness that are robust, if not totally oblivious, to the cost of achieving recourse. We accompany these notions with an efficient, model-agnostic, highly parameterizable, and explainable framework for evaluating subgroup fairness. We demonstrate the advantages, the wide applicability, and the efficiency of our approach through a thorough experimental evaluation of different benchmark datasets. | ['Ioannis Emiris', 'Dimitris Fotakis', 'Dimitrios Rontogiannis', 'Nikolaos Theologitis', 'Eleni Psaroudaki', 'Dimitris Sacharidis', 'Giorgos Giannopoulos', 'Konstantinos Tsopelas', 'Loukas Kavouras'] | 2023-06-26 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.22489047e-02 4.57963228e-01 -5.41732609e-01 -5.59064627e-01
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b3413201-9ee2-4c88-8268-e3f1bc393bbb | simple-baseline-for-weather-forecasting-using | 2212.02952 | null | https://arxiv.org/abs/2212.02952v2 | https://arxiv.org/pdf/2212.02952v2.pdf | Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network | Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using CNN, RNN, and Transformer to training strategies using weather observations datasets with auxiliary inputs. All of this progress has contributed to the field of weather forecasting; however, many elements and complex structures of deep learning models prevent us from reaching physical interpretations. This paper proposes a SImple baseline with a spatiotemporal context Aggregation Network (SIANet) that achieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple but efficient structure uses only satellite images and CNNs in an end-to-end fashion without using a multi-model ensemble or fine-tuning. This simplicity of SIANet can be used as a solid baseline that can be easily applied in weather forecasting using deep learning. | ['Yeji Choi', 'Sewoong Ahn', 'Eunbin Kim', 'Seungheon Shin', 'Doyi Kim', 'Minseok Seo'] | 2022-12-06 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.47984731e-01 -3.87014180e-01 1.43975452e-01 -7.42594719e-01
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9d161216-8c1b-45b9-8cdb-429f49f61d2f | multi-frame-quality-enhancement-for | 1803.04680 | null | http://arxiv.org/abs/1803.04680v4 | http://arxiv.org/pdf/1803.04680v4.pdf | Multi-Frame Quality Enhancement for Compressed Video | The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. In this paper, we investigate that heavy quality
fluctuation exists across compressed video frames, and thus low quality frames
can be enhanced using the neighboring high quality frames, seen as Multi-Frame
Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach
for compressed video, as a first attempt in this direction. In our approach, we
firstly develop a Support Vector Machine (SVM) based detector to locate Peak
Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame
Convolutional Neural Network (MF-CNN) is designed to enhance the quality of
compressed video, in which the non-PQF and its nearest two PQFs are as the
input. The MF-CNN compensates motion between the non-PQF and PQFs through the
Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement
subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help
of its nearest PQFs. Finally, the experiments validate the effectiveness and
generality of our MFQE approach in advancing the state-of-the-art quality
enhancement of compressed video. The code of our MFQE approach is available at
https://github.com/ryangBUAA/MFQE.git | ['Mai Xu', 'Zulin Wang', 'Ren Yang', 'Tianyi Li'] | 2018-03-13 | multi-frame-quality-enhancement-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Multi-Frame_Quality_Enhancement_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Multi-Frame_Quality_Enhancement_CVPR_2018_paper.pdf | cvpr-2018-6 | ['video-enhancement'] | ['computer-vision'] | [ 2.37314209e-01 -2.73462355e-01 -1.77833959e-01 -2.57504522e-03
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a41f351a-0f21-4f49-a9bb-41f87467036e | few-shot-conversational-dense-retrieval | 2105.04166 | null | https://arxiv.org/abs/2105.04166v3 | https://arxiv.org/pdf/2105.04166v3.pdf | Few-Shot Conversational Dense Retrieval | Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is challenging due to DR models' extra needs for supervision signals and the long-tail nature of conversational search. In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products. In addition, we grant ConvDR few-shot ability using a teacher-student framework, where we employ an ad hoc dense retriever as the teacher, inherit its document encodings, and learn a student query encoder to mimic the teacher embeddings on oracle reformulated queries. Our experiments on TREC CAsT and OR-QuAC demonstrate ConvDR's effectiveness in both few-shot and fully-supervised settings. It outperforms previous systems that operate in the sparse word space, matches the retrieval accuracy of oracle query reformulations, and is also more efficient thanks to its simplicity. Our analyses reveal that the advantages of ConvDR come from its ability to capture informative context while ignoring the unrelated context in previous conversation rounds. This makes ConvDR more effective as conversations evolve while previous systems may get confused by the increased noise from previous turns. Our code is publicly available at https://github.com/thunlp/ConvDR. | ['Zhiyuan Liu', 'Tao Feng', 'Chenyan Xiong', 'Zhenghao Liu', 'Shi Yu'] | 2021-05-10 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.10855661e-01 -2.15271395e-02 -3.74224722e-01 -5.17361462e-01
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bca0e5d2-a578-448a-b605-f2c7ab3209ec | spectrally-consistent-unet-for-high-fidelity | 2004.10696 | null | https://arxiv.org/abs/2004.10696v2 | https://arxiv.org/pdf/2004.10696v2.pdf | Spectrally Consistent UNet for High Fidelity Image Transformations | Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks due to their high learning capacity as well as their architectural qualities. The ubiquitous UNet architecture provides an efficient and multi-scale solution that combines local and global information. Despite the success of UNet architectures, the use of upsampling layers can cause artefacts. In this work, a method for assessing the structural biases of UNets and the effects these have on the outputs is presented, characterising their impact in the Fourier domain. A new upsampling module is proposed, based on a novel use of the Guided Image Filter, that provides spectrally consistent outputs when used in a UNet architecture, forming the Guided UNet (GUNet). The GUNet architecture is applied and evaluated for example applications of inverse tone mapping/dynamic range expansion and colourisation from grey-scale images and is shown to provide higher fidelity outputs. | ['Thomas Bashford-Rogers', 'Demetris Marnerides', 'Kurt Debattista'] | 2020-04-22 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.95191443e-01 -2.25784518e-02 3.81691992e-01 -1.55905083e-01
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e3901297-c760-4cf4-ae0d-6b10e5828701 | mmformer-multimodal-transformer-using | 2303.13101 | null | https://arxiv.org/abs/2303.13101v1 | https://arxiv.org/pdf/2303.13101v1.pdf | MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification | To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR). Compared with traditional Vision Transformer (ViT) lacking inductive biases of convolutions, we first introduce convolutional layers to our MMFormer to tokenize patches from multimodal data of HSI and LiDAR. Then we propose a Multi-scale Multi-head Self-Attention (MSMHSA) module to address the problem of compatibility which often limits to fuse HSI with high spectral resolution and LiDAR with relatively low spatial resolution. The proposed MSMHSA module can incorporate HSI to LiDAR data in a coarse-to-fine manner enabling us to learn a fine-grained representation. Ex- tensive experiments on widely used benchmarks (e.g., Trento and MUUFL) demonstrate the effectiveness and superiority of our proposed MMFormer for RS image classification. | ['Kaixing Zhao', 'Liang He', 'Yaqian Liu', 'Wei Feng', 'Zuheng Ming', 'Bo Zhang'] | 2023-03-23 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.68518758e-01 -4.91673768e-01 7.37428069e-02 -5.48199296e-01
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f06ebd3d-0832-4701-aff5-7839551278d8 | multi-task-head-pose-estimation-in-the-wild-1 | 2202.02299 | null | https://arxiv.org/abs/2202.02299v1 | https://arxiv.org/pdf/2202.02299v1.pdf | Multi-task head pose estimation in-the-wild | We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-decoder CNN with residual blocks and lateral skip connections. We show that the combination of head pose estimation and landmark-based face alignment significantly improve the performance of the former task. Further, the location of the pose task at the bottleneck layer, at the end of the encoder, and that of tasks depending on spatial information, such as visibility and alignment, in the final decoder layer, also contribute to increase the final performance. In the experiments conducted the proposed model outperforms the state-of-the-art in the face pose and visibility tasks. By including a final landmark regression step it also produces face alignment results on par with the state-of-the-art. | ['Luis Baumela', 'José Miguel Buenaposada', 'Roberto Valle'] | 2022-02-04 | multi-task-head-pose-estimation-in-the-wild | https://dx.doi.org/10.1109/TPAMI.2020.3046323 | https://dx.doi.org/10.1109/TPAMI.2020.3046323 | null | ['head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-3.33606392e-01 3.56467277e-01 3.93731177e-01 -6.46561801e-01
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87de8655-db4b-44d4-b4bb-63fe157995bb | multimodal-grounding-for-sequence-to-sequence | 1811.03865 | null | http://arxiv.org/abs/1811.03865v2 | http://arxiv.org/pdf/1811.03865v2.pdf | Multimodal Grounding for Sequence-to-Sequence Speech Recognition | Humans are capable of processing speech by making use of multiple sensory
modalities. For example, the environment where a conversation takes place
generally provides semantic and/or acoustic context that helps us to resolve
ambiguities or to recall named entities. Motivated by this, there have been
many works studying the integration of visual information into the speech
recognition pipeline. Specifically, in our previous work, we propose a
multistep visual adaptive training approach which improves the accuracy of an
audio-based Automatic Speech Recognition (ASR) system. This approach, however,
is not end-to-end as it requires fine-tuning the whole model with an adaptation
layer. In this paper, we propose novel end-to-end multimodal ASR systems and
compare them to the adaptive approach by using a range of visual
representations obtained from state-of-the-art convolutional neural networks.
We show that adaptive training is effective for S2S models leading to an
absolute improvement of 1.4% in word error rate. As for the end-to-end systems,
although they perform better than baseline, the improvements are slightly less
than adaptive training, 0.8 absolute WER reduction in single-best models. Using
ensemble decoding, end-to-end models reach a WER of 15% which is the lowest
score among all systems. | ['Loïc Barrault', 'Shruti Palaskar', 'Ramon Sanabria', 'Ozan Caglayan', 'Florian Metze'] | 2018-11-09 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 2.73637384e-01 3.74205321e-01 2.26148263e-01 -5.02069056e-01
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62db05ea-f299-4f63-969d-636d2fd55cf0 | feature-based-recursive-observer-design-for | 1606.03021 | null | http://arxiv.org/abs/1606.03021v1 | http://arxiv.org/pdf/1606.03021v1.pdf | Feature-based Recursive Observer Design for Homography Estimation | This paper presents a new algorithm for online estimation of a sequence of
homographies applicable to image sequences obtained from robotic vehicles
equipped with vision sensors. The approach taken exploits the underlying
Special Linear group structure of the set of homographies along with gyroscope
measurements and direct point-feature correspondences between images to develop
temporal filter for the homography estimate. Theoretical analysis and
experimental results are provided to demonstrate the robustness of the proposed
algorithm. The experimental results show excellent performance even in the case
of very fast camera motion (relative to frame rate), severe occlusion, and in
the presence of specular reflections. | ['Tarek Hamel', 'Minh-Duc Hua', 'Pascal Morin', 'Robert Mahony', 'Jochen Trumpf'] | 2016-06-09 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.52488142e-01 -1.42846823e-01 -5.99782169e-02 -2.03800142e-01
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f8eae272-cf1a-402f-94ae-4d1e30cf3548 | alphafold-distillation-for-improved-inverse | 2210.03488 | null | https://arxiv.org/abs/2210.03488v1 | https://arxiv.org/pdf/2210.03488v1.pdf | AlphaFold Distillation for Improved Inverse Protein Folding | Inverse protein folding, i.e., designing sequences that fold into a given three-dimensional structure, is one of the fundamental design challenges in bio-engineering and drug discovery. Traditionally, inverse folding mainly involves learning from sequences that have an experimentally resolved structure. However, the known structures cover only a tiny space of the protein sequences, imposing limitations on the model learning. Recently proposed forward folding models, e.g., AlphaFold, offer unprecedented opportunity for accurate estimation of the structure given a protein sequence. Naturally, incorporating a forward folding model as a component of an inverse folding approach offers the potential of significantly improving the inverse folding, as the folding model can provide a feedback on any generated sequence in the form of the predicted protein structure or a structural confidence metric. However, at present, these forward folding models are still prohibitively slow to be a part of the model optimization loop during training. In this work, we propose to perform knowledge distillation on the folding model's confidence metrics, e.g., pTM or pLDDT scores, to obtain a smaller, faster and end-to-end differentiable distilled model, which then can be included as part of the structure consistency regularized inverse folding model training. Moreover, our regularization technique is general enough and can be applied in other design tasks, e.g., sequence-based protein infilling. Extensive experiments show a clear benefit of our method over the non-regularized baselines. For example, in inverse folding design problems we observe up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity, while still preserving structural consistency of the generated sequences. | ['Vijil Chenthamarakshan', 'Payel Das', 'Aurelie Lozano', 'Igor Melnyk'] | 2022-10-05 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 3.27906728e-01 1.80735201e-01 -1.33873656e-01 -5.57124734e-01
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475ac60b-d217-4148-a7d2-dfc7b5d54f0e | adversarial-text-generation-via-sequence | null | null | https://aclanthology.org/2020.findings-emnlp.5 | https://aclanthology.org/2020.findings-emnlp.5.pdf | Adversarial Text Generation via Sequence Contrast Discrimination | In this paper, we propose a sequence contrast loss driven text generation framework, which learns the difference between real texts and generated texts and uses that difference. Specifically, our discriminator contains a discriminative sequence generator instead of a binary classifier, and measures the {`}relative realism{'} of generated texts against real texts by making use of them simultaneously. Moreover, our generator uses discriminative sequences to directly improve itself, which not only replaces the gradient propagation process from the discriminator to the generator, but also avoids the time-consuming sampling process of estimating rewards in some previous methods. We conduct extensive experiments with various metrics, substantiating that our framework brings improvements in terms of training stability and the quality of generated texts. | ['Xiaojun Wan', 'Ke Wang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['adversarial-text'] | ['adversarial'] | [ 4.42504495e-01 4.06130478e-02 1.21091923e-03 -2.55189449e-01
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4c185948-2442-4bd8-ab6e-c0dfb0e32e99 | adacc-cumulative-cost-sensitive-boosting-for | 2209.08309 | null | https://arxiv.org/abs/2209.08309v1 | https://arxiv.org/pdf/2209.08309v1.pdf | AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification | Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided as input to the learner. Such parameter tuning is a challenging task that requires domain knowledge and moreover, wrong adjustments might lead to overall predictive performance deterioration. In this work, we propose a novel cost-sensitive boosting approach for imbalanced data that dynamically adjusts the misclassification costs over the boosting rounds in response to model's performance instead of using a fixed misclassification cost matrix. Our method, called AdaCC, is parameter-free as it relies on the cumulative behavior of the boosting model in order to adjust the misclassification costs for the next boosting round and comes with theoretical guarantees regarding the training error. Experiments on 27 real-world datasets from different domains with high class imbalance demonstrate the superiority of our method over 12 state-of-the-art cost-sensitive boosting approaches exhibiting consistent improvements in different measures, for instance, in the range of [0.3%-28.56%] for AUC, [3.4%-21.4%] for balanced accuracy, [4.8%-45%] for gmean and [7.4%-85.5%] for recall. | ['Eirini Ntoutsi', 'Bodo Rosenhahn', 'Symeon Papadopoulos', 'Vasileios Iosifidis'] | 2022-09-17 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.31559122e-01 -2.69484706e-02 -4.75551665e-01 -6.41169310e-01
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1ecff834-3419-4ec8-a938-bd85a7f3785d | ms-lstm-exploring-spatiotemporal-multiscale | 2304.07724 | null | https://arxiv.org/abs/2304.07724v2 | https://arxiv.org/pdf/2304.07724v2.pdf | MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain | The drastic variation of motion in spatial and temporal dimensions makes the video prediction task extremely challenging. Existing RNN models obtain higher performance by deepening or widening the model. They obtain the multi-scale features of the video only by stacking layers, which is inefficient and brings unbearable training costs (such as memory, FLOPs, and training time). Different from them, this paper proposes a spatiotemporal multi-scale model called MS-LSTM wholly from a multi-scale perspective. On the basis of stacked layers, MS-LSTM incorporates two additional efficient multi-scale designs to fully capture spatiotemporal context information. Concretely, we employ LSTMs with mirrored pyramid structures to construct spatial multi-scale representations and LSTMs with different convolution kernels to construct temporal multi-scale representations. Detailed comparison experiments with eight baseline models on four video datasets show that MS-LSTM has better performance but lower training costs. | ['Jie Liu', 'Hao Zhang', 'Zhifeng Ma'] | 2023-04-16 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-1.36880144e-01 -5.20366967e-01 -2.67907381e-01 -2.62513757e-01
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8e2be57d-df73-4106-8498-cf9face2fee0 | bayesian-hierarchical-dynamic-model-for-human | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Bayesian_Hierarchical_Dynamic_Model_for_Human_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Bayesian_Hierarchical_Dynamic_Model_for_Human_Action_Recognition_CVPR_2019_paper.pdf | Bayesian Hierarchical Dynamic Model for Human Action Recognition | Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action. To address this issue, we propose a probabilistic model called Hierarchical Dynamic Model (HDM). Leveraging on Bayesian framework, the model parameters are allowed to vary across different sequences of data, which increase the capacity of the model to adapt to intra-class variations on both spatial and temporal extent of actions. Meanwhile, the generative learning process allows the model to preserve the distinctive dynamic pattern for each action class. Through Bayesian inference, we are able to quantify the uncertainty of the classification, providing insight during the decision process. Compared to state-of-the-art methods, our method not only achieves competitive recognition performance within individual dataset but also shows better generalization capability across different datasets. Experiments conducted on data with missing values also show the robustness of the proposed method.
| [' Qiang Ji', ' Hui Su', ' Wanru Xu', 'Rui Zhao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 3.46909821e-01 -3.92368525e-01 -2.82191396e-01 -4.15941089e-01
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ad023f7e-177e-4e77-9fcb-8e547b3f318b | color-constancy-using-cnns | 1504.04548 | null | http://arxiv.org/abs/1504.04548v1 | http://arxiv.org/pdf/1504.04548v1.pdf | Color Constancy Using CNNs | In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN. | ['Raimondo Schettini', 'Claudio Cusano', 'Simone Bianco'] | 2015-04-17 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.56265283e-01 -2.39840224e-01 1.18180383e-02 -6.13767803e-01
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976c0c48-5aa6-4138-8e37-d489abacdd39 | yedda-a-lightweight-collaborative-text-span | 1711.03759 | null | http://arxiv.org/abs/1711.03759v3 | http://arxiv.org/pdf/1711.03759v3.pdf | YEDDA: A Lightweight Collaborative Text Span Annotation Tool | In this paper, we introduce \textsc{Yedda}, a lightweight but efficient and
comprehensive open-source tool for text span annotation. \textsc{Yedda}
provides a systematic solution for text span annotation, ranging from
collaborative user annotation to administrator evaluation and analysis. It
overcomes the low efficiency of traditional text annotation tools by annotating
entities through both command line and shortcut keys, which are configurable
with custom labels. \textsc{Yedda} also gives intelligent recommendations by
learning the up-to-date annotated text. An administrator client is developed to
evaluate annotation quality of multiple annotators and generate detailed
comparison report for each annotator pair. Experiments show that the proposed
system can reduce the annotation time by half compared with existing annotation
tools. And the annotation time can be further compressed by 16.47\% through
intelligent recommendation. | ['Jie Yang', 'Yue Zhang', 'Linwei Li', 'Xingxuan Li'] | 2017-11-10 | yedda-a-lightweight-collaborative-text-span-1 | https://aclanthology.org/P18-4006 | https://aclanthology.org/P18-4006.pdf | acl-2018-7 | ['text-annotation'] | ['natural-language-processing'] | [-9.21200588e-02 1.76751956e-01 -2.70582251e-02 -3.48796338e-01
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0460a9ba-ecd4-40a9-90a7-83f899f5b2bb | clip-vg-self-paced-curriculum-adapting-of | 2305.08685 | null | https://arxiv.org/abs/2305.08685v2 | https://arxiv.org/pdf/2305.08685v2.pdf | CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding | Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reliance on manually labeled data, unsupervised methods have been developed to locate regions using pseudo-labels. However, the performance of existing unsupervised methods is highly dependent on the quality of pseudo-labels and these methods always encounter issues with limited diversity. In order to utilize vision and language pre-trained models to address the grounding problem, and reasonably take advantage of pseudo-labels, we propose CLIP-VG, a novel method that can conduct self-paced curriculum adapting of CLIP with pseudo-language labels. We propose a simple yet efficient end-to-end network architecture to realize the transfer of CLIP to the visual grounding. Based on the CLIP-based architecture, we further propose single-source and multi-source curriculum adapting algorithms, which can progressively find more reliable pseudo-labels to learn an optimal model, thereby achieving a balance between reliability and diversity for the pseudo-language labels. Our method outperforms the current state-of-the-art unsupervised method by a significant margin on RefCOCO/+/g datasets in both single-source and multi-source scenarios, with improvements ranging from 6.78% to 10.67% and 11.39% to 14.87%, respectively. Furthermore, our approach even outperforms existing weakly supervised methods. The code and models will be available at https://github.com/linhuixiao/CLIP-VG. | ['Changsheng Xu', 'YaoWei Wang', 'Ming Yan', 'Fang Peng', 'Xiaoshan Yang', 'Linhui Xiao'] | 2023-05-15 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 9.38065648e-02 -6.55267164e-02 -3.61040652e-01 -5.66991806e-01
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a70049c9-29ce-4f2b-bd06-d8fceb1e9593 | simple-contrastive-graph-clustering | 2205.07865 | null | https://arxiv.org/abs/2205.07865v3 | https://arxiv.org/pdf/2205.07865v3.pdf | Simple Contrastive Graph Clustering | Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, i.e., pre-processing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent pre-processing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter un-shared siamese encoders and corrupting the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average. | ['Xinwang Liu', 'Sihang Zhou', 'Xihong Yang', 'Yue Liu'] | 2022-05-11 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.03886910e-01 -7.83252344e-03 1.82835404e-02 -4.63807225e-01
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1794013a-c272-4e9c-bed4-3710a706ff28 | a-spatiotemporal-oriented-energy-network-for | 1708.06690 | null | http://arxiv.org/abs/1708.06690v1 | http://arxiv.org/pdf/1708.06690v1.pdf | A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition | This paper presents a novel hierarchical spatiotemporal orientation
representation for spacetime image analysis. It is designed to combine the
benefits of the multilayer architecture of ConvNets and a more controlled
approach to spacetime analysis. A distinguishing aspect of the approach is that
unlike most contemporary convolutional networks no learning is involved;
rather, all design decisions are specified analytically with theoretical
motivations. This approach makes it possible to understand what information is
being extracted at each stage and layer of processing as well as to minimize
heuristic choices in design. Another key aspect of the network is its recurrent
nature, whereby the output of each layer of processing feeds back to the input.
To keep the network size manageable across layers, a novel cross-channel
feature pooling is proposed. The multilayer architecture that results
systematically reveals hierarchical image structure in terms of multiscale,
multiorientation properties of visual spacetime. To illustrate its utility, the
network has been applied to the task of dynamic texture recognition. Empirical
evaluation on multiple standard datasets shows that it sets a new
state-of-the-art. | ['Isma Hadji', 'Richard P. Wildes'] | 2017-08-22 | a-spatiotemporal-oriented-energy-network-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Hadji_A_Spatiotemporal_Oriented_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hadji_A_Spatiotemporal_Oriented_ICCV_2017_paper.pdf | iccv-2017-10 | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 2.16707468e-01 -1.66428369e-02 -9.81210172e-02 -2.79682249e-01
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c98be523-588d-4629-a54e-3e04e005996c | dan-a-segmentation-free-document-attention | 2203.12273 | null | https://arxiv.org/abs/2203.12273v4 | https://arxiv.org/pdf/2203.12273v4.pdf | DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition | Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN. | ['Thierry Paquet', 'Clément Chatelain', 'Denis Coquenet'] | 2022-03-23 | null | null | null | null | ['handwritten-document-recognition'] | ['computer-vision'] | [ 4.79606539e-01 -3.72282937e-02 -2.47793496e-01 -5.71726918e-01
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49c449a4-7c0f-4c76-ac8d-b5fa6cf82129 | d-rex-dialogue-relation-extraction-with | 2109.05126 | null | https://arxiv.org/abs/2109.05126v2 | https://arxiv.org/pdf/2109.05126v2.pdf | D-REX: Dialogue Relation Extraction with Explanations | Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model. Finally, our evaluations on a dialogue relation extraction dataset show that our method is simple yet effective and achieves a state-of-the-art F1 score on relation extraction, improving upon existing methods by 13.5%. | ['William Yang Wang', 'Lise Getoor', 'Yi-Lin Tuan', 'Varun Embar', 'Alon Albalak'] | 2021-09-10 | null | https://aclanthology.org/2022.nlp4convai-1.4 | https://aclanthology.org/2022.nlp4convai-1.4.pdf | nlp4convai-acl-2022-5 | ['dialog-relation-extraction', 'relation-explanation'] | ['natural-language-processing', 'natural-language-processing'] | [ 0.3006738 1.4259088 -0.8775344 -0.62125635 -0.99367887 -0.55858374
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8b393f3d-c253-4052-a0b3-364265046b31 | iart-a-search-engine-for-art-historical | 2108.01542 | null | https://arxiv.org/abs/2108.01542v1 | https://arxiv.org/pdf/2108.01542v1.pdf | iART: A Search Engine for Art-Historical Images to Support Research in the Humanities | In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to define queries and explore results. By using a state-of-the-art cross-modal deep learning approach, it is possible to search for concepts that were not previously detected by trained classification models. Art-historical objects from large, openly licensed collections such as Amsterdam Rijksmuseum and Wikidata are made available to users. | ['Ralph Ewerth', 'Hubertus Kohle', 'Eyke Hüllermeier', 'Javad Rahnama', 'Stefanie Schneider', 'Matthias Springstein'] | 2021-08-03 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-3.56447846e-01 -6.89528883e-01 -4.08817947e-01 8.00728053e-02
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7deccfdf-35d1-4174-8e8f-9ab335860498 | test-time-adaptation-with-regularized-loss | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.pdf | Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection | It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained model to that image. Indeed nothing can be closer to the test data than the test image itself. The main difficulty of test-time adaptation is that the ground truth is not available. Thus test-time adaptation, while intriguing, applies to only a few scenarios where one can design an effective loss function that does not require ground truth. We propose the first approach for test-time Salient Object Detection (SOD) in the context of weak supervision. Our approach is based on a so called regularized loss function, which can be used for training CNN when pixel precise ground truth is unavailable. Regularized loss tends to have lower values for the more likely object segments, and thus it can be used to fine-tune an already trained CNN to a given test image, adapting to images unseen during training. We develop a regularized loss function particularly suitable for test-time adaptation and show that our approach significantly outperforms prior work for weakly supervised SOD. | ['Olga Veksler'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['salient-object-detection-1'] | ['computer-vision'] | [ 3.85647774e-01 2.92957425e-01 -1.00397646e-01 -5.31937063e-01
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22dc8073-a248-4a39-9301-85ca9316ed5c | a-neural-acoustic-echo-canceller-optimized | 2106.00856 | null | https://arxiv.org/abs/2106.00856v1 | https://arxiv.org/pdf/2106.00856v1.pdf | A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data | We consider the problem of recognizing speech utterances spoken to a device which is generating a known sound waveform; for example, recognizing queries issued to a digital assistant which is generating responses to previous user inputs. Previous work has proposed building acoustic echo cancellation (AEC) models for this task that optimize speech enhancement metrics using both neural network as well as signal processing approaches. Since our goal is to recognize the input speech, we consider enhancements which improve word error rates (WERs) when the predicted speech signal is passed to an automatic speech recognition (ASR) model. First, we augment the loss function with a term that produces outputs useful to a pre-trained ASR model and show that this augmented loss function improves WER metrics. Second, we demonstrate that augmenting our training dataset of real world examples with a large synthetic dataset improves performance. Crucially, applying SpecAugment style masks to the reference channel during training aids the model in adapting from synthetic to real domains. In experimental evaluations, we find the proposed approaches improve performance, on average, by 57% over a signal processing baseline and 45% over the neural AEC model without the proposed changes. | ['Rohit Prabhavalkar', 'Alexander Gruenstein', 'Turaj Zakizadeh Shabestary', 'Alex Park', 'Nathan Howard'] | 2021-06-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 9.58029091e-01 1.30670920e-01 6.50650084e-01 -6.69093132e-01
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f520a5b2-5cfe-41a5-b175-5e9dbc815674 | transfer-free-data-efficient-multilingual | 2305.13528 | null | https://arxiv.org/abs/2305.13528v1 | https://arxiv.org/pdf/2305.13528v1.pdf | Transfer-Free Data-Efficient Multilingual Slot Labeling | Slot labeling (SL) is a core component of task-oriented dialogue (ToD) systems, where slots and corresponding values are usually language-, task- and domain-specific. Therefore, extending the system to any new language-domain-task configuration requires (re)running an expensive and resource-intensive data annotation process. To mitigate the inherent data scarcity issue, current research on multilingual ToD assumes that sufficient English-language annotated data are always available for particular tasks and domains, and thus operates in a standard cross-lingual transfer setup. In this work, we depart from this often unrealistic assumption. We examine challenging scenarios where such transfer-enabling English annotated data cannot be guaranteed, and focus on bootstrapping multilingual data-efficient slot labelers in transfer-free scenarios directly in the target languages without any English-ready data. We propose a two-stage slot labeling approach (termed TWOSL) which transforms standard multilingual sentence encoders into effective slot labelers. In Stage 1, relying on SL-adapted contrastive learning with only a handful of SL-annotated examples, we turn sentence encoders into task-specific span encoders. In Stage 2, we recast SL from a token classification into a simpler, less data-intensive span classification task. Our results on two standard multilingual TOD datasets and across diverse languages confirm the effectiveness and robustness of TWOSL. It is especially effective for the most challenging transfer-free few-shot setups, paving the way for quick and data-efficient bootstrapping of multilingual slot labelers for ToD. | ['Anna Korhonen', 'Ivan Vulić', 'Evgeniia Razumovskaia'] | 2023-05-22 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 5.05312867e-02 2.10808650e-01 -3.43084008e-01 -4.52672243e-01
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aeeaa132-3f6a-4296-bd0b-8abbf3fd3237 | probing-for-bridging-inference-in-transformer | 2104.09400 | null | https://arxiv.org/abs/2104.09400v1 | https://arxiv.org/pdf/2104.09400v1.pdf | Probing for Bridging Inference in Transformer Language Models | We probe pre-trained transformer language models for bridging inference. We first investigate individual attention heads in BERT and observe that attention heads at higher layers prominently focus on bridging relations in-comparison with the lower and middle layers, also, few specific attention heads concentrate consistently on bridging. More importantly, we consider language models as a whole in our second approach where bridging anaphora resolution is formulated as a masked token prediction task (Of-Cloze test). Our formulation produces optimistic results without any fine-tuning, which indicates that pre-trained language models substantially capture bridging inference. Our further investigation shows that the distance between anaphor-antecedent and the context provided to language models play an important role in the inference. | ['Yufang Hou', 'Onkar Pandit'] | 2021-04-19 | null | https://aclanthology.org/2021.naacl-main.327 | https://aclanthology.org/2021.naacl-main.327.pdf | naacl-2021-4 | ['cloze-test', 'bridging-anaphora-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-2.84576267e-01 6.73542023e-01 -4.95769262e-01 -2.42578685e-01
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40e55493-eaac-4819-bf5b-cd3a5cf1e5f0 | learning-matchable-colorspace-transformations | 1904.01080 | null | https://arxiv.org/abs/1904.01080v5 | https://arxiv.org/pdf/1904.01080v5.pdf | Learning Matchable Image Transformations for Long-term Metric Visual Localization | Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the `appearance gap,' the number of experiences required for reliable metric localization over days or months can be very large, and methods for reducing the necessary number of experiences are needed for this approach to scale. Taking inspiration from color constancy theory, we learn a nonlinear RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature matches for images captured under different lighting and weather conditions, and use it as a pre-processing step in a conventional single-experience localization pipeline to improve its robustness to appearance change. We train this mapping by approximating the target non-differentiable localization pipeline with a deep neural network, and find that incorporating a learned low-dimensional context feature can further improve cross-appearance feature matching. Using synthetic and real-world datasets, we demonstrate substantial improvements in localization performance across day-night cycles, enabling continuous metric localization over a 30-hour period using a single mapping experience, and allowing experience-based localization to scale to long deployments with dramatically reduced data requirements. | ['Lee Clement', 'Mona Gridseth', 'Justin Tomasi', 'Jonathan Kelly'] | 2019-04-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.14522666e-01 -3.81341934e-01 1.97353199e-01 -6.62268102e-01
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0a458334-94b0-40b0-a62e-c9f0e1d097f7 | nested-named-entity-recognition-for-chinese | null | null | https://aclanthology.org/2021.rocling-1.3 | https://aclanthology.org/2021.rocling-1.3.pdf | Nested Named Entity Recognition for Chinese Electronic Health Records with QA-based Sequence Labeling | This study presents a novel QA-based sequence labeling (QASL) approach to naturally tackle both flat and nested Named Entity Recogntion (NER) tasks on a Chinese Electronic Health Records (CEHRs) dataset. This proposed QASL approach parallelly asks a corresponding natural language question for each specific named entity type, and then identifies those associated NEs of the same specified type with the BIO tagging scheme. The associated nested NEs are then formed by overlapping the results of various types. In comparison with those pure sequence-labeling (SL) approaches, since the given question includes significant prior knowledge about the specified entity type and the capability of extracting NEs with different types, the performance for nested NER task is thus improved, obtaining 90.70% of F1-score. Besides, in comparison with the pure QA-based approach, our proposed approach retains the SL features, which could extract multiple NEs with the same types without knowing the exact number of NEs in the same passage in advance. Eventually, experiments on our CEHR dataset demonstrate that QASL-based models greatly outperform the SL-based models by 6.12% to 7.14% of F1-score. | ['Keh-Yih Su', 'Cheng-Lung Sung', 'Chih-Hao Lin', 'Yu-Lun Chiang'] | null | null | null | null | rocling-2021-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 2.37133771e-01 2.29181781e-01 1.00064673e-01 -1.71830341e-01
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6735141f-087a-4982-b0f2-0f4d14fddeb4 | diversevul-a-new-vulnerable-source-code | 2304.00409 | null | https://arxiv.org/abs/2304.00409v1 | https://arxiv.org/pdf/2304.00409v1.pdf | DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection | We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 150 CWEs, 26,635 vulnerable functions, and 352,606 non-vulnerable functions extracted from 7,861 commits. Our dataset covers 305 more projects than all previous datasets combined. We show that increasing the diversity and volume of training data improves the performance of deep learning models for vulnerability detection. Combining our new dataset with previous datasets, we present an analysis of the challenges and promising research directions of using deep learning for detecting software vulnerabilities. We study 11 model architectures belonging to 4 families. Our results show that deep learning is still not ready for vulnerability detection, due to high false positive rate, low F1 score, and difficulty of detecting hard CWEs. In particular, we demonstrate an important generalization challenge for the deployment of deep learning-based models. However, we also identify hopeful future research directions. We demonstrate that large language models (LLMs) are the future for vulnerability detection, outperforming Graph Neural Networks (GNNs) with manual feature engineering. Moreover, developing source code specific pre-training objectives is a promising research direction to improve the vulnerability detection performance. | ['David Wagner', 'Xinyun Chen', 'Zhoujie Ding', 'Yizheng Chen'] | 2023-04-01 | null | null | null | null | ['feature-engineering', 'vulnerability-detection'] | ['methodology', 'miscellaneous'] | [-3.59981924e-01 3.78006622e-02 -1.54999867e-01 -9.36782807e-02
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590e5932-80ef-46bc-9331-90eb718739f1 | image-processing-operations-identification | 1709.02908 | null | http://arxiv.org/abs/1709.02908v1 | http://arxiv.org/pdf/1709.02908v1.pdf | Image Processing Operations Identification via Convolutional Neural Network | In recent years, image forensics has attracted more and more attention, and
many forensic methods have been proposed for identifying image processing
operations. Up to now, most existing methods are based on hand crafted
features, and just one specific operation is considered in their methods. In
many forensic scenarios, however, multiple classification for various image
processing operations is more practical. Besides, it is difficult to obtain
effective features by hand for some image processing operations. In this paper,
therefore, we propose a new convolutional neural network (CNN) based method to
adaptively learn discriminative features for identifying typical image
processing operations. We carefully design the high pass filter bank to get the
image residuals of the input image, the channel expansion layer to mix up the
resulting residuals, the pooling layers, and the activation functions employed
in our method. The extensive results show that the proposed method can
outperform the currently best method based on hand crafted features and three
related methods based on CNN for image steganalysis and/or forensics, achieving
the state-of-the-art results. Furthermore, we provide more supplementary
results to show the rationality and robustness of the proposed model. | ['Haodong Li', 'Bolin Chen', 'Weiqi Luo'] | 2017-09-09 | null | null | null | null | ['steganalysis', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 3.48637968e-01 -6.26311839e-01 1.67891175e-01 -1.45346224e-01
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ab118e03-a351-427b-9c7e-fff4bf976a39 | causality-based-ctr-prediction-using-graph | 2301.12762 | null | https://arxiv.org/abs/2301.12762v1 | https://arxiv.org/pdf/2301.12762v1.pdf | Causality-based CTR Prediction using Graph Neural Networks | As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising. In our model, a structured representation learning method (GraphFwFM) is designed to capture high-order representations on feature graph based on causal discovery among field features in gated graph neural networks (GGNNs), and GraphSAGE is employed to obtain graph representations of users and ads. Experiments conducted on three public datasets demonstrate the superiority of Causal-GNN in AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order representations on causal feature graph. | ['Chunjie Zhang', 'Yanwu Yang', 'Panyu Zhai'] | 2023-01-30 | null | null | null | null | ['causal-discovery', 'click-through-rate-prediction'] | ['knowledge-base', 'miscellaneous'] | [ 1.50264293e-01 2.07356974e-01 -6.82982564e-01 -6.70661509e-01
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54dcdd6d-c305-4fc4-a707-a20a3671a949 | ma-nerf-motion-assisted-neural-radiance | 2306.10350 | null | https://arxiv.org/abs/2306.10350v2 | https://arxiv.org/pdf/2306.10350v2.pdf | MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images | We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that originate from inputs. Meanwhile, although current NeRF-based avatar methods provide promising results for novel view synthesis, they fail to generalize well for unseen expressions. We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions. At the core of our implementation are structured displacement feature and semantic-aware learning module. Our structured displacement feature will introduce the motion prior as an additional constraints and help perform better for unseen expressions, by constructing displacement volume. Besides, the semantic-aware learning incorporates multi-level prior, e.g., semantic embedding, learnable latent code, to lift the performance to a higher level. Thorough experiments have been doen both quantitatively and qualitatively to demonstrate the design of our framework, and our method achieves much better results than the current state-of-the-arts. | ['Chun Yuan', 'Wensen Feng', 'Yukang Cao', 'Xiang Zhou', 'Weichen Zhang'] | 2023-06-17 | null | null | null | null | ['novel-view-synthesis', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 6.86018243e-02 4.74074066e-01 -7.91029856e-02 -5.11408210e-01
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c29bbdf3-a74c-4eef-8532-cb541a968ac1 | when-accuracy-meets-privacy-two-stage | 2203.12803 | null | https://arxiv.org/abs/2203.12803v2 | https://arxiv.org/pdf/2203.12803v2.pdf | A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study | COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainly bring the other challenge since medical-related records are often deemed patients' private information and protected by laws such as GDPR and HIPPA. Federated learning can ensure the model training is decentralized on different devices and no data is shared among them, which guarantees privacy. However, with data located on different devices, the accessible data of each device could be limited. Since transfer learning has been verified in dealing with limited data with good performance, therefore, in this paper, We made a trial to implement federated learning and transfer learning techniques using CNNs to classify COVID-19 using lung CT scans. We also explored the impact of dataset distribution at the client-side in federated learning and the number of training epochs a model is trained. Finally, we obtained very high performance with federated learning, demonstrating our success in leveraging accuracy and privacy. | ['Naomi Fengqi Li', 'Alexandros Shikun Zhang'] | 2022-03-24 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.64430225e-01 1.73128303e-02 -3.11231464e-01 -3.23202670e-01
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431b5f87-241e-48f8-9088-7570d9cd40fb | improving-cross-domain-cross-lingual-and | null | null | https://aclanthology.org/2022.acl-srw.30 | https://aclanthology.org/2022.acl-srw.30.pdf | Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection | With the increase of deception and misinformation especially in social media, it has become crucial to be able to develop machine learning methods to automatically identify deceptive language. In this proposal, we identify key challenges underlying deception detection in cross-domain, cross-lingual and multi-modal settings. To improve cross-domain deception classification, we propose to use inter-domain distance to identify a suitable source domain for a given target domain. We propose to study the efficacy of multilingual classification models vs translation for cross-lingual deception classification. Finally, we propose to better understand multi-modal deception detection and explore methods to weight and combine information from multiple modalities to improve multi-modal deception classification. | ['Sarah Ita Levitan', 'Subhadarshi Panda'] | null | null | null | null | acl-2022-5 | ['deception-detection'] | ['miscellaneous'] | [-2.44888544e-01 -6.20779037e-01 -2.40933374e-01 -3.66941601e-01
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d202fa15-7477-4244-a090-c06bfc3e27c0 | evopose-a-recursive-transformer-for-3d-human | 2306.09615 | null | https://arxiv.org/abs/2306.09615v1 | https://arxiv.org/pdf/2306.09615v1.pdf | EVOPOSE: A Recursive Transformer For 3D Human Pose Estimation With Kinematic Structure Priors | Transformer is popular in recent 3D human pose estimation, which utilizes long-term modeling to lift 2D keypoints into the 3D space. However, current transformer-based methods do not fully exploit the prior knowledge of the human skeleton provided by the kinematic structure. In this paper, we propose a novel transformer-based model EvoPose to introduce the human body prior knowledge for 3D human pose estimation effectively. Specifically, a Structural Priors Representation (SPR) module represents human priors as structural features carrying rich body patterns, e.g. joint relationships. The structural features are interacted with 2D pose sequences and help the model to achieve more informative spatiotemporal features. Moreover, a Recursive Refinement (RR) module is applied to refine the 3D pose outputs by utilizing estimated results and further injects human priors simultaneously. Extensive experiments demonstrate the effectiveness of EvoPose which achieves a new state of the art on two most popular benchmarks, Human3.6M and MPI-INF-3DHP. | ['Nenghai Yu', 'Qi Chu', 'Zhiwei Zhao', 'Bin Liu', 'Yan Lu', 'Yaqi Zhang'] | 2023-06-16 | null | null | null | null | ['pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.09351146e-01 4.58519459e-02 -1.67333752e-01 -1.93714979e-03
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42679272-fa6a-407b-9ec1-84528afbf160 | 3d-aware-face-swapping | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.pdf | 3D-Aware Face Swapping | Face swapping is an important research topic in computer vision with wide applications in entertainment and privacy protection. Existing methods directly learn to swap 2D facial images, taking no account of the geometric information of human faces. In the presence of large pose variance between the source and the target faces, there always exist undesirable artifacts on the swapped face. In this paper, we present a novel 3D-aware face swapping method that generates high-fidelity and multi-view-consistent swapped faces from single-view source and target images. To achieve this, we take advantage of the strong geometry and texture prior of 3D human faces, where the 2D faces are projected into the latent space of a 3D generative model. By disentangling the identity and attribute features in the latent space, we succeed in swapping faces in a 3D-aware manner, being robust to pose variations while transferring fine-grained facial details. Extensive experiments demonstrate the superiority of our 3D-aware face swapping framework in terms of visual quality, identity similarity, and multi-view consistency. Code is available at https://lyx0208.github.io/3dSwap. | ['Xiaokang Yang', 'Wenhan Zhu', 'Yichao Yan', 'Chao Ma', 'Yixuan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [ 4.60013188e-03 -6.78180670e-03 1.98250487e-01 -4.55113083e-01
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2c2f90de-449b-4856-8288-d32ca5229dcc | quotienting-impertinent-camera-kinematics-for | 1903.09073 | null | https://arxiv.org/abs/1903.09073v2 | https://arxiv.org/pdf/1903.09073v2.pdf | Quotienting Impertinent Camera Kinematics for 3D Video Stabilization | With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows are more robust than the sparse feature matching techniques used by existing 3D stabilization methods, able to better handle large camera displacements and occlusions similar to those often found in consumer videos. Here we introduce a framework for 3D video stabilization that relies on dense scene flow alone. The foundation of this approach is a novel camera motion model that allows for real-world camera poses to be recovered directly from 3D motion fields. Moreover, this model can be extended to describe certain types of non-rigid artifacts that are commonly found in videos, such as those resulting from zooms. This framework gives rise to several robust regimes that produce high-quality stabilization of the kind achieved by prior full 3D methods while avoiding the fragility typically present in feature-based approaches. As an added benefit, our framework is fast: the simplicity of our motion model and efficient flow calculations combine to enable stabilization at a high frame rate. | ['Jin Seob Kim', 'Gregory S. Chirikjian', 'Sipu Ruan', 'Christian Wuelker', 'Thomas W. Mitchel'] | 2019-03-21 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-2.29324222e-01 -3.76739651e-01 -1.17940515e-01 8.44677538e-02
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baaf0105-66e2-4f72-ba38-933f868c595c | joint-learning-based-heterogeneous-graph | null | null | https://aclanthology.org/2022.naacl-main.301 | https://aclanthology.org/2022.naacl-main.301.pdf | Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization | Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a joint learning-based heterogeneous graph attention network for TLS (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics. | ['Manabu Okumura', 'Kotaro Funakoshi', 'Hidetaka Kamigaito', 'Dongyuan Li', 'Jingyi You'] | null | null | null | null | naacl-2022-7 | ['timeline-summarization'] | ['natural-language-processing'] | [-7.83255994e-02 2.35479474e-01 -3.30500960e-01 -2.49193311e-01
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b3c2cbaf-f8e3-44b8-a341-791b97f7777f | persona-based-conversational-ai-state-of-the | 2212.03699 | null | https://arxiv.org/abs/2212.03699v1 | https://arxiv.org/pdf/2212.03699v1.pdf | Persona-Based Conversational AI: State of the Art and Challenges | Conversational AI has become an increasingly prominent and practical application of machine learning. However, existing conversational AI techniques still suffer from various limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art methods and outlines challenges and future research directions for advancing personalized conversational AI technology. | ['Ranga Raju Vatsavai', 'Christopher Symons', 'Junfeng Liu'] | 2022-12-04 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.92935485e-01 4.16010499e-01 -1.63520887e-01 -7.69312561e-01
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bd3b3035-f604-4661-a9c5-a2249def0b07 | simple-pose-rethinking-and-improving-a-bottom | 1911.10529 | null | https://arxiv.org/abs/1911.10529v1 | https://arxiv.org/pdf/1911.10529v1.pdf | Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation | We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an improved stacked hourglass network with attention mechanisms, (3) a novel focal L2 loss which is dedicated to hard keypoint and keypoint association (body part) mining, and (4) a robust greedy keypoint assignment algorithm for grouping the detected keypoints into individual poses. Our approach not only works straightforwardly but also outperforms the baseline by about 15% in average precision and is comparable to the state of the art on the MS-COCO test-dev dataset. The code and pre-trained models are publicly available online. | ['Zengfu Wang', 'Wen Su', 'Jia Li'] | 2019-11-24 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.45600691e-01 1.95301011e-01 -2.92808652e-01 -1.18311942e-01
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3494b090-a141-4e9f-9255-dfa88820c672 | kinematic-data-based-action-segmentation-for | 2303.07814 | null | https://arxiv.org/abs/2303.07814v1 | https://arxiv.org/pdf/2303.07814v1.pdf | Kinematic Data-Based Action Segmentation for Surgical Applications | Action segmentation is a challenging task in high-level process analysis, typically performed on video or kinematic data obtained from various sensors. In the context of surgical procedures, action segmentation is critical for workflow analysis algorithms. This work presents two contributions related to action segmentation on kinematic data. Firstly, we introduce two multi-stage architectures, MS-TCN-BiLSTM and MS-TCN-BiGRU, specifically designed for kinematic data. The architectures consist of a prediction generator with intra-stage regularization and Bidirectional LSTM or GRU-based refinement stages. Secondly, we propose two new data augmentation techniques, World Frame Rotation and Horizontal-Flip, which utilize the strong geometric structure of kinematic data to improve algorithm performance and robustness. We evaluate our models on three datasets of surgical suturing tasks: the Variable Tissue Simulation (VTS) Dataset and the newly introduced Bowel Repair Simulation (BRS) Dataset, both of which are open surgery simulation datasets collected by us, as well as the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a well-known benchmark in robotic surgery. Our methods achieve state-of-the-art performance on all benchmark datasets and establish a strong baseline for the BRS dataset. | ['Shlomi Laufer', 'Carla M Pugh', 'Or Rubin', 'Omer Shubi', 'Adam Goldbraikh'] | 2023-03-14 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 4.40861046e-01 2.03707635e-01 -6.02960467e-01 -1.99410319e-01
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5aae6add-1a77-4c60-bb90-1c2010516665 | multimodal-fusion-of-emg-and-vision-for-human | 2104.03893 | null | https://arxiv.org/abs/2104.03893v3 | https://arxiv.org/pdf/2104.03893v3.pdf | Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control | Objective: For lower arm amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Vision sensors are a major source of information about the environment state and can play a vital role in inferring feasible and intended gestures. However, visual evidence is also susceptible to its own artifacts, most often due to object occlusion, lighting changes, etc. Multimodal evidence fusion using physiological and vision sensor measurements is a natural approach due to the complementary strengths of these modalities. Methods: In this paper, we present a Bayesian evidence fusion framework for grasp intent inference using eye-view video, eye-gaze, and EMG from the forearm processed by neural network models. We analyze individual and fused performance as a function of time as the hand approaches the object to grasp it. For this purpose, we have also developed novel data processing and augmentation techniques to train neural network components. Results: Our results indicate that, on average, fusion improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG and visual evidence individually, resulting in an overall fusion accuracy of 95.3%. Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time. | ['Gunar Schirner', 'Deniz Erdogmus', 'Taskin Padir', 'Cagdas Onal', 'Paolo Bonato', 'Mathew Yarossi', 'Mariusz P. Furmanek', 'Sezen Yagmur Gunay', 'Mohammadreza Sharif', 'Mo Han', 'Mehrshad Zandigohar'] | 2021-04-08 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.50372738e-01 -2.24087715e-01 -3.96186590e-01 5.21267056e-02
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ad684251-cbe1-47bc-a004-b1765100ae5d | authorship-attribution-using-text-distortion-1 | null | null | https://1library.net/document/zpnew20y-authorship-attribution-using-text-distortion.html?utm_source=related_list | https://www.aclweb.org/anthology/E17-1107.pdf | Authorship Attribution Using Text Distortion | Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic. | ['Efstathios Stamatatos'] | 2017-04-03 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.39636740e-01 -1.46012217e-01 -1.46517679e-01 -1.45925850e-01
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9cc7c2c6-e4f1-4f01-8cf6-d2c1441dc945 | deep-homography-estimation-in-dynamic | 2109.15098 | null | https://arxiv.org/abs/2109.15098v2 | https://arxiv.org/pdf/2109.15098v2.pdf | Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction | Current laparoscopic camera motion automation relies on rule-based approaches or only focuses on surgical tools. Imitation Learning (IL) methods could alleviate these shortcomings, but have so far been applied to oversimplified setups. Instead of extracting actions from oversimplified setups, in this work we introduce a method that allows to extract a laparoscope holder's actions from videos of laparoscopic interventions. We synthetically add camera motion to a newly acquired dataset of camera motion free da Vinci surgery image sequences through a novel homography generation algorithm. The synthetic camera motion serves as a supervisory signal for camera motion estimation that is invariant to object and tool motion. We perform an extensive evaluation of state-of-the-art (SOTA) Deep Neural Networks (DNNs) across multiple compute regimes, finding our method transfers from our camera motion free da Vinci surgery dataset to videos of laparoscopic interventions, outperforming classical homography estimation approaches in both, precision by 41%, and runtime on a CPU by 43%. | ['Tom Vercauteren', 'Christos Bergeles', 'Sébastien Ourselin', 'Martin Huber'] | 2021-09-30 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.42269894e-01 9.54767913e-02 -1.45754457e-01 5.82327880e-02
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5cff55ed-3f37-4976-bcfd-fe667aa05d0d | copulaboost-additive-modeling-with-copula | 2208.04669 | null | https://arxiv.org/abs/2208.04669v1 | https://arxiv.org/pdf/2208.04669v1.pdf | Copulaboost: additive modeling with copula-based model components | We propose a type of generalised additive models with of model components based on pair-copula constructions, with prediction as a main aim. The model components are designed such that our model may capture potentially complex interaction effects in the relationship between the response covariates. In addition, our model does not require discretisation of continuous covariates, and is therefore suitable for problems with many such covariates. Further, we have designed a fitting algorithm inspired by gradient boosting, as well as efficient procedures for model selection and evaluation of the model components, through constraints on the model space and approximations, that speed up time-costly computations. In addition to being absolutely necessary for our model to be a realistic alternative in higher dimensions, these techniques may also be useful as a basis for designing efficient models selection algorithms for other types of copula regression models. We have explored the characteristics of our method in a simulation study, in particular comparing it to natural alternatives, such as logic regression, classic boosting models and penalised logistic regression. We have also illustrated our approach on the Wisconsin breast cancer dataset and on the Boston housing dataset. The results show that our method has a prediction performance that is either better than or comparable to the other methods, even when the proportion of discrete covariates is high. | ['Ingrid Hobæk Haff', 'Simon Boge Brant'] | 2022-08-09 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 6.54816721e-03 6.65471032e-02 -1.18544303e-01 -5.38252473e-01
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0f9331c8-1ece-49f5-9ce1-44d482ba3427 | a-bottom-up-approach-for-automatic-pancreas | 1407.8497 | null | http://arxiv.org/abs/1407.8497v1 | http://arxiv.org/pdf/1407.8497v1.pdf | A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans | Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD)
system to detect pathologies and perform quantitative analysis. For
anatomically high-variability abdominal organs such as the pancreas, previous
segmentation works report low accuracies when comparing to organs like the
heart or liver. In this paper, a fully-automated bottom-up method is presented
for pancreas segmentation, using abdominal computed tomography (CT) scans. The
method is based on a hierarchical two-tiered information propagation by
classifying image patches. It labels superpixels as pancreas or not via pooling
patch-level confidences on 2D CT slices over-segmented by the Simple Linear
Iterative Clustering approach. A supervised random forest (RF) classifier is
trained on the patch level and a two-level cascade of RFs is applied at the
superpixel level, coupled with multi-channel feature extraction, respectively.
On six-fold cross-validation using 80 patient CT volumes, we achieved 68.8%
Dice coefficient and 57.2% Jaccard Index, comparable to or slightly better than
published state-of-the-art methods. | ['Le Lu', 'Amal Farag', 'Jiamin Liu', 'Ronald M. Summers', 'Evrim Turkbey'] | 2014-07-31 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 3.03685993e-01 3.58576447e-01 -2.51477420e-01 -3.61166388e-01
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294bb99e-be5c-4f0b-b979-131db7e52c3a | totally-ordered-sequential-rules-for-utility | 2209.13501 | null | https://arxiv.org/abs/2209.13501v1 | https://arxiv.org/pdf/2209.13501v1.pdf | Totally-ordered Sequential Rules for Utility Maximization | High utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, allowing it to solve the problem in HUSPM. All existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this paper, we formulate the problem of high utility totally-ordered sequential rule mining and propose two novel algorithms, called TotalSR and TotalSR+, which aim to identify all high utility totally-ordered sequential rules (HTSRs). TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence. We also introduce a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR can also drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. In addition, TotalSR+ uses an auxiliary antecedent record table to more efficiently discover HTSRs. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is significantly more efficient than algorithms with fewer pruning strategies, and TotalSR+ is significantly more efficient than TotalSR in terms of running time and scalability. | ['Philip S. Yu', 'Wensheng Gan', 'Maohua Lyu', 'Chunkai Zhang'] | 2022-09-27 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.29735035e-01 9.58548710e-02 -5.74563146e-01 -7.93408006e-02
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54298ece-364c-4b90-984c-fa203d11952b | learning-like-a-child-fast-novel-visual | 1504.06692 | null | http://arxiv.org/abs/1504.06692v2 | http://arxiv.org/pdf/1504.06692v2.pdf | Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images | In this paper, we address the task of learning novel visual concepts, and
their interactions with other concepts, from a few images with sentence
descriptions. Using linguistic context and visual features, our method is able
to efficiently hypothesize the semantic meaning of new words and add them to
its word dictionary so that they can be used to describe images which contain
these novel concepts. Our method has an image captioning module based on m-RNN
with several improvements. In particular, we propose a transposed weight
sharing scheme, which not only improves performance on image captioning, but
also makes the model more suitable for the novel concept learning task. We
propose methods to prevent overfitting the new concepts. In addition, three
novel concept datasets are constructed for this new task. In the experiments,
we show that our method effectively learns novel visual concepts from a few
examples without disturbing the previously learned concepts. The project page
is http://www.stat.ucla.edu/~junhua.mao/projects/child_learning.html | ['Wei Xu', 'Zhiheng Huang', 'Yi Yang', 'Junhua Mao', 'Alan Yuille', 'Jiang Wang'] | 2015-04-25 | learning-like-a-child-fast-novel-visual-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Mao_Learning_Like_a_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Mao_Learning_Like_a_ICCV_2015_paper.pdf | iccv-2015-12 | ['novel-concepts'] | ['reasoning'] | [ 2.69043922e-01 1.34706855e-01 -1.83506846e-01 -5.13212562e-01
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bed0c449-99fc-4bcd-9ba8-ce1c502397ad | simultaneous-or-sequential-training-how | 2306.02972 | null | https://arxiv.org/abs/2306.02972v1 | https://arxiv.org/pdf/2306.02972v1.pdf | Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System | Speech representation learning with self-supervised algorithms has resulted in notable performance boosts in many downstream tasks. Recent work combined self-supervised learning (SSL) and visually grounded speech (VGS) processing mechanisms for representation learning. The joint training with SSL and VGS mechanisms provides the opportunity to utilize both unlabeled speech and speech-related visual information based on data availability. This has shown to enhance the quality of learned representations, especially at encoding semantic- and lexical-level knowledge. In this work, we further study the joint optimization of wav2vec 2.0-based SSL and transformer-based VGS as a multi-task learning system. We explore a set of training scenarios to understand how speech representations are shared or transferred between the two tasks, and what is the optimal training strategy for cross-modal semantic retrieval and phoneme discrimination performance. As a result, we find that sequential training with wav2vec 2.0 first and VGS next provides higher performance on audio-visual retrieval compared to simultaneous optimization of both learning mechanisms. However, the parallel SSL-VGS training reduces the effects of catastrophic forgetting when switching between optimization criteria. Moreover, the results suggest that phonemic representations learned through the VGS mechanism may generalize better across datasets compared to those learned with SSL. | ['Okko Räsänen', 'Tuomas Virtanen', 'María Andrea Cruz Blandón', 'Khazar Khorrami'] | 2023-06-05 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.69996336e-01 -9.87671092e-02 -1.18248150e-01 -2.10035458e-01
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341fbe2a-4030-4f1f-88ba-f6cd6b483d4c | lavt-language-aware-vision-transformer-for | 2112.02244 | null | https://arxiv.org/abs/2112.02244v2 | https://arxiv.org/pdf/2112.02244v2.pdf | LAVT: Language-Aware Vision Transformer for Referring Image Segmentation | Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling this problem is to leverage a powerful vision-language ("cross-modal") decoder to fuse features independently extracted from a vision encoder and a language encoder. Recent methods have made remarkable advancements in this paradigm by exploiting Transformers as cross-modal decoders, concurrent to the Transformer's overwhelming success in many other vision-language tasks. Adopting a different approach in this work, we show that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network. By conducting cross-modal feature fusion in the visual feature encoding stage, we can leverage the well-proven correlation modeling power of a Transformer encoder for excavating helpful multi-modal context. This way, accurate segmentation results are readily harvested with a light-weight mask predictor. Without bells and whistles, our method surpasses the previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins. | ['Philip H. S. Torr', 'Hengshuang Zhao', 'Kai Chen', 'Yansong Tang', 'Jiaqi Wang', 'Zhao Yang'] | 2021-12-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.68661153e-01 2.35875487e-01 -2.21613161e-02 -3.76024544e-01
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d8f9e79a-37c8-4640-96a4-774b2b356b06 | heart-rate-estimation-from | 1809.03174 | null | http://arxiv.org/abs/1809.03174v1 | http://arxiv.org/pdf/1809.03174v1.pdf | Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder | This paper presents a robust method to monitor heart rate (HR) from BCG
(Ballistocardiography) signal, which is acquired from the sensor embedded in a
chair or a mattress. The proposed algorithm addresses the shortfalls in
traditional Fast Fourier Transform (FFT) based approaches by introducing
Hilbert Transform to extract the pulse envelope that models the repetition of
J-peaks in BCG signal. The frequency resolution is further enhanced by applying
FFT and phase vocoder to the pulse envelope. The performance of the proposed
algorithm is verified by experiment from 7 subjects. For HR estimation, mean
absolute error (MAE) of 0.90 beats per minute (BPM) and standard deviation of
absolute error (STD) of 1.14 BPM are obtained. Pearson correlation coefficient
between estimated HR and ground truth HR of 0.98 is also achieved. | [] | 2018-09-10 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 6.78411007e-01 2.71448176e-02 1.88446954e-01 -1.17109790e-01
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1149f575-1262-46bd-bac6-d9682c7f980f | uncovering-the-background-induced-bias-in-rgb | 2304.08230 | null | https://arxiv.org/abs/2304.08230v1 | https://arxiv.org/pdf/2304.08230v1.pdf | Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation | In recent years, there has been a growing trend of using data-driven methods in industrial settings. These kinds of methods often process video images or parts, therefore the integrity of such images is crucial. Sometimes datasets, e.g. consisting of images, can be sophisticated for various reasons. It becomes critical to understand how the manipulation of video and images can impact the effectiveness of a machine learning method. Our case study aims precisely to analyze the Linemod dataset, considered the state of the art in 6D pose estimation context. That dataset presents images accompanied by ArUco markers; it is evident that such markers will not be available in real-world contexts. We analyze how the presence of the markers affects the pose estimation accuracy, and how this bias may be mitigated through data augmentation and other methods. Our work aims to show how the presence of these markers goes to modify, in the testing phase, the effectiveness of the deep learning method used. In particular, we will demonstrate, through the tool of saliency maps, how the focus of the neural network is captured in part by these ArUco markers. Finally, a new dataset, obtained by applying geometric tools to Linemod, will be proposed in order to demonstrate our hypothesis and uncovering the bias. Our results demonstrate the potential for bias in 6DOF pose estimation networks, and suggest methods for reducing this bias when training with markers. | ['Marko Bertogna', 'Micaela Verucchi', 'Paola Ardòn', 'Giorgia Franchini', 'Tobia Poppi', 'Carmelo Scribano', 'Davide Sapienza', 'Elena Govi'] | 2023-04-17 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 4.07023579e-01 2.09214211e-01 -4.20174263e-02 -1.31569207e-01
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d0d271e4-4aa2-4c20-b99d-3acfa9cccb59 | an-account-of-opinion-implicatures | 1404.6491 | null | http://arxiv.org/abs/1404.6491v1 | http://arxiv.org/pdf/1404.6491v1.pdf | An Account of Opinion Implicatures | While previous sentiment analysis research has concentrated on the
interpretation of explicitly stated opinions and attitudes, this work initiates
the computational study of a type of opinion implicature (i.e.,
opinion-oriented inference) in text. This paper described a rule-based
framework for representing and analyzing opinion implicatures which we hope
will contribute to deeper automatic interpretation of subjective language. In
the course of understanding implicatures, the system recognizes implicit
sentiments (and beliefs) toward various events and entities in the sentence,
often attributed to different sources (holders) and of mixed polarities; thus,
it produces a richer interpretation than is typical in opinion analysis. | ['Lingjia Deng', 'Janyce Wiebe'] | 2014-04-23 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [ 1.01520315e-01 7.28959858e-01 -4.37133104e-01 -8.82080197e-01
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956cac1b-0d3c-4daf-a943-248a2a5a8afc | improving-seasonal-forecast-using | 2010.14610 | null | https://arxiv.org/abs/2010.14610v1 | https://arxiv.org/pdf/2010.14610v1.pdf | Improving seasonal forecast using probabilistic deep learning | The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in quasi-global seasonal forecast of precipitation and near-surface temperature. We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system. We introduce the saliency analysis approach to efficiently identify the key predictors that influence seasonal variability. Furthermore, by explicitly modeling uncertainty using variational Bayes, we give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability. | ['Jiwoo Lee', 'CEline J. W. Bonfils', 'Donald D. Lucas', 'Andre Goncalves', 'Gemma J. Anderson', 'Baoxiang Pan'] | 2020-10-27 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.11826050e-01 -2.01450542e-01 -1.94299184e-02 -3.84263903e-01
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5eace04d-6def-4b4e-a004-34552d61c33f | using-natural-language-and-program | 2205.11558 | null | https://arxiv.org/abs/2205.11558v3 | https://arxiv.org/pdf/2205.11558v3.pdf | Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key. | ['Thomas L. Griffiths', 'Karthik Narasimhan', 'Jonathan D. Cohen', 'Nathaniel D. Daw', 'Robert D. Hawkins', 'Michael Y. Hu', 'Raja Marjieh', 'Ishita Dasgupta', 'Carlos G. Correa', 'Sreejan Kumar'] | 2022-05-23 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.07328185e-01 6.86145663e-01 -4.57462847e-01 -5.26572049e-01
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bfb4a2b7-a303-4442-a59a-429ddd9dd6f6 | text-aware-single-image-specular-highlight | 2108.06881 | null | https://arxiv.org/abs/2108.06881v1 | https://arxiv.org/pdf/2108.06881v1.pdf | Text-Aware Single Image Specular Highlight Removal | Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. Therefore, in this paper, we first raise and study the text-aware single image specular highlight removal problem. The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images. To tackle this challenging problem, we first collect three high-quality datasets with fine-grained annotations, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-stage network, which contains a highlight detection network and a highlight removal network. The output of highlight detection network provides additional information about highlight regions to guide the subsequent highlight removal network. Moreover, we suggest a measurement set including the end-to-end text detection and recognition evaluation and auxiliary visual quality evaluation. Extensive experiments on our collected datasets demonstrate the superior performance of the proposed method. | ['Dong-Ming Yan', 'Jingen Jiang', 'Weize Quan', 'Chaoqun Wang', 'Shiyu Hou'] | 2021-08-16 | null | null | null | null | ['highlight-detection', 'highlight-removal'] | ['computer-vision', 'computer-vision'] | [ 7.19110668e-01 -6.55193269e-01 1.53039679e-01 -2.57596403e-01
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0106d422-f9e9-405d-ada9-e2fed8d3d304 | audiovisual-transfer-learning-for-audio | 2106.05408 | null | https://arxiv.org/abs/2106.05408v1 | https://arxiv.org/pdf/2106.05408v1.pdf | Audiovisual transfer learning for audio tagging and sound event detection | We study the merit of transfer learning for two sound recognition problems, i.e., audio tagging and sound event detection. Employing feature fusion, we adapt a baseline system utilizing only spectral acoustic inputs to also make use of pretrained auditory and visual features, extracted from networks built for different tasks and trained with external data. We perform experiments with these modified models on an audiovisual multi-label data set, of which the training partition contains a large number of unlabeled samples and a smaller amount of clips with weak annotations, indicating the clip-level presence of 10 sound categories without specifying the temporal boundaries of the active auditory events. For clip-based audio tagging, this transfer learning method grants marked improvements. Addition of the visual modality on top of audio also proves to be advantageous in this context. When it comes to generating transcriptions of audio recordings, the benefit of pretrained features depends on the requested temporal resolution: for coarse-grained sound event detection, their utility remains notable. But when more fine-grained predictions are required, performance gains are strongly reduced due to a mismatch between the problem at hand and the goals of the models from which the pretrained vectors were obtained. | ['Hugo Van hamme', 'Wim Boes'] | 2021-06-09 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 4.43749905e-01 -1.50801808e-01 2.63165414e-01 -3.03314626e-01
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26f24fd8-f0c0-4958-a4d4-80978956b565 | a-novel-sleep-stage-classification-using-cnn | 2110.15277 | null | https://arxiv.org/abs/2110.15277v3 | https://arxiv.org/pdf/2110.15277v3.pdf | A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick | With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too. | ['Adam Slowik', 'Ziming Yuan', 'Yu Xue'] | 2021-10-27 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [-9.18138176e-02 -4.02622968e-01 2.53922269e-02 -2.76373953e-01
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49de5ced-21cf-4efa-a335-ec642328b46f | fast-video-salient-object-detection-via | 2010.10027 | null | https://arxiv.org/abs/2010.10027v2 | https://arxiv.org/pdf/2010.10027v2.pdf | Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation | Since the wide employment of deep learning frameworks in video salient object detection, the accuracy of the recent approaches has made stunning progress. These approaches mainly adopt the sequential modules, based on optical flow or recurrent neural network (RNN), to learn robust spatiotemporal features. These modules are effective but significantly increase the computational burden of the corresponding deep models. In this paper, to simplify the network and maintain the accuracy, we present a lightweight network tailored for video salient object detection through the spatiotemporal knowledge distillation. Specifically, in the spatial aspect, we combine a saliency guidance feature embedding structure and spatial knowledge distillation to refine the spatial features. In the temporal aspect, we propose a temporal knowledge distillation strategy, which allows the network to learn the robust temporal features through the infer-frame feature encoding and distilling information from adjacent frames. The experiments on widely used video datasets (e.g., DAVIS, DAVSOD, SegTrack-V2) prove that our approach achieves competitive performance. Furthermore, without the employment of the complex sequential modules, the proposed network can obtain high efficiency with 0.01s per frame. | ['Wenbin Zou', 'Yuanman Li', 'Yi Tang'] | 2020-10-20 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [-1.32314295e-01 -2.15257376e-01 -3.28495950e-01 -2.44102161e-03
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3db1fe53-18ce-4c93-b0b7-028399c8f96c | social-media-personal-event-notifier-using | 2210.05001 | null | https://arxiv.org/abs/2210.05001v1 | https://arxiv.org/pdf/2210.05001v1.pdf | Social Media Personal Event Notifier Using NLP and Machine Learning | Social media apps have become very promising and omnipresent in daily life. Most social media apps are used to deliver vital information to those nearby and far away. As our lives become more hectic, many of us strive to limit our usage of social media apps because they are too addictive, and the majority of us have gotten preoccupied with our daily lives. Because of this, we frequently overlook crucial information, such as invitations to weddings, interviews, birthday parties, etc., or find ourselves unable to attend the event. In most cases, this happens because users are more likely to discover the invitation or information only before the event, giving them little time to prepare. To solve this issue, in this study, we created a system that will collect social media chat and filter it using Natural Language Processing (NLP) methods like Tokenization, Stop Words Removal, Lemmatization, Segmentation, and Named Entity Recognition (NER). Also, Machine Learning Algorithms such as K-Nearest Neighbor (KNN) Algorithm are implemented to prioritize the received invitation and to sort the level of priority. Finally, a customized notification will be delivered to the users where they acknowledge the upcoming event. So, the chances of missing the event are less or can be planned. | ['Vetriselvi A', 'Ashwin Kumar BR', 'Sharan Padmanabhan', 'Pavithiran G'] | 2022-10-10 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-1.11458264e-01 1.58666596e-01 -2.85773665e-01 -4.03379947e-01
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4ce6269a-52ef-4c9a-993e-b0d54422938d | cross-domain-collaborative-learning-for | 2305.08078 | null | https://arxiv.org/abs/2305.08078v1 | https://arxiv.org/pdf/2305.08078v1.pdf | Cross-domain Collaborative Learning for Recognizing Multiple Retinal Diseases from Wide-Field Fundus Images | This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective reuse of existing labeled color fundus photo (CFP) data, we propose Cross-domain Collaborative Learning (CdCL). Inspired by the success of fixed-ratio based mixup in unsupervised domain adaptation, we re-purpose this strategy for the current task. Due to the intrinsic disparity between the field-of-view of CFP and WF/UWF images, a scale bias naturally exists in a mixup sample that the anatomic structure from a CFP image will be considerably larger than its WF/UWF counterpart. The CdCL method resolves the issue by Scale-bias Correction, which employs Transformers for producing scale-invariant features. As demonstrated by extensive experiments on multiple datasets covering both WF and UWF images, the proposed method compares favorably against a number of competitive baselines. | ['Xirong Li', 'Dayong Ding', 'Youxin Chen', 'Niranchana Manivannan', 'Sheng Yang', 'Xinyu Zhao', 'Jianchun Zhao', 'Jinrui Wang', 'Bo wang', 'Jingyuan Yang', 'Qijie Wei'] | 2023-05-14 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 2.76378900e-01 -2.29085572e-02 -3.17065269e-01 -3.56246918e-01
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edd388f1-0913-4c2b-bfa4-a2c911da5b1a | predictive-coding-based-deep-dynamic-neural | 1706.02444 | null | http://arxiv.org/abs/1706.02444v1 | http://arxiv.org/pdf/1706.02444v1.pdf | Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning | This study presents a dynamic neural network model based on the predictive
coding framework for perceiving and predicting the dynamic visuo-proprioceptive
patterns. In our previous study [1], we have shown that the deep dynamic neural
network model was able to coordinate visual perception and action generation in
a seamless manner. In the current study, we extended the previous model under
the predictive coding framework to endow the model with a capability of
perceiving and predicting dynamic visuo-proprioceptive patterns as well as a
capability of inferring intention behind the perceived visuomotor information
through minimizing prediction error. A set of synthetic experiments were
conducted in which a robot learned to imitate the gestures of another robot in
a simulation environment. The experimental results showed that with given
intention states, the model was able to mentally simulate the possible incoming
dynamic visuo-proprioceptive patterns in a top-down process without the inputs
from the external environment. Moreover, the results highlighted the role of
minimizing prediction error in inferring underlying intention of the perceived
visuo-proprioceptive patterns, supporting the predictive coding account of the
mirror neuron systems. The results also revealed that minimizing prediction
error in one modality induced the recall of the corresponding representation of
another modality acquired during the consolidative learning of raw-level
visuo-proprioceptive patterns. | ['Jungsik Hwang', 'Minkyu Choi', 'Jinhyung Kim', 'Ahmadreza Ahmadi', 'Jun Tani'] | 2017-06-08 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 3.96091253e-01 3.82221282e-01 2.17383616e-02 1.59291048e-02
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24632c69-f528-412d-9457-aedd2cb6b127 | trading-quality-for-efficiency-of-graph | null | null | https://openreview.net/forum?id=e6MWIbNeW1 | https://openreview.net/pdf?id=e6MWIbNeW1 | Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs | Many applications of network systems can be formulated as several NP-hard combinatorial optimization problems regarding graph partitioning (GP), e.g., modularity maximization and NCut minimization. Due to the NP-hardness, to balance the quality and efficiency of GP remains a challenge. Existing methods use machine learning techniques to obtain high-quality solutions but usually have high complexity. Some fast GP methods adopt heuristic strategies to ensure low runtime but suffer from quality degradation. In contrast to conventional transductive GP methods applied to a static graph, we propose an inductive graph partitioning (IGP) framework across multiple evolving graph snapshots to alleviate the NP-hard challenge. IGP first conducts the offline training of a novel dual graph neural network on historical snapshots to capture the structural properties of a system. The trained model is then generalized to newly generated snapshots for fast high-quality online GP without additional optimization, where a better trade-off between quality and efficiency is achieved. IGP is also a generic framework that can capture the permutation invariant partitioning ground-truth of historical snapshots in the offline training and tackle the online GP on graphs with non-fixed number of nodes and clusters. Experiments on a set of benchmarks demonstrate that IGP achieves competitive quality and efficiency to various state-of-the-art baselines. | ['Dit-yan Yeung', 'Gong Zhang', 'Bo Bai', 'Chaorui Zhang', 'Meng Qin'] | 2021-09-29 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.42620052e-02 2.43439436e-01 -2.34458402e-01 1.02125280e-01
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10699954-3d9e-494e-9fd2-e2d4dd039f75 | equivalence-of-dataflow-graphs-via-rewrite | 2002.06799 | null | https://arxiv.org/abs/2002.06799v2 | https://arxiv.org/pdf/2002.06799v2.pdf | Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model | In this work we target the problem of provably computing the equivalence between two programs represented as dataflow graphs. To this end, we formalize the problem of equivalence between two programs as finding a set of semantics-preserving rewrite rules from one into the other, such that after the rewrite the two programs are structurally identical, and therefore trivially equivalent. We then develop the first graph-to-sequence neural network system for program equivalence, trained to produce such rewrite sequences from a carefully crafted automatic example generation algorithm. We extensively evaluate our system on a rich multi-type linear algebra expression language, using arbitrary combinations of 100+ graph-rewriting axioms of equivalence. Our system outputs via inference a correct rewrite sequence for 96% of the 10,000 program pairs isolated for testing, using 30-term programs. And in all cases, the validity of the sequence produced and therefore the provable assertion of program equivalence is computable, in negligible time. | ['Louis-Noël Pouchet', 'Théo Barollet', 'Steve Kommrusch'] | 2020-02-17 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 5.26871800e-01 3.82086903e-01 -3.15916777e-01 -3.09017152e-01
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dce24ac1-dc86-424d-be38-ea0673e4c0df | mick-a-meta-learning-framework-for-few-shot | 2004.14164 | null | https://arxiv.org/abs/2004.14164v2 | https://arxiv.org/pdf/2004.14164v2.pdf | MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data | Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given small training data, and achieves competitive results with sufficiently large training data. | ['Yinggong Zhao', 'Libin Shen', 'Xiaoqing Geng', 'Xiwen Chen', 'Kenny Q. Zhu'] | 2020-04-26 | null | null | null | null | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 4.87952381e-01 5.89222550e-01 -9.25446868e-01 -4.92377371e-01
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ef71eda7-097c-448f-9739-4f77cb322941 | attention-based-open-ran-slice-management | 2306.09490 | null | https://arxiv.org/abs/2306.09490v1 | https://arxiv.org/pdf/2306.09490v1.pdf | Attention-based Open RAN Slice Management using Deep Reinforcement Learning | As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods. | ['Jonathan Ashdown', 'Fatemeh Afghah', 'Fatemeh Lotfi'] | 2023-06-15 | null | null | null | null | ['management'] | ['miscellaneous'] | [-3.75178993e-01 5.10440730e-02 -8.24716747e-01 -5.69882929e-01
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d6483444-d1e8-4ce7-aec0-949530cb82da | deep-learning-for-table-detection-and | 2211.08469 | null | https://arxiv.org/abs/2211.08469v1 | https://arxiv.org/pdf/2211.08469v1.pdf | Deep learning for table detection and structure recognition: A survey | Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition. | ['Islam Taj-Eddin', 'Daniyar Nurseitov', 'Mohamed Hamada', 'Mohamed Mahmoud', 'Mahmoud Abdalla', 'Ebrahem Elkady', 'Alexander Berendeyev', 'Abdelrahman Abdallah', 'Mahmoud Kasem'] | 2022-11-15 | null | null | null | null | ['table-recognition', 'table-detection', 'table-extraction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [-1.43195257e-01 -1.84010595e-01 -4.53957528e-01 -2.30697632e-01
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696eccdf-172b-495c-92ac-dfda7a07f265 | diffusion-transport-alignment | 2206.07305 | null | https://arxiv.org/abs/2206.07305v1 | https://arxiv.org/pdf/2206.07305v1.pdf | Diffusion Transport Alignment | The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domains of the entire dataset, which may be unrealistic. Furthermore, existing manifold alignment methods are not suited for cases where the data contains domain-specific regions, i.e., there is not a counterpart for a certain portion of the data in the other domain. We propose Diffusion Transport Alignment (DTA), a semi-supervised manifold alignment method that exploits prior correspondence knowledge between only a few points to align the domains. By building a diffusion process, DTA finds a transportation plan between data measured from two heterogeneous domains with different feature spaces, which by assumption, share a similar geometrical structure coming from the same underlying data generating process. DTA can also compute a partial alignment in a data-driven fashion, resulting in accurate alignments when some data are measured in only one domain. We empirically demonstrate that DTA outperforms other methods in aligning multimodal data in this semisupervised setting. We also empirically show that the alignment obtained by DTA can improve the performance of machine learning tasks, such as domain adaptation, inter-domain feature mapping, and exploratory data analysis, while outperforming competing methods. | ['Kevin R. Moon', 'Guy Wolf', 'Andres F. Duque'] | 2022-06-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 1.56749457e-01 -1.01624802e-01 -2.12853357e-01 -2.63230443e-01
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030c6599-2ae6-4b90-9c1b-51b7bfd25123 | medical-knowledge-guided-deep-learning-for | 2111.10620 | null | https://arxiv.org/abs/2111.10620v2 | https://arxiv.org/pdf/2111.10620v2.pdf | Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification | Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional middle classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. All codes of this work will be publicly available upon acceptance of the paper. | ['Shandong Wu', 'Margarita L. Zuley', 'Ashok Panigrahy', 'Dooman Arefan', 'Chang Liu', 'Long Gao'] | 2021-11-20 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 4.33947325e-01 1.65959194e-01 -5.41221797e-01 -7.53891826e-01
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4d95d1d6-8161-4653-aabd-b02431e1c7b6 | training-compact-neural-networks-via | 1909.02214 | null | https://arxiv.org/abs/1909.02214v2 | https://arxiv.org/pdf/1909.02214v2.pdf | Auxiliary Learning for Deep Multi-task Learning | Multi-task learning (MTL) is an efficient solution to solve multiple tasks simultaneously in order to get better speed and performance than handling each single-task in turn. The most current methods can be categorized as either: (i) hard parameter sharing where a subset of the parameters is shared among tasks while other parameters are task-specific; or (ii) soft parameter sharing where all parameters are task-specific but they are jointly regularized. Both methods suffer from limitations: the shared hidden layers of the former are difficult to optimize due to the competing objectives while the complexity of the latter grows linearly with the increasing number of tasks. To mitigate those drawbacks, this paper proposes an alternative, where we explicitly construct an auxiliary module to mimic the soft parameter sharing for assisting the optimization of the hard parameter sharing layers in the training phase. In particular, the auxiliary module takes the outputs of the shared hidden layers as inputs and is supervised by the auxiliary task loss. During training, the auxiliary module is jointly optimized with the MTL network, serving as a regularization by introducing an inductive bias to the shared layers. In the testing phase, only the original MTL network is kept. Thus our method avoids the limitation of both categories. We evaluate the proposed auxiliary module on pixel-wise prediction tasks, including semantic segmentation, depth estimation, and surface normal prediction with different network structures. The extensive experiments over various settings verify the effectiveness of our methods. | ['Wei Yin', 'Chunhua Shen', 'Yifan Liu', 'Hao Chen', 'Bohan Zhuang'] | 2019-09-05 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.64529610e-01 2.97896326e-01 -2.29690999e-01 -3.22178960e-01
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af8b8310-a2da-41f3-86ad-644c51ad17d2 | cross-category-video-highlight-detection-via | 2108.11770 | null | https://arxiv.org/abs/2108.11770v1 | https://arxiv.org/pdf/2108.11770v1.pdf | Cross-category Video Highlight Detection via Set-based Learning | Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the situation where highlight annotations are not available on the target video category used in practice, while the supervision on another video category (named as source video category) is achievable. In such a situation, one can derive an effective highlight detector on target video category by transferring the highlight knowledge acquired from source video category to the target one. We call this problem cross-category video highlight detection, which has been rarely studied in previous works. For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework. Under this framework, we first design a Set-based Learning module (SL-module) to improve the conventional pair-based learning by assessing the highlight extent of a video segment under a broader context. Based on such learning manner, we introduce two different learners to acquire the basic distinction of target category videos and the characteristics of highlight moments on source video category, respectively. These two types of highlight knowledge are further consolidated via knowledge distillation. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed SL-module, and the DL-VHD method outperforms five typical Unsupervised Domain Adaptation (UDA) algorithms on various cross-category highlight detection tasks. Our code is available at https://github.com/ChrisAllenMing/Cross_Category_Video_Highlight . | ['Changhu Wang', 'Zhenbang Sun', 'Riheng Zhu', 'Bingbing Ni', 'Hang Wang', 'Minghao Xu'] | 2021-08-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Cross-Category_Video_Highlight_Detection_via_Set-Based_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Cross-Category_Video_Highlight_Detection_via_Set-Based_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 2.70506710e-01 -2.74860084e-01 -3.28720182e-01 -7.00968131e-02
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0bf71363-1007-401e-b304-df5b37ac974e | efficient-and-accurate-scene-text-detection | 2306.15142 | null | https://arxiv.org/abs/2306.15142v1 | https://arxiv.org/pdf/2306.15142v1.pdf | Efficient and Accurate Scene Text Detection with Low-Rank Approximation Network | Recently, regression-based methods, which predict parameter curves for localizing texts, are popular in scene text detection. However, these methods struggle to balance concise structure and fast post-processing, and the existing parameter curves are still not ideal for modeling arbitrary-shaped texts, leading to a challenge in balancing speed and accuracy. To tackle these challenges, we firstly propose a dual matching scheme for positive samples, which accelerates inference speed through sparse matching scheme and accelerates model convergence through dense matching scheme. Then, we propose a novel text contour representation method based on low-rank approximation by exploiting the shape correlation between different text contours, which is complete, compact, simplicity and robustness. Based on these designs, we implement an efficient and accurate arbitrary-shaped text detector, named LRANet. Extensive experiments are conducted on three challenging datasets, which demonstrate the accuracy and efficiency of our LRANet over state-of-the-art methods. The code will be released soon. | ['Yuchen Su'] | 2023-06-27 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.34036252e-02 -6.42507195e-01 -2.07109600e-01 -7.36874193e-02
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6fb7ecb7-d66f-473b-a309-ad6e023ce370 | textsc-ambipun-generating-humorous-puns-with | null | null | https://openreview.net/forum?id=MXqSsBbZkF- | https://openreview.net/pdf?id=MXqSsBbZkF- | $\textsc{AmbiPun}$: Generating Humorous Puns with Ambiguous Context | In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52\% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 3.01369458e-01 -7.73051307e-02 1.35074677e-02 7.20536560e-02
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f2921773-a6d0-4cd8-a0f4-f239273c84f0 | supervised-classification-based-stock | 1406.0824 | null | http://arxiv.org/abs/1406.0824v1 | http://arxiv.org/pdf/1406.0824v1.pdf | Supervised classification-based stock prediction and portfolio optimization | As the number of publicly traded companies as well as the amount of their
financial data grows rapidly, it is highly desired to have tracking, analysis,
and eventually stock selections automated. There have been few works focusing
on estimating the stock prices of individual companies. However, many of those
have worked with very small number of financial parameters. In this work, we
apply machine learning techniques to address automated stock picking, while
using a larger number of financial parameters for individual companies than the
previous studies. Our approaches are based on the supervision of prediction
parameters using company fundamentals, time-series properties, and correlation
information between different stocks. We examine a variety of supervised
learning techniques and found that using stock fundamentals is a useful
approach for the classification problem, when combined with the high
dimensional data handling capabilities of support vector machine. The portfolio
our system suggests by predicting the behavior of stocks results in a 3% larger
growth on average than the overall market within a 3-month time period, as the
out-of-sample test suggests. | ['Adam Goldberg', 'Sercan Arik', 'Sukru Burc Eryilmaz'] | 2014-06-03 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.63798630e-01 -3.84990513e-01 -4.38003093e-01 -2.37950623e-01
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87a1a657-c381-483e-b93d-e518dcb28cdd | complementary-intrinsics-from-neural-radiance | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.pdf | Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting | Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multi-view stereo. With neural radiance fields (NeRFs), editing the appearance code could produce more realistic results without explicitly interpreting the outdoor scene image formation. This paper proposes to complement the intrinsic estimation from volume rendering using NeRFs and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes. | ['Boxin Shi', 'Zhaofei Yu', 'Si Li', 'Jiajun Tang', 'Yongjie Zhu', 'Xuanning Cui', 'Siqi Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.25907362e-01 2.11474016e-01 6.23711765e-01 -7.49016762e-01
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22a93f28-fb76-490c-8701-62b14a92098c | write-a-speaker-text-based-emotional-and | 2104.07995 | null | https://arxiv.org/abs/2104.07995v2 | https://arxiv.org/pdf/2104.07995v2.pdf | Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation | In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage. In the speaker-independent stage, we design three parallel networks to generate animation parameters of the mouth, upper face, and head from texts, separately. In the speaker-specific stage, we present a 3D face model guided attention network to synthesize videos tailored for different individuals. It takes the animation parameters as input and exploits an attention mask to manipulate facial expression changes for the input individuals. Furthermore, to better establish authentic correspondences between visual motions (i.e., facial expression changes and head movements) and audios, we leverage a high-accuracy motion capture dataset instead of relying on long videos of specific individuals. After attaining the visual and audio correspondences, we can effectively train our network in an end-to-end fashion. Extensive experiments on qualitative and quantitative results demonstrate that our algorithm achieves high-quality photo-realistic talking-head videos including various facial expressions and head motions according to speech rhythms and outperforms the state-of-the-art. | ['Suzhen Wang', 'Lincheng Li', 'Changjie Fan', 'Xin Yu', 'Yixing Zheng', 'Yu Ding', 'Zhimeng Zhang'] | 2021-04-16 | null | null | null | null | ['talking-head-generation', 'face-model'] | ['computer-vision', 'computer-vision'] | [-9.42532420e-02 1.16708688e-05 7.95953721e-02 -5.72554886e-01
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4054d52c-ddbc-491a-9f5b-d85e52dc653a | frenchmedmcqa-a-french-multiple-choice-1 | 2304.04280 | null | https://arxiv.org/abs/2304.04280v1 | https://arxiv.org/pdf/2304.04280v1.pdf | FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain | This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online. | ['Pierre-Antoine Gourraud', 'Béatrice Daille', 'Emmanuel Morin', 'Mickael Rouvier', 'Richard Dufour', 'Adrien Bazoge', 'Yanis Labrak'] | 2023-04-09 | frenchmedmcqa-a-french-multiple-choice | https://hal.archives-ouvertes.fr/hal-03824241v1 | https://hal.archives-ouvertes.fr/hal-03824241/document | louhi-2022-10 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 2.70756572e-01 5.89709997e-01 2.46709466e-01 -5.19030094e-01
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ea4d9434-da3d-40c2-9819-8663885678f7 | reinforcement-learning-for-datacenter | 2102.09337 | null | https://arxiv.org/abs/2102.09337v2 | https://arxiv.org/pdf/2102.09337v2.pdf | Reinforcement Learning for Datacenter Congestion Control | We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world. | ['Shie Mannor', 'Gal Chechik', 'Benjamin Fuhrer', 'Doron Haritan Kazakov', 'Amit Mandelbaum', 'Gal Dalal', 'Yuval Shpigelman', 'Chen Tessler'] | 2021-02-18 | null | null | null | null | ['network-congestion-control'] | ['miscellaneous'] | [-4.42740947e-01 -5.94223924e-02 -4.55106378e-01 -7.52519220e-02
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55de216b-0756-485a-9743-ed8dcbc30084 | adversarial-learning-semantic-volume-for-2d | null | null | https://openreview.net/pdf?id=gafjGfv8uR | https://openreview.net/pdf?id=gafjGfv8uR | Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild | Regression-based methods have revolutionized 2D landmark localization with the exploitation of deep neural networks and massive annotated datasets in the wild. However, it remains challenging for 3D landmark localization due to the lack of annotated datasets and the ambiguous nature of landmarks under the 3D perspective. This paper revisits regression-based methods and proposes an adversarial voxel and coordinate regression framework for 2D and 3D facial landmark localization in real-world scenarios. First, a semantic volumetric representation is introduced to encode the per-voxel likelihood of positions being the 3D landmarks. Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector. Such a pipeline not only enhances the robustness and accuracy of the predictions but also unifies the 2D and 3D landmark localization so that the 2D and 3D datasets could be utilized simultaneously. Further, an adversarial learning strategy is exploited to distill 3D structure learned from synthetic datasets to real-world datasets under weakly supervised settings, where an auxiliary regression discriminator is proposed to encourage the network to produce plausible predictions for both the synthetic and real-world images. The effectiveness of our method is validated on benchmark datasets 3DFAW and AFLW2000-3D for both 2D and 3D facial landmark localization tasks. The experimental results show that the proposed method achieves significant improvements over the previous state-of-the-art methods. | ['Zhenan Sun', 'Qi Li', 'Hongwen Zhang'] | 2019-04-19 | null | null | null | ieee-transactions-on-image-processing-2019-4 | ['face-alignment', '3d-facial-landmark-localization'] | ['computer-vision', 'computer-vision'] | [-1.59594551e-01 3.54862362e-01 -2.48512238e-01 -6.07443452e-01
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42b49029-098f-4ee3-892b-eadbcbf13850 | bistnet-semantic-image-prior-guided | 2212.02268 | null | https://arxiv.org/abs/2212.02268v1 | https://arxiv.org/pdf/2212.02268v1.pdf | BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization | How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an effective BiSTNet to explore colors of reference exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the reference exemplars in deep feature space to explore color information from reference exemplars. Then, to better propagate the colors of reference exemplars into each frame and avoid the inaccurate matches colors from exemplars we develop a simple yet effective bidirectional temporal feature fusion module to better colorize each frame. We note that there usually exist color-bleeding artifacts around the boundaries of the important objects in videos. To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance. In addition, we develop a multi-scale recurrent block to progressively colorize frames in a coarse-to-fine manner. Extensive experimental results demonstrate that the proposed BiSTNet performs favorably against state-of-the-art methods on the benchmark datasets. Our code will be made available at \url{https://yyang181.github.io/BiSTNet/} | ['Jinshan Pan', 'Jinhui Tang', 'Zhulin Tao', 'Xiaoyu Du', 'Zhongzheng Peng', 'Yixin Yang'] | 2022-12-05 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.14951812e-01 -6.99713886e-01 3.93260159e-02 -1.63147271e-01
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4cad60af-ce4a-45cc-9a01-005b5de05277 | masked-multi-step-probabilistic-forecasting | 2302.06818 | null | https://arxiv.org/abs/2302.06818v1 | https://arxiv.org/pdf/2302.06818v1.pdf | Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand | Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1) sample-based approaches, where each forecast is made independently, and (2) time series regression approaches, where some historical load and other feature information is used. When making a short-to-mid-term electricity demand forecast, some future information is available, such as the weather forecast and calendar variables. However, in existing forecasting models this future information is not fully incorporated. To overcome this limitation of existing approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting (MMMPF), a novel and general framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make probabilistic predictions. Experiments are performed on a real-world dataset for short-to-mid-term electricity demand forecasting for multiple regions and compared with various ML methods. They show that the proposed MMMPF framework outperforms not only sample-based methods but also existing time-series forecasting models with the exact same base models. Models trainded with MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future. | ['Honggang Wang', 'Nurali Virani', 'Yiwei Fu'] | 2023-02-14 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-3.02854359e-01 -2.70291507e-01 -2.76502192e-01 -8.38187933e-01
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d7c7343b-7b41-4b0b-82a4-826585f24fcd | scicml-information-theoretic-co-clustering | 2205.09523 | null | https://arxiv.org/abs/2205.09523v1 | https://arxiv.org/pdf/2205.09523v1.pdf | scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data | Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to assay celluar heterogeneity from multiple biological layers. However, the datasets generated from these technologies tend to have high level of noise and are highly sparse, bringing challenges to data analysis. In this paper, we develop a novel information-theoretic co-clustering-based multi-view learning (scICML) method for multi-omics single-cell data integration. scICML utilizes co-clusterings to aggregate similar features for each view of data and uncover the common clustering pattern for cells. In addition, scICML automatically matches the clusters of the linked features across different data types for considering the biological dependency structure across different types of genomic features. Our experiments on four real-world datasets demonstrate that scICML improves the overall clustering performance and provides biological insights into the data analysis of peripheral blood mononuclear cells. | ['Zhixiang Lin', 'Pengcheng Zeng'] | 2022-05-19 | null | null | null | null | ['multi-view-learning', 'data-integration'] | ['computer-vision', 'knowledge-base'] | [-3.97744700e-02 -8.62924337e-01 -3.70646507e-01 -1.82379082e-01
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07348c43-cfa9-4501-8ad7-0f36fb31842d | mgimn-multi-grained-interactive-matching | 2204.04952 | null | https://arxiv.org/abs/2204.04952v3 | https://arxiv.org/pdf/2204.04952v3.pdf | MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification | Text classification struggles to generalize to unseen classes with very few labeled text instances per class. In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results. Previous studies mainly aim to derive a prototype representation for each class. However, they neglect that it is challenging-yet-unnecessary to construct a compact representation which expresses the entire meaning for each class. They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification. To deal with these issues, we propose a meta-learning based method MGIMN which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning. The key of instance-wise comparison is the interactive matching within the class-specific context and episode-specific context. Extensive experiments demonstrate that the proposed method significantly outperforms the existing state-of-the-art approaches, under both the standard FSL and generalized FSL settings. | ['Ji Zhang', 'Yuanhang Zheng', 'Xing Gao', 'Mieradilijiang Maimaiti', 'Jianhai Zhang'] | 2022-04-11 | null | https://aclanthology.org/2022.naacl-main.141 | https://aclanthology.org/2022.naacl-main.141.pdf | naacl-2022-7 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.20347816e-01 -3.40547234e-01 -4.23780978e-01 -7.05203354e-01
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686054a5-009d-4982-9668-bd5be51ebcd2 | refractive-light-field-features-for-curved | 2103.15349 | null | https://arxiv.org/abs/2103.15349v2 | https://arxiv.org/pdf/2103.15349v2.pdf | Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion | Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us to address this challenge by capturing the view-dependent appearance of such objects in a single exposure. We propose a novel image feature for light fields that detects and describes the patterns of light refracted through curved transparent objects. We derive characteristic points based on these features allowing them to be used in place of conventional 2D features. Using our features, we demonstrate improved structure-from-motion performance in challenging scenes containing refractive objects, including quantitative evaluations that show improved camera pose estimates and 3D reconstructions. Additionally, our methods converge 15-35% more frequently than the state-of-the-art. Our method is a critical step towards allowing robots to operate around refractive objects, with applications in manufacturing, quality assurance, pick-and-place, and domestic robots working with acrylic, glass and other transparent materials. | ['Donald G. Dansereau', 'Thierry Peynot', 'Peter Corke', 'Dorian Tsai'] | 2021-03-29 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 3.88519704e-01 -1.31486848e-01 5.33166528e-01 -3.19666266e-01
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0be97c76-0d74-425b-aa16-030d3ea54889 | chatgpt-is-a-remarkable-tool-for-experts | 2306.03102 | null | https://arxiv.org/abs/2306.03102v1 | https://arxiv.org/pdf/2306.03102v1.pdf | ChatGPT is a Remarkable Tool -- For Experts | This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. Furthermore, we highlight the potential risks associated with excessive reliance on ChatGPT in these fields. These limitations encompass factors like incorrect and fictitious responses, inaccuracies in code, limited logical reasoning abilities, overconfidence, and critical ethical concerns of copyrights and privacy violation. We outline areas and objectives where ChatGPT proves beneficial, applications where it should be used judiciously, and scenarios where its reliability may be limited. In light of observed limitations, and given that the tool's fundamental errors may pose a special challenge for non-experts, ChatGPT should be used with a strategic methodology. By drawing from comprehensive experimental studies, we offer methods and flow charts for effectively using ChatGPT. Our recommendations emphasize iterative interaction with ChatGPT and independent verification of its outputs. Considering the importance of utilizing ChatGPT judiciously and with expertise, we recommend its usage for experts who are well-versed in the respective domains. | ['Shulamit Reches', 'Rina Azoulay', 'Amos Azaria'] | 2023-06-02 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 7.79857785e-02 3.87258619e-01 1.50085419e-01 -2.19974190e-01
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b10574dd-ea8f-495b-a36e-735d0d20a01c | constrained-convolutional-neural-networks-a | null | null | https://ieeexplore.ieee.org/abstract/document/8335799 | https://misl.ece.drexel.edu/wp-content/uploads/2018/04/BayarStammTIFS01.pdf | Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection | Identifying the authenticity and processing history
of an image is an important task in multimedia forensics.
By analyzing traces left by different image manipulations, researchers have been able to develop several algorithms capable
of detecting targeted editing operations. While this approach has
led to the development of several successful forensic algorithms,
an important problem remains: creating forensic detectors for
different image manipulations is a difficult and time consuming
process. Furthermore, forensic analysts need ‘general purpose’
forensic algorithms capable of detecting multiple different image
manipulations. In this paper, we address both of these problems
by proposing a new general purpose forensic approach using
convolutional neural networks (CNNs). While CNNs are capable
of learning classification features directly from data, in their
existing form they tend to learn features representative of an
image’s content. To overcome this issue, we have developed
a new type of CNN layer, called a constrained convolutional
layer, that is able to jointly suppress an image’s content and
adaptively learn manipulation detection features. Through a
series of experiments, we show that our proposed constrained
CNN is able to learn manipulation detection features directly
from data. Our experimental results demonstrate that our CNN
can detect multiple different editing operations with up to 99.97%
accuracy and outperform the existing state-of-the-art general
purpose manipulation detector. Furthermore, our constrained
CNN can still accurately detect image manipulations in realistic
scenarios where there is a source camera model mismatch
between the training and testing data | ['Matthew C. Stamm', 'Belhassen Bayar'] | 2018-04-11 | null | null | null | ieee-transactions-on-information-forensics-5 | ['image-manipulation-detection'] | ['computer-vision'] | [ 3.67290318e-01 -6.10193014e-01 1.03879929e-01 -1.11051731e-01
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03217aea-6324-42e5-8e9a-3d09bf360b6f | low-resource-neural-machine-translation-a-1 | null | null | https://aclanthology.org/2022.vardial-1.4 | https://aclanthology.org/2022.vardial-1.4.pdf | Low-Resource Neural Machine Translation: A Case Study of Cantonese | The development of Natural Language Processing (NLP) applications for Cantonese, a language with over 85 million speakers, is lagging compared to other languages with a similar number of speakers. In this paper, we present, to our best knowledge, the first benchmark of multiple neural machine translation (NMT) systems from Mandarin Chinese to Cantonese. Additionally, we performed parallel sentence mining (PSM) as data augmentation for the extremely low resource language pair and increased the number of sentence pairs from 1,002 to 35,877. Results show that with PSM, the best performing model (BPE-level bidirectional LSTM) scored 11.98 BLEU better than the vanilla baseline and 9.93 BLEU higher than our strong baseline. Our unsupervised NMT (UNMT) results also refuted previous assumption n (Rubino et al., 2020) that the poor performance was related to the lack of linguistic similarities between the target and source languages, particularly in the case of Cantonese and Mandarin. In the process of building the NMT system, we also created the first large-scale parallel training and evaluation datasets of the language pair. Codes and datasets are publicly available at https://github.com/evelynkyl/yue_nmt. | ['Evelyn Kai-Yan Liu'] | null | null | null | null | vardial-coling-2022-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.53217179e-02 -5.62442169e-02 -2.07671970e-01 -4.27319229e-01
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58c3de40-470a-49d5-bbed-87dd26edb586 | isolation-scheme-for-virtual-network | 2211.14158 | null | https://arxiv.org/abs/2211.14158v2 | https://arxiv.org/pdf/2211.14158v2.pdf | An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning | Virtualization technologies are the foundation of modern ICT infrastructure, enabling service providers to create dedicated virtual networks (VNs) that can support a wide range of smart city applications. These VNs continuously generate massive amounts of data, necessitating stringent reliability and security requirements. In virtualized network environments, however, multiple VNs may coexist on the same physical infrastructure and, if not properly isolated, may interfere with or provide unauthorized access to one another. The former causes performance degradation, while the latter compromises the security of VNs. Service assurance for infrastructure providers becomes significantly more complicated when a specific VN violates the isolation requirement. In an effort to address the isolation issue, this paper proposes isolation during virtual network embedding (VNE), the procedure of allocating VNs onto physical infrastructure. We define a simple abstracted concept of isolation levels to capture the variations in isolation requirements and then formulate isolation-aware VNE as an optimization problem with resource and isolation constraints. A deep reinforcement learning (DRL)-based VNE algorithm ISO-DRL_VNE, is proposed that considers resource and isolation constraints and is compared to the existing three state-of-the-art algorithms: NodeRank, Global Resource Capacity (GRC), and Mote-Carlo Tree Search (MCTS). Evaluation results show that the ISO-DRL_VNE algorithm outperforms others in acceptance ratio, long-term average revenue, and long-term average revenue-to-cost ratio by 6%, 13%, and 15%. | ['Sanghwan Lee', 'Chunming Rong', 'Ali Gohar'] | 2022-11-25 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.86599016e-01 -1.73075214e-01 -4.35728431e-01 3.20930868e-01
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f526d9b4-f4ea-43bf-973f-ffaf74ecb8c0 | a-feature-rich-constituent-context-model-for | null | null | https://aclanthology.info/papers/P12-2004/p12-2004 | https://www.aclweb.org/anthology/P12-2004 | A Feature-Rich Constituent Context Model for Grammar Induction | null | ['Jakob Uszkoreit', 'Dave Golland', 'John DeNero'] | 2012-07-01 | null | https://aclanthology.org/P12-2004 | https://aclanthology.org/P12-2004.pdf | acl-2012-7 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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812653a6-3090-417e-a455-e17ec3eb2fae | appearance-and-structure-aware-robust-deep | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.pdf | Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond | Despite the recent breakthrough of high accuracy deep graph matching (GM) over visual images, the robustness of deep GM models is rarely studied which yet has been revealed an important issue in modern deep nets, ranging from image recognition to graph learning tasks. We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models. Accordingly, we propose our defense strategy, namely Appearance and Structure Aware Robust Graph Matching (ASAR-GM). Specifically, orthogonal to de facto adversarial training (AT), we devise the Appearance Aware Regularizer (AAR) on those appearance-similar keypoints between graphs that are likely to confuse. Experimental results show that our ASAR-GM achieves better robustness compared to AT. Moreover, our locality attack can serve as a data augmentation technique, which boosts the state-of-the-art GM models even on the clean test dataset. Code is available at https://github.com/Thinklab-SJTU/RobustMatch. | ['Junchi Yan', 'Runzhong Wang', 'Qingquan Bao', 'Qibing Ren'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['graph-matching'] | ['graphs'] | [ 2.31656656e-02 2.22153828e-01 -9.52157006e-02 -1.26642764e-01
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3cc433b9-1d1a-4c9f-a728-4a1e35b562f7 | macedonian-speech-synthesis-for-assistive | 2205.09198 | null | https://arxiv.org/abs/2205.09198v2 | https://arxiv.org/pdf/2205.09198v2.pdf | Macedonian Speech Synthesis for Assistive Technology Applications | Speech technology is becoming ever more ubiquitous with the advance of speech enabled devices and services. The use of speech synthesis in Augmentative and Alternative Communication tools, has facilitated inclusion of individuals with speech impediments allowing them to communicate with their surroundings using speech. Although there are numerous speech synthesis systems for the most spoken world languages, there is still a limited offer for smaller languages. We propose and compare three models built using parametric and deep learning techniques for Macedonian trained on a newly recorded corpus. We target low-resource edge deployment for Augmentative and Alternative Communication and assistive technologies, such as communication boards and screen readers. The listening test results show that parametric speech synthesis is as performant compared to the more advanced deep learning models. Since it also requires less resources, and offers full speech rate and pitch control, it is the preferred choice for building a Macedonian TTS system for this application scenario. | ['Branislav Gerazov', 'Dimitar Tashkovski', 'Zoran Ivanovski', 'Toni Bachvarovski', 'Kristijan Lazarev', 'Stefan Janev', 'Risto Chavdarov', 'Tea Veljkovikj', 'Violeta Argirova', 'Martin Velichkovski', 'Elena Velovska', 'Bojan Sofronievski'] | 2022-05-18 | null | null | null | null | ['pitch-control'] | ['audio'] | [-2.08972171e-01 4.43503231e-01 8.55077058e-02 -6.05211668e-02
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c8bd75bd-eab7-4552-bf8b-61389f0e9581 | proxy-indicators-for-the-quality-of-open | null | null | https://aclanthology.org/2021.emnlp-main.618 | https://aclanthology.org/2021.emnlp-main.618.pdf | Proxy Indicators for the Quality of Open-domain Dialogues | The automatic evaluation of open-domain dialogues remains a largely unsolved challenge. Despite the abundance of work done in the field, human judges have to evaluate dialogues’ quality. As a consequence, performing such evaluations at scale is usually expensive. This work investigates using a deep-learning model trained on the General Language Understanding Evaluation (GLUE) benchmark to serve as a quality indication of open-domain dialogues. The aim is to use the various GLUE tasks as different perspectives on judging the quality of conversation, thus reducing the need for additional training data or responses that serve as quality references. Due to this nature, the method can infer various quality metrics and can derive a component-based overall score. We achieve statistically significant correlation coefficients of up to 0.7. | ['Ricardo Usbeck', 'Jens Lehmann', 'Rostislav Nedelchev'] | null | null | null | null | emnlp-2021-11 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-2.33427182e-01 4.83218491e-01 1.39920011e-01 -6.75106406e-01
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fb21b81c-37c0-4799-9d85-770cddbbfe3f | concreteness-and-subjectivity-as-dimensions | null | null | https://aclanthology.org/P14-2118 | https://aclanthology.org/P14-2118.pdf | Concreteness and Subjectivity as Dimensions of Lexical Meaning | null | ['Felix Hill', 'Anna Korhonen'] | 2014-06-01 | null | null | null | acl-2014-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0d3356db-c281-4055-a5bf-efb55ef6032f | resource-constrained-station-keeping-for | 2303.01173 | null | https://arxiv.org/abs/2303.01173v1 | https://arxiv.org/pdf/2303.01173v1.pdf | Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning | High altitude balloons have proved useful for ecological aerial surveys, atmospheric monitoring, and communication relays. However, due to weight and power constraints, there is a need to investigate alternate modes of propulsion to navigate in the stratosphere. Very recently, reinforcement learning has been proposed as a control scheme to maintain the balloon in the region of a fixed location, facilitated through diverse opposing wind-fields at different altitudes. Although air-pump based station keeping has been explored, there is no research on the control problem for venting and ballasting actuated balloons, which is commonly used as a low-cost alternative. We show how reinforcement learning can be used for this type of balloon. Specifically, we use the soft actor-critic algorithm, which on average is able to station-keep within 50\;km for 25\% of the flight, consistent with state-of-the-art. Furthermore, we show that the proposed controller effectively minimises the consumption of resources, thereby supporting long duration flights. We frame the controller as a continuous control reinforcement learning problem, which allows for a more diverse range of trajectories, as opposed to current state-of-the-art work, which uses discrete action spaces. Furthermore, through continuous control, we can make use of larger ascent rates which are not possible using air-pumps. The desired ascent-rate is decoupled into desired altitude and time-factor to provide a more transparent policy, compared to low-level control commands used in previous works. Finally, by applying the equations of motion, we establish appropriate thresholds for venting and ballasting to prevent the agent from exploiting the environment. More specifically, we ensure actions are physically feasible by enforcing constraints on venting and ballasting. | ['Wenbin Li', 'Alan Hunter', 'Özgür Şimşek', 'Loïc Prenevost', 'Jack Saunders'] | 2023-03-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.36554340e-01 8.71523917e-02 -2.00329974e-01 1.96986914e-01
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504839b3-b8c7-488b-89f0-e26e44ac272b | glu-net-global-local-universal-network-for | 1912.05524 | null | https://arxiv.org/abs/1912.05524v3 | https://arxiv.org/pdf/1912.05524v3.pdf | GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences | Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addressed with specialized network architectures, designed for only one particular task. This severely limits the generalization capabilities of such networks to new scenarios, where e.g. robustness to larger displacements or higher accuracy is required. In this work, we propose a universal network architecture that is directly applicable to all the aforementioned dense correspondence problems. We achieve both high accuracy and robustness to large displacements by investigating the combined use of global and local correlation layers. We further propose an adaptive resolution strategy, allowing our network to operate on virtually any input image resolution. The proposed GLU-Net achieves state-of-the-art performance for geometric and semantic matching as well as optical flow, when using the same network and weights. Code and trained models are available at https://github.com/PruneTruong/GLU-Net. | ['Radu Timofte', 'Martin Danelljan', 'Prune Truong'] | 2019-12-11 | glu-net-global-local-universal-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.pdf | cvpr-2020-6 | ['geometric-matching', 'dense-pixel-correspondence-estimation'] | ['computer-vision', 'computer-vision'] | [ 8.40104371e-02 -2.50373542e-01 -1.04666203e-01 -2.27923915e-01
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44e56024-ba73-4823-bdd6-010d0a19ff0c | aboships-an-inshore-and-offshore-maritime | 2102.05869 | null | https://arxiv.org/abs/2102.05869v1 | https://arxiv.org/pdf/2102.05869v1.pdf | ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations | Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we collected a dataset of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and subsequently, we used the CSRT [1] tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN [2], R-FCN [3], SSD [4] and EfficientDet [5]). The algorithms were previously trained on the Microsoft COCO dataset. We compare their accuracy based on feature extractor and object size. Our experiments show that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter. | ['Johan Lilius', 'Luca Zelioli', 'Valentin Soloviev', 'Bogdan Iancu'] | 2021-02-11 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.42790312e-01 -8.35733339e-02 7.01811612e-01 -2.62601525e-01
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31e52af9-19d4-4644-8b26-b0735d1255c2 | domain-adaptive-semantic-segmentation-by | 2303.16435 | null | https://arxiv.org/abs/2303.16435v1 | https://arxiv.org/pdf/2303.16435v1.pdf | Domain Adaptive Semantic Segmentation by Optimal Transport | Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. Therefore, how to use a small size of labeled data to achieve semantic segmentation becomes more and more important. In this paper, we propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue. Concretely, first we generate the output space via CNN due to its superiority of feature representation. Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model. In particular, with OT, the number of network parameters has been reduced and the network has been better interpretable. Third, to better describe the multi-scale property of features, we construct a multi-scale segmentation network to perform domain adaptation. Finally, in order to verify the performance of our proposed method, we conduct experimental comparison with three benchmark and four SOTA methods on three scene datasets, and the mean intersection-over-union (mIOU) has been significant improved, and visualization results under multiple domain adaptation scenarios also show that our proposed method has better performance than compared semantic segmentation methods. | ['Shihui Ying', 'Ce Li', 'Xin Wang', 'Yaqian Guo'] | 2023-03-29 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.27825576e-01 -2.09261760e-01 -2.23968029e-02 -5.81848145e-01
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