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f69e9bdf-baec-4e88-bb0b-37310b4f17f8 | uboco-unsupervised-boundary-contrastive | 2111.14799 | null | https://arxiv.org/abs/2111.14799v2 | https://arxiv.org/pdf/2111.14799v2.pdf | UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection | Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video parsing. Still at an early development stage, existing GEBD solvers are simple extensions of relevant video understanding tasks, disregarding GEBD's distinctive characteristics. In this paper, we propose a novel framework for unsupervised/supervised GEBD, by using the Temporal Self-similarity Matrix (TSM) as the video representation. The new Recursive TSM Parsing (RTP) algorithm exploits local diagonal patterns in TSM to detect boundaries, and it is combined with the Boundary Contrastive (BoCo) loss to train our encoder to generate more informative TSMs. Our framework can be applied to both unsupervised and supervised settings, with both achieving state-of-the-art performance by a huge margin in GEBD benchmark. Especially, our unsupervised method outperforms the previous state-of-the-art "supervised" model, implying its exceptional efficacy. | ['Seon Joo Kim', 'Taehyun Kim', 'Jinwoo Kim', 'Hyolim Kang'] | 2021-11-29 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.53729057e-01 2.19173193e-01 -4.30597603e-01 -4.25822824e-01
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25cc2347-18b9-4273-b9af-7e0d5baf5ede | non-autoregressive-end-to-end-approaches-for | 2304.10869 | null | https://arxiv.org/abs/2304.10869v1 | https://arxiv.org/pdf/2304.10869v1.pdf | Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding | This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks. The proposed NAR systems employ a Conformer encoder that applies connectionist temporal classification (CTC) to transcribe the speech utterance into raw ASR hypotheses, which are further refined with a bidirectional encoder representations from Transformers (BERT)-like decoder. In the meantime, the intent and slot labels of the utterance are predicted simultaneously using the same decoder. Both Mask-CTC and self-conditioned CTC (SC-CTC) approaches are explored for this study. Experiments conducted on the SLURP dataset show that the proposed SC-Mask-CTC NAR system achieves 3.7% and 3.2% absolute gains in SLU metrics and a competitive level of ASR accuracy, when compared to a Conformer-Transformer based autoregressive (AR) model. Additionally, the NAR systems achieve 6x faster decoding speed than the AR baseline. | ['Rama Doddipatla', 'Mohan Li'] | 2023-04-21 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.77942502e-01 2.96628565e-01 -1.76114552e-02 -6.98391020e-01
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5975a77c-642e-473c-848d-7046e037574d | experimenting-with-an-evaluation-framework | 2301.10888 | null | https://arxiv.org/abs/2301.10888v1 | https://arxiv.org/pdf/2301.10888v1.pdf | Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL) | Introduction Data imbalance is one of the crucial issues in big data analysis with fewer labels. For example, in real-world healthcare data, spam detection labels, and financial fraud detection datasets. Many data balance methods were introduced to improve machine learning algorithms' performance. Research claims SMOTE and SMOTE-based data-augmentation (generate new data points) methods could improve algorithm performance. However, we found in many online tutorials, the valuation methods were applied based on synthesized datasets that introduced bias into the evaluation, and the performance got a false improvement. In this study, we proposed, a new evaluation framework for imbalanced data learning methods. We have experimented on five data balance methods and whether the performance of algorithms will improve or not. Methods We collected 8 imbalanced healthcare datasets with different imbalanced rates from different domains. Applied 6 data augmentation methods with 11 machine learning methods testing if the data augmentation will help with improving machine learning performance. We compared the traditional data augmentation evaluation methods with our proposed cross-validation evaluation framework Results Using traditional data augmentation evaluation meta hods will give a false impression of improving the performance. However, our proposed evaluation method shows data augmentation has limited ability to improve the results. Conclusion EFIDL is more suitable for evaluating the prediction performance of an ML method when data are augmented. Using an unsuitable evaluation framework will give false results. Future researchers should consider the evaluation framework we proposed when dealing with augmented datasets. Our experiments showed data augmentation does not help improve ML prediction performance. | ['Xia Jiang', 'Chenyu Li'] | 2023-01-26 | null | null | null | null | ['imbalanced-classification', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [-5.55774681e-02 3.89943361e-01 -3.47691596e-01 -5.61672390e-01
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63d828de-9636-4881-8dac-3a2b0e71af0b | transformer-meets-tracker-exploiting-temporal | 2103.11681 | null | https://arxiv.org/abs/2103.11681v2 | https://arxiv.org/pdf/2103.11681v2.pdf | Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking | In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. In this work, we bridge the individual video frames and explore the temporal contexts across them via a transformer architecture for robust object tracking. Different from classic usage of the transformer in natural language processing tasks, we separate its encoder and decoder into two parallel branches and carefully design them within the Siamese-like tracking pipelines. The transformer encoder promotes the target templates via attention-based feature reinforcement, which benefits the high-quality tracking model generation. The transformer decoder propagates the tracking cues from previous templates to the current frame, which facilitates the object searching process. Our transformer-assisted tracking framework is neat and trained in an end-to-end manner. With the proposed transformer, a simple Siamese matching approach is able to outperform the current top-performing trackers. By combining our transformer with the recent discriminative tracking pipeline, our method sets several new state-of-the-art records on prevalent tracking benchmarks. | ['Houqaing Li', 'Jie Wang', 'Wengang Zhou', 'Ning Wang'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-object-tracking'] | ['computer-vision'] | [ 8.39126259e-02 -3.69849205e-01 -3.02976340e-01 -1.50987148e-01
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fa291ec5-626d-4d34-bf97-3a61bf7712a2 | behavioral-epidemiology-an-economic-model-to | 2202.04174 | null | https://arxiv.org/abs/2202.04174v1 | https://arxiv.org/pdf/2202.04174v1.pdf | Behavioral epidemiology: An economic model to evaluate optimal policy in the midst of a pandemic | This paper combines a canonical epidemiology model of disease dynamics with government policy of lockdown and testing, and agents' decision to social distance in order to avoid getting infected. The model is calibrated with data on deaths and testing outcomes in the Unites States. It is shown that an intermediate but prolonged lockdown is socially optimal when both mortality and GDP are taken into account. This is because the government wants the economy to keep producing some output and the slack in reducing infection is picked up by social distancing agents. Social distancing best responds to the optimal government policy to keep the effective reproductive number at one and avoid multiple waves through the pandemic. Calibration shows testing to have been effective, but it could have been even more instrumental if it had been aggressively pursued from the beginning of the pandemic. Not having any lockdown or shutting down social distancing would have had extreme consequences. Greater centralized control on social activities would have mitigated further the spread of the pandemic. | ['Rohit Lamba', 'Ilia Krasikov', 'Shomak Chakrabarti'] | 2022-02-08 | null | null | null | null | ['epidemiology'] | ['medical'] | [-5.91271818e-02 7.80213833e-01 -2.18582332e-01 8.44059512e-02
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eabe979c-8e98-499e-8899-1b5ceb331916 | memory-consistent-unsupervised-off-the-shelf | 2209.07910 | null | https://arxiv.org/abs/2209.07910v1 | https://arxiv.org/pdf/2209.07910v1.pdf | Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation | Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is typically jointly trained on data from both domains, accessing the labeled source domain data is often restricted, due to concerns over patient data privacy or intellectual property. To sidestep this, we propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation. Toward this goal, we aim to develop a novel batch-wise normalization (BN) statistics adaptation framework. In particular, we gradually adapt the domain-specific low-order BN statistics, e.g., mean and variance, through an exponential momentum decay strategy, while explicitly enforcing the consistency of the domain shareable high-order BN statistics, e.g., scaling and shifting factors, via our optimization objective. We also adaptively quantify the channel-wise transferability to gauge the importance of each channel, via both low-order statistics divergence and a scaling factor.~Furthermore, we incorporate unsupervised self-entropy minimization into our framework to boost performance alongside a novel queued, memory-consistent self-training strategy to utilize the reliable pseudo label for stable and efficient unsupervised adaptation. We evaluated our OSUDA-based framework on both cross-modality and cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks. Our experimental results showed that our memory consistent OSUDA performs better than existing source-relaxed UDA methods and yields similar performance to UDA methods with source data. | ['Jonghye Woo', 'Georges El Fakhri', 'Fangxu Xing', 'Xiaofeng Liu'] | 2022-09-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.64746273e-01 -2.61746459e-02 -3.69129270e-01 -6.78760827e-01
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102020e7-6e6f-493d-87e5-3035104d05fb | diffblender-scalable-and-composable | 2305.15194 | null | https://arxiv.org/abs/2305.15194v1 | https://arxiv.org/pdf/2305.15194v1.pdf | DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models | The recent progress in diffusion-based text-to-image generation models has significantly expanded generative capabilities via conditioning the text descriptions. However, since relying solely on text prompts is still restrictive for fine-grained customization, we aim to extend the boundaries of conditional generation to incorporate diverse types of modalities, e.g., sketch, box, and style embedding, simultaneously. We thus design a multimodal text-to-image diffusion model, coined as DiffBlender, that achieves the aforementioned goal in a single model by training only a few small hypernetworks. DiffBlender facilitates a convenient scaling of input modalities, without altering the parameters of an existing large-scale generative model to retain its well-established knowledge. Furthermore, our study sets new standards for multimodal generation by conducting quantitative and qualitative comparisons with existing approaches. By diversifying the channels of conditioning modalities, DiffBlender faithfully reflects the provided information or, in its absence, creates imaginative generation. | ['Namhyuk Ahn', 'Daesik Kim', 'Kibeom Hong', 'Junsoo Lee', 'Sungnyun Kim'] | 2023-05-24 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 4.67293799e-01 2.87940890e-01 -1.98699579e-01 -1.32436454e-01
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ddd175a9-4fd5-4cda-9ec7-e1edaa4000c1 | robust-and-precise-facial-landmark-detection | 2112.12328 | null | https://arxiv.org/abs/2112.12328v1 | https://arxiv.org/pdf/2112.12328v1.pdf | Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network | Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this paper, we propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention Network (SCPAN) to achieve more robust and precise facial landmark detection in challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity (BALI) field is proposed to model more effective facial shape constraints by fusing boundary and landmark intensity field information. Moreover, a Self-Calibrated Pose Attention (SCPA) model is designed to provide a self-learned objective function that enforces intermediate supervision without label information by introducing a self-calibrated mechanism and a pose attention mask. We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved. The experimental results obtained for challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature. | ['Hang Sun', 'Xu Wang', 'Witold Pedrycz', 'Zhihui Lai', 'Jie zhou', 'Hui Xi', 'Jun Wan'] | 2021-12-23 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 9.23120007e-02 1.74440220e-01 -2.57504016e-01 -8.10674012e-01
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ed9e92f3-ed0f-4212-b226-abf43ae2b4d2 | rethinking-content-and-style-exploring-bias-1 | 2102.10544 | null | https://arxiv.org/abs/2102.10544v2 | https://arxiv.org/pdf/2102.10544v2.pdf | Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement | Content and style (C-S) disentanglement intends to decompose the underlying explanatory factors of objects into two independent subspaces. From the unsupervised disentanglement perspective, we rethink content and style and propose a formulation for unsupervised C-S disentanglement based on our assumption that different factors are of different importance and popularity for image reconstruction, which serves as a data bias. The corresponding model inductive bias is introduced by our proposed C-S disentanglement Module (C-S DisMo), which assigns different and independent roles to content and style when approximating the real data distributions. Specifically, each content embedding from the dataset, which encodes the most dominant factors for image reconstruction, is assumed to be sampled from a shared distribution across the dataset. The style embedding for a particular image, encoding the remaining factors, is used to customize the shared distribution through an affine transformation. The experiments on several popular datasets demonstrate that our method achieves the state-of-the-art unsupervised C-S disentanglement, which is comparable or even better than supervised methods. We verify the effectiveness of our method by downstream tasks: domain translation and single-view 3D reconstruction. Project page at https://github.com/xrenaa/CS-DisMo. | ['Wenjun Zeng', 'Yuwang Wang', 'Tao Yang', 'Xuanchi Ren'] | 2021-02-21 | rethinking-content-and-style-exploring-bias | https://openreview.net/forum?id=KjeUNkU2d26 | https://openreview.net/pdf?id=KjeUNkU2d26 | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-7.18548968e-02 -1.45294428e-01 -4.47006166e-01 -3.45681101e-01
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7e7c9789-31eb-4dfa-b07c-665f592b59a9 | an-ensemble-based-system-for-microaneurysm | 1410.8577 | null | http://arxiv.org/abs/1410.8577v1 | http://arxiv.org/pdf/1410.8577v1.pdf | An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading | Reliable microaneurysm detection in digital fundus images is still an open
issue in medical image processing. We propose an ensemble-based framework to
improve microaneurysm detection. Unlike the well-known approach of considering
the output of multiple classifiers, we propose a combination of internal
components of microaneurysm detectors, namely preprocessing methods and
candidate extractors. We have evaluated our approach for microaneurysm
detection in an online competition, where this algorithm is currently ranked as
first and also on two other databases. Since microaneurysm detection is
decisive in diabetic retinopathy grading, we also tested the proposed method
for this task on the publicly available Messidor database, where a promising
AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification
based on the presence or absence of the microaneurysms. | ['Balint Antal', 'Andras Hajdu'] | 2014-10-30 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 1.76060602e-01 2.19572216e-01 1.31563514e-01 -3.46248865e-01
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c2c9717d-28ac-448d-b083-8ea054088883 | neural-shuffle-exchange-networks-sequence | 1907.07897 | null | https://arxiv.org/abs/1907.07897v3 | https://arxiv.org/pdf/1907.07897v3.pdf | Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time | A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O($n^2$) complexity that leads to slow execution for long sequences. We introduce a new Shuffle-Exchange neural network model for sequence to sequence tasks which have O(log n) depth and O(n log n) total complexity. We show that this model is powerful enough to infer efficient algorithms for common algorithmic benchmarks including sorting, addition and multiplication. We evaluate our architecture on the challenging LAMBADA question answering dataset and compare it with the state-of-the-art models which use attention. Our model achieves competitive accuracy and scales to sequences with more than a hundred thousand of elements. We are confident that the proposed model has the potential for building more efficient architectures for processing large interrelated data in language modeling, music generation and other application domains. | ['Agris Šostaks', 'Emīls Ozoliņš', 'Kārlis Freivalds'] | 2019-07-18 | null | null | null | null | ['lambada'] | ['natural-language-processing'] | [ 4.97979105e-01 -3.56723249e-01 5.70472926e-02 -4.46765333e-01
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1792fb39-86b1-4d92-aac3-fbe8b4354451 | deep-learning-computer-vision-algorithms-for | 2211.01037 | null | https://arxiv.org/abs/2211.01037v1 | https://arxiv.org/pdf/2211.01037v1.pdf | Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing | This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. Four use cases are considered: target detection, classification and localization, road segmentation for autonomous navigation in GNSS-denied zones, human body segmentation, and human action recognition. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks. Acquisition campaigns have been carried out to collect custom datasets reflecting typical operational scenarios, where the peculiar point of view of a multi-rotor UAV is replicated. Algorithms architectures and trained models performances are reported, showing high levels of both accuracy and inference speed. Output examples and on-field videos are presented, demonstrating models operation when deployed on a GPU-powered commercial embedded device (NVIDIA Jetson Xavier) mounted on board of a custom quad-rotor, paving the way to enabling high level autonomy. | ['Pietro Andronico', 'Alessandro Palmas'] | 2022-11-02 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.05595618e-01 -1.64052323e-01 1.83352441e-01 -2.13080540e-01
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0d2c2394-208e-4b42-bbbb-3e3284017cea | disentangling-identity-and-pose-for-facial | 2208.08106 | null | https://arxiv.org/abs/2208.08106v1 | https://arxiv.org/pdf/2208.08106v1.pdf | Disentangling Identity and Pose for Facial Expression Recognition | Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation. We regard the holistic facial representation as the combination of identity, pose and expression. These three components are encoded with different encoders. For identity encoder, a well pre-trained face recognition model is utilized and fixed during training, which alleviates the restriction on specific expression training data in previous works and makes the disentanglement practicable on in-the-wild datasets. At the same time, the pose and expression encoder are optimized with corresponding labels. Combining identity and pose feature, a neutral face of input individual should be generated by the decoder. When expression feature is added, the input image should be reconstructed. By comparing the difference between synthesized neutral and expressional images of the same individual, the expression component is further disentangled from identity and pose. Experimental results verify the effectiveness of our method on both lab-controlled and in-the-wild databases and we achieve state-of-the-art recognition performance. | ['Weihong Deng', 'Jing Jiang'] | 2022-08-17 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.21975213e-01 -5.61074764e-02 -6.72013089e-02 -7.66181588e-01
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46af13e6-cee8-40c0-a618-e25d2979db0c | learning-user-s-confidence-for-active | 2104.07791 | null | https://arxiv.org/abs/2104.07791v1 | https://arxiv.org/pdf/2104.07791v1.pdf | Learning User's confidence for active learning | In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing the effect of resolution is such a learning task. Experiments on two QuickBird images of different resolutions (with and without pansharpening) and considering committees of users prove the efficiency of the filtering scheme proposed, which maximizes the number of useful queries with respect to traditional active learning. | ['Jordi Munoz-Mari', 'Devis Tuia'] | 2021-04-15 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 8.62432942e-02 6.23669147e-01 -2.51230448e-01 -4.53469217e-01
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2c075ee9-0d2c-4906-9a3e-ce178f60be4a | deep-saliency-mapping-for-3d-meshes-and | null | null | https://dl.acm.org/doi/10.1145/3550073 | https://dl.acm.org/doi/pdf/10.1145/3550073 | Deep Saliency Mapping for 3D Meshes and Applications | Nowadays, three-dimensional (3D) meshes are widely used in various applications in different areas (e.g., industry, education, entertainment and safety). The 3D models are captured with multiple RGB-D sensors, and the sampled geometric manifolds are processed, compressed, simplified, stored, and transmitted to be reconstructed in a virtual space. These low-level processing applications require the accurate representation of the 3D models that can be achieved through saliency estimation mechanisms that identify specific areas of the 3D model representing surface patches of importance. Therefore, saliency maps guide the selection of feature locations facilitating the prioritization of 3D manifold segments and attributing to vertices more bits during compression or lower decimation probability during simplification, since compression and simplification are counterparts of the same process. In this work, we present a novel deep saliency mapping approach applied to 3D meshes, emphasizing decreasing the execution time of the saliency map estimation, especially when compared with the corresponding time by other relevant approaches. Our method utilizes baseline 3D importance maps to train convolutional neural networks. Furthermore, we present applications that utilize the extracted saliency, namely feature-aware multiscale compression and simplification frameworks. | ['Konstantinos Moustakas', 'Aris Lalos', 'Gerasimos Arvanitis', 'Stavros Nousias'] | 2023-02-06 | null | null | null | acm-transactions-on-multimedia-computing-2 | ['saliency-prediction'] | ['computer-vision'] | [ 6.36981487e-01 1.03409871e-01 -9.84559134e-02 1.57075152e-02
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242cb5c6-19e5-4ba4-a0b8-0b8fd2da6c26 | obpose-leveraging-canonical-pose-for-object | 2206.03591 | null | https://arxiv.org/abs/2206.03591v3 | https://arxiv.org/pdf/2206.03591v3.pdf | ObPose: Leveraging Pose for Object-Centric Scene Inference and Generation in 3D | We present ObPose, an unsupervised object-centric inference and generation model which learns 3D-structured latent representations from RGB-D scenes. Inspired by prior art in 2D representation learning, ObPose considers a factorised latent space, separately encoding object location (where) and appearance (what). ObPose further leverages an object's pose (i.e. location and orientation), defined via a minimum volume principle, as a novel inductive bias for learning the where component. To achieve this, we propose an efficient, voxelised approximation approach to recover the object shape directly from a neural radiance field (NeRF). As a consequence, ObPose models each scene as a composition of NeRFs, richly representing individual objects. To evaluate the quality of the learned representations, ObPose is evaluated quantitatively on the YCB, MultiShapeNet, and CLEVR datatasets for unsupervised scene segmentation, outperforming the current state-of-the-art in 3D scene inference (ObSuRF) by a significant margin. Generative results provide qualitative demonstration that the same ObPose model can both generate novel scenes and flexibly edit the objects in them. These capacities again reflect the quality of the learned latents and the benefits of disentangling the where and what components of a scene. Key design choices made in the ObPose encoder are validated with ablations. | ['Ingmar Posner', 'Oiwi Parker Jones', 'Yizhe Wu'] | 2022-06-07 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.62004352e-01 5.12090147e-01 -3.39794927e-03 -4.63139981e-01
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e0dfd4d6-50b9-4153-9c5c-cff6ce803e05 | comparing-methods-for-twitter-sentiment | 1505.02973 | null | http://arxiv.org/abs/1505.02973v1 | http://arxiv.org/pdf/1505.02973v1.pdf | Comparing methods for Twitter Sentiment Analysis | This work extends the set of works which deal with the popular problem of
sentiment analysis in Twitter. It investigates the most popular document
("tweet") representation methods which feed sentiment evaluation mechanisms. In
particular, we study the bag-of-words, n-grams and n-gram graphs approaches and
for each of them we evaluate the performance of a lexicon-based and 7
learning-based classification algorithms (namely SVM, Na\"ive Bayesian
Networks, Logistic Regression, Multilayer Perceptrons, Best-First Trees,
Functional Trees and C4.5) as well as their combinations, using a set of 4451
manually annotated tweets. The results demonstrate the superiority of
learning-based methods and in particular of n-gram graphs approaches for
predicting the sentiment of tweets. They also show that the combinatory
approach has impressive effects on n-grams, raising the confidence up to 83.15%
on the 5-Grams, using majority vote and a balanced dataset (equal number of
positive, negative and neutral tweets for training). In the n-gram graph cases
the improvement was small to none, reaching 94.52% on the 4-gram graphs, using
Orthodromic distance and a threshold of 0.001. | ['Dimosthenis Anagnostopoulos', 'Evangelos Psomakelis', 'Theodora Varvarigou', 'Konstantinos Tserpes'] | 2015-05-12 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.54278332e-01 2.65295058e-01 -5.63847065e-01 -5.23503423e-01
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f2adeee1-4e95-4229-9e0a-8c06754a7b5e | action-gpt-leveraging-large-scale-language | 2211.15603 | null | https://arxiv.org/abs/2211.15603v3 | https://arxiv.org/pdf/2211.15603v3.pdf | Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Action Generation | We introduce Action-GPT, a plug-and-play framework for incorporating Large Language Models (LLMs) into text-based action generation models. Action phrases in current motion capture datasets contain minimal and to-the-point information. By carefully crafting prompts for LLMs, we generate richer and fine-grained descriptions of the action. We show that utilizing these detailed descriptions instead of the original action phrases leads to better alignment of text and motion spaces. We introduce a generic approach compatible with stochastic (e.g. VAE-based) and deterministic (e.g. MotionCLIP) text-to-motion models. In addition, the approach enables multiple text descriptions to be utilized. Our experiments show (i) noticeable qualitative and quantitative improvement in the quality of synthesized motions, (ii) benefits of utilizing multiple LLM-generated descriptions, (iii) suitability of the prompt function, and (iv) zero-shot generation capabilities of the proposed approach. Project page: https://actiongpt.github.io | ['Ravi Kiran Sarvadevabhatla', 'Shubh Maheshwari', 'Sai Shashank Kalakonda'] | 2022-11-28 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 2.16541137e-03 7.37300515e-02 -2.74825454e-01 -2.21027061e-02
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dc6911b1-5f54-44a6-aab9-3202fdd8942b | interpreting-hidden-semantics-in-the | 2303.06652 | null | https://arxiv.org/abs/2303.06652v1 | https://arxiv.org/pdf/2303.06652v1.pdf | Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network | Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer. Specially, we reveal that the 3D point cloud classification neural network has learned the plane-level and part-level hidden semantics in the intermediate layers, and utilize the normal and IoU to evaluate the consistency of both levels' hidden semantics. Besides, by using the hidden semantics, we generate the adversarial attack samples to attack 3D point cloud classifiers. Experiments show that our proposed method reveals the hidden semantics of the 3D point cloud classification neural network on ModelNet40 and ShapeNet, which can be used for the unsupervised point cloud part segmentation without labels and attacking the 3D point cloud classifiers. | ['Cheng Wang', 'Shijun Zheng', 'Minghao Liu', 'Weiquan Liu'] | 2023-03-12 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-9.71395895e-02 2.25086734e-01 -1.07884794e-01 -4.89128530e-01
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72ef2892-1060-4b51-af6b-82cffd578214 | crowdcam-dynamic-region-segmentation | 1811.11455 | null | https://arxiv.org/abs/1811.11455v2 | https://arxiv.org/pdf/1811.11455v2.pdf | CrowdCam: Dynamic Region Segmentation | We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. This new type of images raises the need to develop new algorithms tailored specifically for it. We propose a comprehensive solution to the problem. Our solution combines cues that are based on geometry, appearance and proximity. First, geometric reasoning is used to produce rough score maps that determine, for every pixel, how likely it is to be the projection of a static or dynamic scene point. These maps are noisy because CrowdCam images are usually few and far apart both in space and in time. Then, we use similarity in appearance space and proximity in the image plane to encourage neighboring pixels to be labeled similarly as either static or dynamic. We collected a new, and challenging, data set to evaluate our algorithm. Results show that the success score of our algorithm is nearly double that of the current state of the art approach. | ['Yael Moses', 'Shai Avidan', 'Nir Zarrabi'] | 2018-11-28 | null | null | null | null | ['dynamic-region-segmentation'] | ['computer-vision'] | [ 1.98256791e-01 -1.36692122e-01 3.85772079e-01 -4.57525134e-01
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32540187-c71d-4df9-b6cd-bda64b34701c | mid-tracking-and-identifying-people-with | null | null | https://doi.org/10.1109/DCOSS.2019.00028 | http://www.cs.ox.ac.uk/files/10889/%5BDCOSS19%5DmID.pdf | mID: Tracking and Identifying People with Millimeter Wave Radar | The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Unlike competing techniques based on WiFi Channel State Information (CSI), it is capable of tracking and identifying multiple people simultaneously. Using a lowcost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users. We evaluate and demonstrate our system across a variety of scenarios, showing median position errors of 0.16 m and identification accuracy of 89% for 12 people. | ['Jianan Wang', 'and Andrew Markham', 'Niki Trigoni', 'Chris Xiaoxuan Lu', 'Wei Wang', 'Peijun Zhao', 'Changhao Chen'] | 2019-05-29 | null | null | null | 2019-15th-international-conference-on | ['rf-based-visual-tracking'] | ['computer-vision'] | [ 1.98866293e-01 -1.99782029e-01 1.77239954e-01 -1.89196337e-02
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bfb9f08d-0683-42ef-9210-b42939bbb0cc | lightweight-monocular-depth-estimation-via | 2306.05682 | null | https://arxiv.org/abs/2306.05682v1 | https://arxiv.org/pdf/2306.05682v1.pdf | Lightweight Monocular Depth Estimation via Token-Sharing Transformer | Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular depth estimation networks have been proposed for mobile robotics systems. While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently. However, massive parameters and large computational costs in the Transformer disturb the deployment to embedded devices. In this paper, we present a Token-Sharing Transformer (TST), an architecture using the Transformer for monocular depth estimation, optimized especially in embedded devices. The proposed TST utilizes global token sharing, which enables the model to obtain an accurate depth prediction with high throughput in embedded devices. Experimental results show that TST outperforms the existing lightweight monocular depth estimation methods. On the NYU Depth v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and 142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods. Furthermore, TST achieves real-time depth estimation of high-resolution images on Jetson TX2 with competitive results. | ['Junmo Kim', 'Sung-Sik Cho', 'Yeong-Hun Park', 'Eojindl Yi', 'Hyounguk Shon', 'Jae Young Lee', 'Dong-Jae Lee'] | 2023-06-09 | null | null | null | null | ['depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [-3.08962256e-01 -3.79810214e-01 -1.03853561e-01 -3.49613130e-01
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15211e35-e879-4548-a008-414db150cec6 | leveraging-unlabelled-data-in-multiple | 2305.00249 | null | https://arxiv.org/abs/2305.00249v1 | https://arxiv.org/pdf/2305.00249v1.pdf | Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions | Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every day life. However, obtaining fine-grained ground-truth and remaining unobtrusive is a contradiction and therefore, the problem is usually addressed via multiple-instance learning. Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required. In contrast, large scale collection of data without any ground-truth is much easier. Nevertheless, utilizing unlabelled data in a multiple-instance setting is not straightforward, as the topic has received very little research attention. Here we try to fill this gap by introducing a new method for combining semi-supervised with multiple-instance learning. Our approach builds on the Virtual Adversarial Training principle, a state-of-the-art approach for regular semi-supervised learning, which we adapt and modify appropriately for the multiple-instance setting. We first establish the validity of the proposed approach through proof-of-concept experiments on synthetic problems generated from two well-known benchmark datasets. We then move on to the actual task of detecting PD tremor from hand acceleration signals collected in-the-wild, but in the presence of additional completely unlabelled data. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains (up to 9% increase in F1-score) in per-subject tremor detection for a cohort of 45 subjects with known tremor ground-truth. | ['Anastasios Delopoulos', 'Alexandros Papadopoulos'] | 2023-04-29 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.71589565e-01 3.49451602e-01 -1.92548707e-01 -2.98624158e-01
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93a2ab0f-f6d8-4a8d-a423-21ab883b74a4 | online-unmixing-of-multitemporal | 1510.05893 | null | http://arxiv.org/abs/1510.05893v3 | http://arxiv.org/pdf/1510.05893v3.pdf | Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability | Hyperspectral unmixing is aimed at identifying the reference spectral
signatures composing an hyperspectral image and their relative abundance
fractions in each pixel. In practice, the identified signatures may vary
spectrally from an image to another due to varying acquisition conditions, thus
inducing possibly significant estimation errors. Against this background,
hyperspectral unmixing of several images acquired over the same area is of
considerable interest. Indeed, such an analysis enables the endmembers of the
scene to be tracked and the corresponding endmember variability to be
characterized. Sequential endmember estimation from a set of hyperspectral
images is expected to provide improved performance when compared to methods
analyzing the images independently. However, the significant size of
hyperspectral data precludes the use of batch procedures to jointly estimate
the mixture parameters of a sequence of hyperspectral images. Provided that
each elementary component is present in at least one image of the sequence, we
propose to perform an online hyperspectral unmixing accounting for temporal
endmember variability. The online hyperspectral unmixing is formulated as a
two-stage stochastic program, which can be solved using a stochastic
approximation. The performance of the proposed method is evaluated on synthetic
and real data. A comparison with independent unmixing algorithms finally
illustrates the interest of the proposed strategy. | ['Jean-Yves Tourneret', 'Nicolas Dobigeon', 'Pierre-Antoine Thouvenin'] | 2015-10-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 1.03471565e+00 -7.60802627e-01 1.95080563e-01 -9.12646577e-02
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1d793400-9c22-4787-8c5d-2227289f19d3 | voice-conversion-with-just-nearest-neighbors | 2305.18975 | null | https://arxiv.org/abs/2305.18975v1 | https://arxiv.org/pdf/2305.18975v1.pdf | Voice Conversion With Just Nearest Neighbors | Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference. Recent methods produce convincing conversions, but at the cost of increased complexity -- making results difficult to reproduce and build on. Instead, we keep it simple. We propose k-nearest neighbors voice conversion (kNN-VC): a straightforward yet effective method for any-to-any conversion. First, we extract self-supervised representations of the source and reference speech. To convert to the target speaker, we replace each frame of the source representation with its nearest neighbor in the reference. Finally, a pretrained vocoder synthesizes audio from the converted representation. Objective and subjective evaluations show that kNN-VC improves speaker similarity with similar intelligibility scores to existing methods. Code, samples, trained models: https://bshall.github.io/knn-vc | ['Herman Kamper', 'Benjamin van Niekerk', 'Matthew Baas'] | 2023-05-30 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 2.11620212e-01 5.28678223e-02 -1.14083745e-01 -2.27692619e-01
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e6aa28b3-0253-418e-88c1-e7d10d34aec0 | spam-t5-benchmarking-large-language-models | 2304.01238 | null | https://arxiv.org/abs/2304.01238v3 | https://arxiv.org/pdf/2304.01238v3.pdf | Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection | This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we examine well-established machine learning techniques for spam detection, such as Na\"ive Bayes and LightGBM, as baseline methods. We assess the performance of these models across four public datasets, utilizing different numbers of training samples (full training set and few-shot settings). Our findings reveal that, in the majority of cases, LLMs surpass the performance of the popular baseline techniques, particularly in few-shot scenarios. This adaptability renders LLMs uniquely suited to spam detection tasks, where labeled samples are limited in number and models require frequent updates. Additionally, we introduce Spam-T5, a Flan-T5 model that has been specifically adapted and fine-tuned for the purpose of detecting email spam. Our results demonstrate that Spam-T5 surpasses baseline models and other LLMs in the majority of scenarios, particularly when there are a limited number of training samples available. Our code is publicly available at https://github.com/jpmorganchase/emailspamdetection. | ['Sean Moran', 'Maxime Labonne'] | 2023-04-03 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-7.78590422e-03 -6.09379053e-01 -1.75537914e-01 -2.94729233e-01
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6d61b2be-e133-4195-8676-969097a25696 | techniques-for-automated-machine-learning | 1907.08908 | null | https://arxiv.org/abs/1907.08908v1 | https://arxiv.org/pdf/1907.08908v1.pdf | Techniques for Automated Machine Learning | Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML. | ['Yi-Wei Chen', 'Qingquan Song', 'Xia Hu'] | 2019-07-21 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-3.42639565e-01 5.36532961e-02 -1.34796292e-01 -3.04217637e-01
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a16c8b0c-0438-4cda-8d36-dbf89f5c4a74 | ensemble-based-transfer-learning-for-low | 2105.07622 | null | https://arxiv.org/abs/2105.07622v1 | https://arxiv.org/pdf/2105.07622v1.pdf | Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation | Quality Estimation (QE) of Machine Translation (MT) is a task to estimate the quality scores for given translation outputs from an unknown MT system. However, QE scores for low-resource languages are usually intractable and hard to collect. In this paper, we focus on the Sentence-Level QE Shared Task of the Fifth Conference on Machine Translation (WMT20), but in a more challenging setting. We aim to predict QE scores of given translation outputs when barely none of QE scores of that paired languages are given during training. We propose an ensemble-based predictor-estimator QE model with transfer learning to overcome such QE data scarcity challenge by leveraging QE scores from other miscellaneous languages and translation results of targeted languages. Based on the evaluation results, we provide a detailed analysis of how each of our extension affects QE models on the reliability and the generalization ability to perform transfer learning under multilingual tasks. Finally, we achieve the best performance on the ensemble model combining the models pretrained by individual languages as well as different levels of parallel trained corpus with a Pearson's correlation of 0.298, which is 2.54 times higher than baselines. | ['Yi-Chieh Liu', 'Yung-An Hsieh', 'Ting-Wei Wu'] | 2021-05-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-4.23406772e-02 -4.33647513e-01 -2.92688400e-01 -3.38563263e-01
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5fa320ef-420f-47d8-8b9f-db76136432c3 | ntire-2021-challenge-on-quality-enhancement | 2104.10782 | null | https://arxiv.org/abs/2104.10782v5 | https://arxiv.org/pdf/2104.10782v5.pdf | NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study | This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-the-art of quality enhancement on compressed video. The proposed LDV dataset is publicly available at the homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh | ['Radu Timofte', 'Ren Yang'] | 2021-04-21 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [-1.09278830e-02 -6.75451219e-01 -3.25442046e-01 -1.93242997e-01
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b2c70e8f-02bc-4642-81c4-a8ca8789fe31 | 4seasons-benchmarking-visual-slam-and-long | 2301.01147 | null | https://arxiv.org/abs/2301.01147v1 | https://arxiv.org/pdf/2301.01147v1.pdf | 4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions | In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://www.4seasons-dataset.com/. | ['Daniel Cremers', 'Niclas Zeller', 'Rui Wang', 'Nan Yang', 'Patrick Wenzel'] | 2022-12-31 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.84862715e-01 -4.46369261e-01 -2.84295857e-01 -6.23218000e-01
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8a7c2fa1-638e-41ab-810a-ee72db84b7fd | recap-retrieval-enhanced-context-aware-prefix | 2306.07206 | null | https://arxiv.org/abs/2306.07206v1 | https://arxiv.org/pdf/2306.07206v1.pdf | RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation | Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model's performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations. | ['Jonathan May', 'Xuezhe Ma', 'Marjorie Freedman', 'Hyundong J. Cho', 'Shuai Liu'] | 2023-06-12 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.65817821e-01 3.70045722e-01 6.65729493e-02 -6.41852975e-01
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c9d741a4-6d90-4991-8558-21824e24d160 | exploiting-open-ie-for-deriving-multiple | null | null | https://aclanthology.org/R19-1144 | https://aclanthology.org/R19-1144.pdf | Exploiting Open IE for Deriving Multiple Premises Entailment Corpus | Natural language inference (NLI) is a key part of natural language understanding. The NLI task is defined as a decision problem whether a given sentence {--} hypothesis {--} can be inferred from a given text. Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single premise entailment (SPE) task. Recently, a derived task of NLI from multiple premises (MPE) was introduced together with the first annotated corpus and corresponding several strong baselines. Nevertheless, the further development in MPE field requires accessibility of huge amounts of annotated data. In this paper we introduce a novel method for rapid deriving of MPE corpora from an existing NLI (SPE) annotated data that does not require any additional annotation work. This proposed approach is based on using an open information extraction system. We demonstrate the application of the method on a well known SNLI corpus. Over the obtained corpus, we provide the first evaluations as well as we state a strong baseline. | ["Jakub Kl{\\'\\i}mek", "Martin V{\\'\\i}ta"] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['open-information-extraction'] | ['natural-language-processing'] | [ 5.72227895e-01 7.12207198e-01 9.44680721e-02 -6.83705688e-01
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62703e8b-61c6-44f9-8b8b-2db2e4b89214 | streaming-submodular-maximization-under-a-k | 2002.03352 | null | https://arxiv.org/abs/2002.03352v1 | https://arxiv.org/pdf/2002.03352v1.pdf | Streaming Submodular Maximization under a $k$-Set System Constraint | In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for submodular maximization subject to a $k$-matchoid constraint. Moreover, we propose the first streaming algorithm for monotone submodular maximization subject to $k$-extendible and $k$-set system constraints. Together with our proposed reduction, we obtain $O(k\log k)$ and $O(k^2\log k)$ approximation ratio for submodular maximization subject to the above constraints, respectively. We extensively evaluate the empirical performance of our algorithm against the existing work in a series of experiments including finding the maximum independent set in randomly generated graphs, maximizing linear functions over social networks, movie recommendation, Yelp location summarization, and Twitter data summarization. | ['Amin Karbasi', 'Moran Feldman', 'Ran Haba', 'Ehsan Kazemi'] | 2020-02-09 | null | null | null | null | ['movie-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [ 1.10535488e-01 5.32860756e-01 -5.94585955e-01 -3.47130030e-01
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cad533f8-c6bb-499a-aa97-061f20f0a444 | exploiting-pseudo-image-captions-for | 2305.05496 | null | https://arxiv.org/abs/2305.05496v1 | https://arxiv.org/pdf/2305.05496v1.pdf | Exploiting Pseudo Image Captions for Multimodal Summarization | Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance. | ['Shikun Zhang', 'Jinan Sun', 'Wei Ye', 'Rui Xie', 'Chaoya Jiang'] | 2023-05-09 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 4.43277985e-01 1.04926057e-01 -1.20099835e-01 -4.47880089e-01
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87ff4cac-6a22-4a81-b391-1ec30a9e12e0 | stylizednerf-consistent-3d-scene-stylization | 2205.12183 | null | https://arxiv.org/abs/2205.12183v2 | https://arxiv.org/pdf/2205.12183v2.pdf | StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning | 3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered from different views. Directly applying methods for image or video stylization to 3D scenes cannot achieve such consistency. Thanks to recently proposed neural radiance fields (NeRF), we are able to represent a 3D scene in a consistent way. Consistent 3D scene stylization can be effectively achieved by stylizing the corresponding NeRF. However, there is a significant domain gap between style examples which are 2D images and NeRF which is an implicit volumetric representation. To address this problem, we propose a novel mutual learning framework for 3D scene stylization that combines a 2D image stylization network and NeRF to fuse the stylization ability of 2D stylization network with the 3D consistency of NeRF. We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF. It is followed by distilling the prior knowledge of spatial consistency from NeRF to the 2D stylization network through an introduced consistency loss. We also introduce a mimic loss to supervise the mutual learning of the NeRF style module and fine-tune the 2D stylization decoder. In order to further make our model handle ambiguities of 2D stylization results, we introduce learnable latent codes that obey the probability distributions conditioned on the style. They are attached to training samples as conditional inputs to better learn the style module in our novel stylized NeRF. Experimental results demonstrate that our method is superior to existing approaches in both visual quality and long-range consistency. | ['Lin Gao', 'Yu-Kun Lai', 'Yu-Jie Yuan', 'Yue He', 'Yi-Hua Huang'] | 2022-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_StylizedNeRF_Consistent_3D_Scene_Stylization_As_Stylized_NeRF_via_2D-3D_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_StylizedNeRF_Consistent_3D_Scene_Stylization_As_Stylized_NeRF_via_2D-3D_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-stylization'] | ['computer-vision'] | [ 2.56393045e-01 1.51323736e-01 -2.37731114e-02 -3.58895063e-01
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b442f93c-e899-4827-85ec-5021bcb1fe03 | virtual-to-real-reinforcement-learning-for | 1704.03952 | null | http://arxiv.org/abs/1704.03952v4 | http://arxiv.org/pdf/1704.03952v4.pdf | Virtual to Real Reinforcement Learning for Autonomous Driving | Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data. | ['Ziyan Wang', 'Yurong You', 'Cewu Lu', 'Xinlei Pan'] | 2017-04-13 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [-3.28355581e-02 4.06138867e-01 -1.43349618e-01 -3.93542320e-01
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c6bf80cc-4819-4259-a98e-5b45f0b2c4fb | improving-accuracy-of-zero-shot-action | 2301.08874 | null | https://arxiv.org/abs/2301.08874v2 | https://arxiv.org/pdf/2301.08874v2.pdf | Improving Zero-Shot Action Recognition using Human Instruction with Text Description | Zero-shot action recognition, which recognizes actions in videos without having received any training examples, is gaining wide attention considering it can save labor costs and training time. Nevertheless, the performance of zero-shot learning is still unsatisfactory, which limits its practical application. To solve this problem, this study proposes a framework to improve zero-shot action recognition using human instructions with text descriptions. The proposed framework manually describes video contents, which incurs some labor costs; in many situations, the labor costs are worth it. We manually annotate text features for each action, which can be a word, phrase, or sentence. Then by computing the matching degrees between the video and all text features, we can predict the class of the video. Furthermore, the proposed model can also be combined with other models to improve its accuracy. In addition, our model can be continuously optimized to improve the accuracy by repeating human instructions. The results with UCF101 and HMDB51 showed that our model achieved the best accuracy and improved the accuracies of other models. | ['Kazuhiko Kawamoto', 'Hiroshi Kera', 'Nan Wu'] | 2023-01-21 | null | null | null | null | ['zero-shot-action-recognition', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 3.10404450e-01 -3.07906955e-01 -4.69492048e-01 -4.02742893e-01
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c7b3490a-0a54-4c86-94f4-e55929c460b5 | cloze-test-helps-effective-video-anomaly | 2008.11988 | null | https://arxiv.org/abs/2008.11988v1 | https://arxiv.org/pdf/2008.11988v1.pdf | Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events | As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a reconstruction or frame prediction routine. They suffer from two gaps: (1) They cannot localize video activities in a both precise and comprehensive manner. (2) They lack sufficient abilities to utilize high-level semantics and temporal context information. Inspired by frequently-used cloze test in language study, we propose a brand-new VAD solution named Video Event Completion (VEC) to bridge gaps above: First, we propose a novel pipeline to achieve both precise and comprehensive enclosure of video activities. Appearance and motion are exploited as mutually complimentary cues to localize regions of interest (RoIs). A normalized spatio-temporal cube (STC) is built from each RoI as a video event, which lays the foundation of VEC and serves as a basic processing unit. Second, we encourage DNN to capture high-level semantics by solving a visual cloze test. To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE). The DNN learns to restore the original video event from the IE by inferring the missing patch. Third, to incorporate richer motion dynamics, another DNN is trained to infer erased patches' optical flow. Finally, two ensemble strategies using different types of IE and modalities are proposed to boost VAD performance, so as to fully exploit the temporal context and modality information for VAD. VEC can consistently outperform state-of-the-art methods by a notable margin (typically 1.5%-5% AUROC) on commonly-used VAD benchmarks. Our codes and results can be verified at github.com/yuguangnudt/VEC_VAD. | ['Zhiping Cai', 'Siqi Wang', 'En Zhu', 'Jianping Yin', 'Guang Yu', 'Chuanfu Xu', 'Marius Kloft'] | 2020-08-27 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 9.31607038e-02 -4.86237645e-01 -2.12977692e-01 -1.90537512e-01
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86c9ce52-d4a4-4762-a90f-285beda0f411 | mkiou-loss-towards-accurate-oriented-object | 2206.15109 | null | https://arxiv.org/abs/2206.15109v1 | https://arxiv.org/pdf/2206.15109v1.pdf | MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images | Oriented bounding box regression is crucial for oriented object detection. However, regression-based methods often suffer from boundary problems and the inconsistency between loss and evaluation metrics. In this paper, a modulated Kalman IoU loss of approximate SkewIoU is proposed, named MKIoU. To avoid boundary problems, we convert the oriented bounding box to Gaussian distribution, then use the Kalman filter to approximate the intersection area. However, there exists significant difference between the calculated and actual intersection areas. Thus, we propose a modulation factor to adjust the sensitivity of angle deviation and width-height offset to loss variation, making the loss more consistent with the evaluation metric. Furthermore, the Gaussian modeling method avoids the boundary problem but causes the angle confusion of square objects simultaneously. Thus, the Gaussian Angle Loss (GA Loss) is presented to solve this problem by adding a corrected loss for square targets. The proposed GA Loss can be easily extended to other Gaussian-based methods. Experiments on three publicly available aerial image datasets, DOTA, UCAS-AOD, and HRSC2016, show the effectiveness of the proposed method. | ['Linlin Ou', 'Mi Lin', 'Jiangping Lu', 'Xinyi Yu'] | 2022-06-30 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.18276432e-01 -3.60450476e-01 2.51547426e-01 -4.52662617e-01
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f3848a3f-3d2c-442b-a520-360d3b011417 | listening-to-the-world-improves-speech | 1710.08377 | null | http://arxiv.org/abs/1710.08377v1 | http://arxiv.org/pdf/1710.08377v1.pdf | Listening to the World Improves Speech Command Recognition | We study transfer learning in convolutional network architectures applied to
the task of recognizing audio, such as environmental sound events and speech
commands. Our key finding is that not only is it possible to transfer
representations from an unrelated task like environmental sound classification
to a voice-focused task like speech command recognition, but also that doing so
improves accuracies significantly. We also investigate the effect of increased
model capacity for transfer learning audio, by first validating known results
from the field of Computer Vision of achieving better accuracies with
increasingly deeper networks on two audio datasets: UrbanSound8k and the newly
released Google Speech Commands dataset. Then we propose a simple multiscale
input representation using dilated convolutions and show that it is able to
aggregate larger contexts and increase classification performance. Further, the
models trained using a combination of transfer learning and multiscale input
representations need only 40% of the training data to achieve similar
accuracies as a freshly trained model with 100% of the training data. Finally,
we demonstrate a positive interaction effect for the multiscale input and
transfer learning, making a case for the joint application of the two
techniques. | ['Brian McMahan', 'Delip Rao'] | 2017-10-23 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.85606539e-01 -7.56425783e-02 4.91679877e-01 -4.93509263e-01
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e2808306-4ad1-4739-b798-fc8a0d890708 | handmime-sign-language-fingerspelling | 2209.05135 | null | https://arxiv.org/abs/2209.05135v3 | https://arxiv.org/pdf/2209.05135v3.pdf | Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction | Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fingerspelling sign language in robots. In this paper, we propose an approach for learning dexterous motor imitation from video examples without additional information. To achieve this, we first build a URDF model of a robotic hand with a single actuator for each joint. We then leverage pre-trained deep vision models to extract the 3D pose of the hand from RGB videos. Next, using state-of-the-art reinforcement learning algorithms for motion imitation (namely, proximal policy optimization and soft actor-critic), we train a policy to reproduce the movement extracted from the demonstrations. We identify the optimal set of hyperparameters for imitation based on a reference motion. Finally, we demonstrate the generalizability of our approach by testing it on six different tasks, corresponding to fingerspelled letters. Our results show that our approach is able to successfully imitate these fine-grained movements without additional information, highlighting its potential for real-world applications in robotics. | ['Angelo Cangelosi', 'Aphrodite Galata', 'Federico Tavella'] | 2022-09-12 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.10944742e-01 2.05007419e-01 -6.49878085e-02 1.13764912e-01
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815ddae2-f890-4143-9f3f-dc9dcb7d1cc4 | recovering-arrhythmic-eeg-transients-from | 2303.07683 | null | https://arxiv.org/abs/2303.07683v1 | https://arxiv.org/pdf/2303.07683v1.pdf | Recovering Arrhythmic EEG Transients from Their Stochastic Interference | Traditionally, the neuronal dynamics underlying electroencephalograms (EEG) have been understood as arising from \textit{rhythmic oscillators with varying degrees of synchronization}. This dominant metaphor employs frequency domain EEG analysis to identify the most prominent populations of neuronal current sources in terms of their frequency and spectral power. However, emerging perspectives on EEG highlight its arrhythmic nature, which is primarily inferred from broadband EEG properties like the ubiquitous $1/f$ spectrum. In the present study, we use an \textit{arrhythmic superposition of pulses} as a metaphor to explain the origin of EEG. This conceptualization has a fundamental problem because the interference produced by the superpositions of pulses generates colored Gaussian noise, masking the temporal profile of the generating pulse. We solved this problem by developing a mathematical method involving the derivative of the autocovariance function to recover excellent approximations of the underlying pulses, significantly extending the analysis of this type of stochastic processes. When the method is applied to spontaneous mouse EEG sampled at $5$ kHz during the sleep-wake cycle, specific patterns -- called $\Psi$-patterns -- characterizing NREM sleep, REM sleep, and wakefulness are revealed. $\Psi$-patterns can be understood theoretically as \textit{power density in the time domain} and correspond to combinations of generating pulses at different time scales. Remarkably, we report the first EEG wakefulness-specific feature, which corresponds to an ultra-fast ($\sim 1$ ms) transient component of the observed patterns. By shifting the paradigm of EEG genesis from oscillators to random pulse generators, our theoretical framework pushes the boundaries of traditional Fourier-based EEG analysis, paving the way for new insights into the arrhythmic components of neural dynamics. | ['Kaspar E. Vogt', 'Masashi Yanagisawa', 'Juan-Carlos Letelier', 'Xifang Hayashi', 'Olga Malyshevskaya', 'GoEun Han', 'Hiroyasu Ando', 'Javier Díaz'] | 2023-03-14 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.00972456e-01 -3.43652308e-01 5.89853227e-01 9.46537554e-02
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6d8132ff-e78c-424b-af84-14af871e7518 | hypliloc-towards-effective-lidar-pose | 2304.00932 | null | https://arxiv.org/abs/2304.00932v2 | https://arxiv.org/pdf/2304.00932v2.pdf | HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion | LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. On the other hand, pose regression methods take images or point clouds as inputs and directly regress global poses in an end-to-end manner. They do not perform database matching and are more computationally efficient than retrieval techniques. We propose HypLiLoc, a new model for LiDAR pose regression. We use two branched backbones to extract 3D features and 2D projection features, respectively. We consider multi-modal feature fusion in both Euclidean and hyperbolic spaces to obtain more effective feature representations. Experimental results indicate that HypLiLoc achieves state-of-the-art performance in both outdoor and indoor datasets. We also conduct extensive ablation studies on the framework design, which demonstrate the effectiveness of multi-modal feature extraction and multi-space embedding. Our code is released at: https://github.com/sijieaaa/HypLiLoc | ['Wee Peng Tay', 'Yang song', 'Kai Zhao', 'Wei Wang', 'Rui She', 'Qiyu Kang', 'Sijie Wang'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lidar-absolute-pose-regression', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-2.02351838e-01 -4.85749662e-01 -3.02261591e-01 -4.78913099e-01
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8adfe2b2-6a61-451c-ae09-cd0d3df0556f | learning-multi-modal-brain-tumor-segmentation | 2208.12781 | null | https://arxiv.org/abs/2208.12781v1 | https://arxiv.org/pdf/2208.12781v1.pdf | Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning | Due to the difficulties of obtaining multimodal paired images in clinical practice, recent studies propose to train brain tumor segmentation models with unpaired images and capture complementary information through modality translation. However, these models cannot fully exploit the complementary information from different modalities. In this work, we thus present a novel two-step (intra-modality and inter-modality) curriculum disentanglement learning framework to effectively utilize privileged semi-paired images, i.e. limited paired images that are only available in training, for brain tumor segmentation. Specifically, in the first step, we propose to conduct reconstruction and segmentation with augmented intra-modality style-consistent images. In the second step, the model jointly performs reconstruction, unsupervised/supervised translation, and segmentation for both unpaired and paired inter-modality images. A content consistency loss and a supervised translation loss are proposed to leverage complementary information from different modalities in this step. Through these two steps, our method effectively extracts modality-specific style codes describing the attenuation of tissue features and image contrast, and modality-invariant content codes containing anatomical and functional information from the input images. Experiments on three brain tumor segmentation tasks show that our model outperforms competing segmentation models based on unpaired images. | ['Rui Li', 'Jia Wei', 'Zecheng Liu'] | 2022-08-26 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 9.01438117e-01 1.11994542e-01 -5.63840926e-01 -5.01462102e-01
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90c3ff36-aa2f-4b2a-ab82-1c202adc8524 | soccernet-a-scalable-dataset-for-action | 1804.04527 | null | http://arxiv.org/abs/1804.04527v2 | http://arxiv.org/pdf/1804.04527v2.pdf | SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos | In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
$\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet. | ['Tarek Dghaily', 'Silvio Giancola', 'Mohieddine Amine', 'Bernard Ghanem'] | 2018-04-12 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [-6.15672953e-02 -4.66210037e-01 -6.30350113e-01 -1.26287252e-01
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e032a6f1-e1b3-452b-aa76-222e74b8d310 | taxoexpan-self-supervised-taxonomy-expansion | 2001.09522 | null | https://arxiv.org/abs/2001.09522v1 | https://arxiv.org/pdf/2001.09522v1.pdf | TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network | Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of <query concept, anchor concept> pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion. | ['Chenyan Xiong', 'Kuansan Wang', 'Jiawei Han', 'Zhihong Shen', 'Jiaming Shen', 'Chi Wang'] | 2020-01-26 | null | null | null | null | ['product-recommendation', 'taxonomy-expansion'] | ['miscellaneous', 'natural-language-processing'] | [ 7.71555584e-03 -2.16840245e-02 -7.26357937e-01 -5.63830614e-01
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1babe5f0-2671-49a0-9ae8-3178e435e7c4 | data-expansion-using-wordnet-based-semantic | null | null | https://aclanthology.org/2022.lrec-1.187 | https://aclanthology.org/2022.lrec-1.187.pdf | Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection | Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural networks. This paper advocates a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. We use both word sense disambiguation and synonymy relation in WordNet lexical database to generate coherent equivalent utterances of cyberbullying input data. The disambiguation and semantic expansion are intended to overcome the inherent limitations of social media posts, such as an abundance of unstructured constructs and limited semantic content. Besides, to test the feasibility, a novel protocol has been employed to collect cyberbullying traces data from AskFm forum, where about a 10K-size dataset has been manually labeled. Next, the problem of cyberbullying identification is viewed as a binary classification problem using an elaborated data augmentation strategy and an appropriate classifier. For the latter, a Convolutional Neural Network (CNN) architecture with FastText and BERT was put forward, whose results were compared against commonly employed Naïve Bayes (NB) and Logistic Regression (LR) classifiers with and without data augmentation. The research outcomes were promising and yielded almost 98.4% of classifier accuracy, an improvement of more than 4% over baseline results. | ['Muhidin Mohamed', 'Mourad Oussalah', 'Djamila Romaissa Beddiar', 'Md Saroar Jahan'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.09521151e-01 3.77788961e-01 -2.30173081e-01 -2.74146080e-01
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dfbf7af2-bc91-4c8a-addd-37e317a789e2 | collaborative-filtering-via-heterogeneous | null | null | http://fange.pro/files/2021Collaborative.pdf | http://fange.pro/files/2021Collaborative.pdf | Collaborative filtering via heterogeneous neural networks | After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personalized informativeness of each vertex. In this work, we propose a method named Personalized Proximity Preserved Network Embedding (PPPNE) to adaptively capture the personalization of vertices based on the personalized ranking loss. Our theoretical analysis shows that PPPNE generalizes prior work based on the matrix factorization or the neural network with a single layer, and we argue that preserving personalized proximity is the key to learning more informative representations. Moreover, to better capture the network structure in multiple scales, we exploit the distance ordering of each vertex. Our method can be efficiently optimized with a vertex-anchored sampling strategy. The results of extensive experiments on five real-world networks demonstrate that our approach outperforms state-of-the-art network embedding methods with a considerable improvement on several common tasks including link prediction and vertex classification. Additionally, PPPNE is efficient and can be easily accelerated by parallel computing, which enables PPPNE to work on large-scale networks. | ['Wei Zeng', 'Changjie Fan', 'Kai Wang', 'Jianrong Tao', 'Biao Geng', 'Ge Fan'] | 2022-02-01 | null | null | null | neurocomputing-2022-2 | ['network-embedding', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-1.01079218e-01 1.79904044e-01 -7.87872195e-01 -2.15167522e-01
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ed1a7e38-58b8-46c8-bb1e-4e6d9295f5e7 | understanding-the-challenges-and | 2303.05463 | null | https://arxiv.org/abs/2303.05463v1 | https://arxiv.org/pdf/2303.05463v1.pdf | Understanding the Challenges and Opportunities of Pose-based Anomaly Detection | Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Utilizing pose data alleviates privacy and ethical issues. Also, computation-wise, the complexity of pose-based models is lower than pixel-based approaches. However, it introduces more challenges, such as noisy skeleton data, losing important pixel information, and not having enriched enough features. These problems are exacerbated by a lack of anomaly detection datasets that are good enough representatives of real-world scenarios. In this work, we analyze and quantify the characteristics of two well-known video anomaly datasets to better understand the difficulties of pose-based anomaly detection. We take a step forward, exploring the discriminating power of pose and trajectory for video anomaly detection and their effectiveness based on context. We believe these experiments are beneficial for a better comprehension of pose-based anomaly detection and the datasets currently available. This will aid researchers in tackling the task of anomaly detection with a more lucid perspective, accelerating the development of robust models with better performance. | ['Hamed Tabkhi', 'Vinit Katariya', 'Armin Danesh Pazho', 'Ghazal Alinezhad Noghre'] | 2023-03-09 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 3.57816964e-01 -4.54164326e-01 4.54473030e-03 -3.58863473e-01
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7e4be5b0-9f7d-44b4-bd70-804e289dd166 | global-feature-aggregation-for-accident | 2006.08942 | null | https://arxiv.org/abs/2006.08942v1 | https://arxiv.org/pdf/2006.08942v1.pdf | Global Feature Aggregation for Accident Anticipation | Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggregation (FA) block that refines each object's features by computing a weighted sum of the features of all objects in a frame. We use FA block along with Long Short Term Memory (LSTM) network to anticipate accidents in the video sequences. We report mean Average Precision (mAP) and Average Time-to-Accident (ATTA) on Street Accident (SA) dataset. Our proposed method achieves the highest score for risk anticipation by predicting accidents 0.32 sec and 0.75 sec earlier compared to the best results with Adaptive Loss and dynamic parameter prediction based methods respectively. | ['Muhammad Umar Karim Khan', 'Chong Min Kyung', 'Mishal Fatima'] | 2020-06-16 | null | null | null | null | ['accident-anticipation', 'parameter-prediction'] | ['computer-vision', 'miscellaneous'] | [ 7.99387023e-02 6.71361834e-02 2.72816986e-01 -6.30755424e-01
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9c431fea-d18d-4a67-bf0f-b24a43338e66 | what-you-need-is-a-more-professional-teacher | 1906.02517 | null | https://arxiv.org/abs/1906.02517v5 | https://arxiv.org/pdf/1906.02517v5.pdf | Guided learning for weakly-labeled semi-supervised sound event detection | We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data. The guidance is guaranteed by the audio tagging performance gap of the two models. In the meantime, the student model liberated from the trade-off is able to provide more excellent boundary detection results. We propose a principle to design such two models based on the relation between the temporal compression scale and the two sub-targets. We also propose an end-to-end semi-supervised learning process for these two models to enable their abilities to rise alternately. Experiments on the DCASE2018 Task4 dataset show that our approach achieves competitive performance. | ['Liwei Lin', 'Hong Liu', 'Yueliang Qian', 'Xiangdong Wang'] | 2019-06-06 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 1.55824870e-01 6.09801233e-01 -2.22342834e-01 -3.30071926e-01
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d6a550d7-37e7-43ed-b5c3-cb32d861b4b0 | privacy-in-practice-private-covid-19 | 2211.11434 | null | https://arxiv.org/abs/2211.11434v4 | https://arxiv.org/pdf/2211.11434v4.pdf | Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version) | Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 models are in part based on small datasets, provide weaker or unclear privacy guarantees, and do not investigate practical privacy. We suggest improvements to address these open gaps. We account for inherent class imbalances and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets. Our evaluation is supported by empirically estimating practical privacy through black-box Membership Inference Attacks (MIAs). The introduced DP should help limit leakage threats posed by MIAs, and our practical analysis is the first to test this hypothesis on the COVID-19 classification task. Our results indicate that needed privacy levels might differ based on the task-dependent practical threat from MIAs. The results further suggest that with increasing DP guarantees, empirical privacy leakage only improves marginally, and DP therefore appears to have a limited impact on practical MIA defense. Our findings identify possibilities for better utility-privacy trade-offs, and we believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy. | ['Erhard Rahm', 'Peter Christen', 'Maja Schneider', 'Lucas Lange'] | 2022-11-21 | null | null | null | null | ['membership-inference-attack', 'privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 2.37926707e-01 2.67517865e-01 -4.43970650e-01 -3.22335035e-01
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d039fe74-efbf-4380-9171-7a2844c6c61f | localizing-semantic-patches-for-accelerating | 2206.03367 | null | https://arxiv.org/abs/2206.03367v1 | https://arxiv.org/pdf/2206.03367v1.pdf | Localizing Semantic Patches for Accelerating Image Classification | Existing works often focus on reducing the architecture redundancy for accelerating image classification but ignore the spatial redundancy of the input image. This paper proposes an efficient image classification pipeline to solve this problem. We first pinpoint task-aware regions over the input image by a lightweight patch proposal network called AnchorNet. We then feed these localized semantic patches with much smaller spatial redundancy into a general classification network. Unlike the popular design of deep CNN, we aim to carefully design the Receptive Field of AnchorNet without intermediate convolutional paddings. This ensures the exact mapping from a high-level spatial location to the specific input image patch. The contribution of each patch is interpretable. Moreover, AnchorNet is compatible with any downstream architecture. Experimental results on ImageNet show that our method outperforms SOTA dynamic inference methods with fewer inference costs. Our code is available at https://github.com/winycg/AnchorNet. | ['Yongjun Xu', 'Zhulin An', 'Chuanguang Yang'] | 2022-06-07 | null | null | null | null | ['classification'] | ['methodology'] | [ 8.09162185e-02 2.72994697e-01 -3.23475838e-01 -5.01536012e-01
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b098e1f6-3012-4f21-b324-b902e5e91e8e | causal-graph-discovery-from-self-and-mutually | 2301.11197 | null | https://arxiv.org/abs/2301.11197v2 | https://arxiv.org/pdf/2301.11197v2.pdf | Causal Graph Discovery from Self and Mutually Exciting Time Series | We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. Furthermore, we develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program to establish confidence intervals for a wide range of non-linear monotone link functions. We validate our theoretical results and show the competitive performance of our method via extensive numerical experiments. Most importantly, we demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs) while achieving comparable prediction performance to powerful ``black-box'' models such as XGBoost. Thus, the future adoption of our proposed method to conduct continuous surveillance of high-risk patients by clinicians is much more likely. | ['Rishikesan Kamaleswaran', 'Christopher S. Josef', 'Yao Xie', 'Song Wei'] | 2023-01-26 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.47060114e-01 2.94726551e-01 -4.49433178e-01 -4.68641132e-01
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d514eb88-ae33-4936-b1e3-880605f4a409 | how-do-seq2seq-models-perform-on-end-to-end-1 | null | null | https://aclanthology.org/2022.acl-long.531 | https://aclanthology.org/2022.acl-long.531.pdf | How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? | With the rapid development of deep learning, Seq2Seq paradigm has become prevalent for end-to-end data-to-text generation, and the BLEU scores have been increasing in recent years. However, it is widely recognized that there is still a gap between the quality of the texts generated by models and the texts written by human. In order to better understand the ability of Seq2Seq models, evaluate their performance and analyze the results, we choose to use Multidimensional Quality Metric(MQM) to evaluate several representative Seq2Seq models on end-to-end data-to-text generation. We annotate the outputs of five models on four datasets with eight error types and find that 1) copy mechanism is helpful for the improvement in Omission and Inaccuracy Extrinsic errors but it increases other types of errors such as Addition; 2) pre-training techniques are highly effective, and pre-training strategy and model size are very significant; 3) the structure of the dataset also influences the model’s performance greatly; 4) some specific types of errors are generally challenging for seq2seq models. | ['Xiaojun Wan', 'Xunjian Yin'] | null | null | null | null | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.31972109e-02 -1.94536503e-02 1.56888202e-01 -3.29367876e-01
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8f5c0683-1744-4dab-89e0-7c7f9564ade3 | tive-a-toolbox-for-identifying-video-instance | 2210.08856 | null | https://arxiv.org/abs/2210.08856v1 | https://arxiv.org/pdf/2210.08856v1.pdf | TIVE: A Toolbox for Identifying Video Instance Segmentation Errors | Since first proposed, Video Instance Segmentation(VIS) task has attracted vast researchers' focus on architecture modeling to boost performance. Though great advances achieved in online and offline paradigms, there are still insufficient means to identify model errors and distinguish discrepancies between methods, as well approaches that correctly reflect models' performance in recognizing object instances of various temporal lengths remain barely available. More importantly, as the fundamental model abilities demanded by the task, spatial segmentation and temporal association are still understudied in both evaluation and interaction mechanisms. In this paper, we introduce TIVE, a Toolbox for Identifying Video instance segmentation Errors. By directly operating output prediction files, TIVE defines isolated error types and weights each type's damage to mAP, for the purpose of distinguishing model characters. By decomposing localization quality in spatial-temporal dimensions, model's potential drawbacks on spatial segmentation and temporal association can be revealed. TIVE can also report mAP over instance temporal length for real applications. We conduct extensive experiments by the toolbox to further illustrate how spatial segmentation and temporal association affect each other. We expect the analysis of TIVE can give the researchers more insights, guiding the community to promote more meaningful explorations for video instance segmentation. The proposed toolbox is available at https://github.com/wenhe-jia/TIVE. | ['Qing Song', 'Yilin Zhou', 'Wenyi Zhao', 'Zilong Jia', 'Lu Yang', 'Wenhe Jia'] | 2022-10-17 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-7.33254850e-02 -3.16910028e-01 -3.38782400e-01 -3.49596143e-01
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b6121bd8-e086-4e0b-94c9-ad852d92ddae | driven-to-distraction-self-supervised | 1711.06623 | null | http://arxiv.org/abs/1711.06623v2 | http://arxiv.org/pdf/1711.06623v2.pdf | Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments | We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic. | ['Will Maddern', 'Ingmar Posner', 'Geoffrey Pascoe', 'Dan Barnes'] | 2017-11-17 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.18782707e-01 1.05440378e-01 4.65405658e-02 -5.89736462e-01
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7c6b212e-2b0a-402f-bdc5-da8ffc78f931 | persistent-homology-captures-the | 2106.00012 | null | https://arxiv.org/abs/2106.00012v1 | https://arxiv.org/pdf/2106.00012v1.pdf | Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set | The training of neural networks is usually monitored with a validation (holdout) set to estimate the generalization of the model. This is done instead of measuring intrinsic properties of the model to determine whether it is learning appropriately. In this work, we suggest studying the training of neural networks with Algebraic Topology, specifically Persistent Homology (PH). Using simplicial complex representations of neural networks, we study the PH diagram distance evolution on the neural network learning process with different architectures and several datasets. Results show that the PH diagram distance between consecutive neural network states correlates with the validation accuracy, implying that the generalization error of a neural network could be intrinsically estimated without any holdout set. | ['Marta Villegas', 'Jordi Armengol-Estapé', 'David Pérez-Fernández', 'Asier Gutiérrez-Fandiño'] | 2021-05-31 | persistent-homology-captures-the-1 | https://openreview.net/forum?id=BM64dm9HvN | https://openreview.net/pdf?id=BM64dm9HvN | neurips-2021-12 | ['holdout-set'] | ['computer-vision'] | [ 3.54462326e-01 6.32417619e-01 -6.58569038e-02 -2.28758588e-01
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e1909470-4ded-42e7-8549-457a2e72952c | piano-a-parametric-hand-bone-model-from | 2106.10893 | null | https://arxiv.org/abs/2106.10893v1 | https://arxiv.org/pdf/2106.10893v1.pdf | PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging | Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available. | ['Jingyi Yu', 'Lan Xu', 'Yuyao Zhang', 'Minye Wu', 'Yuwei Li'] | 2021-06-21 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [-2.62125999e-01 2.50524133e-01 -2.47002885e-01 -2.71489173e-02
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3eafbc89-4f45-489c-8365-5d2bfe094ca5 | unsupervised-domain-adaptation-for-spatio | 2010.09211 | null | https://arxiv.org/abs/2010.09211v1 | https://arxiv.org/pdf/2010.09211v1.pdf | Unsupervised Domain Adaptation for Spatio-Temporal Action Localization | Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features. This problem is typically formulated in the context of supervised learning, where the learned classifiers operate on the premise that both training and test data are sampled from the same underlying distribution. However, this assumption does not hold when there is a significant domain shift, leading to poor generalization performance on the test data. To address this, we focus on the hard and novel task of generalizing training models to test samples without access to any labels from the latter for spatio-temporal action localization by proposing an end-to-end unsupervised domain adaptation algorithm. We extend the state-of-the-art object detection framework to localize and classify actions. In order to minimize the domain shift, three domain adaptation modules at image level (temporal and spatial) and instance level (temporal) are designed and integrated. We design a new experimental setup and evaluate the proposed method and different adaptation modules on the UCF-Sports, UCF-101 and JHMDB benchmark datasets. We show that significant performance gain can be achieved when spatial and temporal features are adapted separately, or jointly for the most effective results. | ['Ming-Hsuan Yang', 'Behzad Dariush', 'Yi-Ting Chen', 'Nakul Agarwal'] | 2020-10-19 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 5.09956002e-01 -4.20713186e-01 -4.26734269e-01 -5.10272920e-01
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d18ab2d4-4a9d-4389-a622-9cd215df063c | adatag-multi-attribute-value-extraction-from | 2106.02318 | null | https://arxiv.org/abs/2106.02318v1 | https://arxiv.org/pdf/2106.02318v1.pdf | AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding | Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model's capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods. | ['Xin Luna Dong', 'Xiang Ren', 'Christan Grant', 'Yan Liang', 'Nasser Zalmout', 'Jun Yan'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.362 | https://aclanthology.org/2021.acl-long.362.pdf | acl-2021-5 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.22901702e-02 4.64291215e-01 -6.92350864e-01 -8.56029332e-01
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80c167b7-ccd5-42db-98d5-daf7d25b27c2 | deep-multi-modal-classification-of | 1710.09779 | null | http://arxiv.org/abs/1710.09779v3 | http://arxiv.org/pdf/1710.09779v3.pdf | Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis | Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system
to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In
our proposed approach, we use minimum and maximum intensity projections to ease
the annotation variations among different slices and type of MRIs. Then, we
present a CNN to obtain deep feature representation corresponding to each MRI
modality (T1-weighted and T2-weighted). At the final step, we employ canonical
correlation analysis (CCA) to perform a fusion operation at the feature level,
leading to discriminative canonical correlation features. Extracted features
are used for classification. Our results indicate significant improvements over
other potential approaches to solve this important problem. The proposed
approach doesn't require explicit sample balancing in cases of imbalance
between positive and negative examples. To the best of our knowledge, our study
is the first to automatically diagnose IPMN using multi-modal MRI. | ['Candice W. Bolan', 'Juan E. Corral', 'Sarfaraz Hussein', 'Ulas Bagci', 'Michael B. Wallace', 'Pujan Kandel'] | 2017-10-26 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 1.42497540e-01 -6.37141541e-02 -3.00221950e-01 -3.84664625e-01
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1ca63cfc-ad01-43ba-b79e-e0d407c2eadc | classifying-the-ideological-orientation-of | null | null | https://ieeexplore.ieee.org/document/10069289 | https://ieeexplore.ieee.org/document/10069289 | Classifying the Ideological Orientation of User-Submitted Texts in Social Media | With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of text articles posted to the online communities r/Liberal and r/Conservative from the social media website Reddit. Using the corpus, we develop and apply three classifiers. The baseline classifier is a Bayes model that accounts for each text article’s web domain, as such, classification is independent of content. Next, we develop a support vector machine (SVM) model with term frequency-inverse document frequency (TF-IDF) features; this approach highlight differences in language using a count-based feature-space to differentiate text articles. Last, we evaluate the context-based transformer (RoBERTa) model and discuss its under-performance relative to the baseline and SVM models. | ['Rickard Ewetz', 'Adan Ernesto Vela', 'Kamalakkannan Ravi'] | 2022-12-12 | null | null | null | ieee-international-conference-on-machine-1 | ['news-classification'] | ['natural-language-processing'] | [-1.22414948e-02 -8.61635581e-02 -8.52354586e-01 -2.16909185e-01
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c8cd7990-5c68-470d-9db8-84b8ab815526 | the-current-state-of-summarization | 2305.04853 | null | https://arxiv.org/abs/2305.04853v1 | https://arxiv.org/pdf/2305.04853v1.pdf | The Current State of Summarization | With the explosive growth of textual information, summarization systems have become increasingly important. This work aims at indicating the current state of the art in abstractive text summarization concisely. As part of this, we outline the current paradigm shifts towards pre-trained encoder-decoder models and large autoregressive language models. Additionally, we delve further into the challenges of evaluating summarization systems and the potential of instruction-tuned models for zero-shot summarization. Finally, we provide a brief overview of how summarization systems are currently being integrated into commercial applications. | ['Fabian Retkowski'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.41300845e-01 3.51158202e-01 -3.11336040e-01 -1.96491227e-01
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b154244f-6d92-4bd5-9ae0-2b7f359ab77e | finding-a-balanced-degree-of-automation-for | 2109.11503 | null | https://arxiv.org/abs/2109.11503v1 | https://arxiv.org/pdf/2109.11503v1.pdf | Finding a Balanced Degree of Automation for Summary Evaluation | Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs. Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment. In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. Semi-automatic Lite2Pyramid retains the reusable human-labeled Summary Content Units (SCUs) for reference(s) but replaces the manual work of judging SCUs' presence in system summaries with a natural language inference (NLI) model. Fully automatic Lite3Pyramid further substitutes SCUs with automatically extracted Semantic Triplet Units (STUs) via a semantic role labeling (SRL) model. Finally, we propose in-between metrics, Lite2.xPyramid, where we use a simple regressor to predict how well the STUs can simulate SCUs and retain SCUs that are more difficult to simulate, which provides a smooth transition and balance between automation and manual evaluation. Comparing to 15 existing metrics, we evaluate human-metric correlations on 3 existing meta-evaluation datasets and our newly-collected PyrXSum (with 100/10 XSum examples/systems). It shows that Lite2Pyramid consistently has the best summary-level correlations; Lite3Pyramid works better than or comparable to other automatic metrics; Lite2.xPyramid trades off small correlation drops for larger manual effort reduction, which can reduce costs for future data collection. Our code and data are publicly available at: https://github.com/ZhangShiyue/Lite2-3Pyramid | ['Mohit Bansal', 'Shiyue Zhang'] | 2021-09-23 | null | https://aclanthology.org/2021.emnlp-main.531 | https://aclanthology.org/2021.emnlp-main.531.pdf | emnlp-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 2.31204808e-01 1.33986875e-01 -3.32201779e-01 -3.64866227e-01
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bfc80bba-6f75-4eed-ac6e-734e3bd2a7eb | mention-centered-graph-neural-network-for | 2103.08200 | null | https://arxiv.org/abs/2103.08200v1 | https://arxiv.org/pdf/2103.08200v1.pdf | Mention-centered Graph Neural Network for Document-level Relation Extraction | Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations. | ['Yiyan Zhang', 'Min Peng', 'Jiaxin Pan'] | 2021-03-15 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 9.75372717e-02 5.67234755e-01 -4.20878530e-01 -4.90964144e-01
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4a86d48b-94a0-4802-9855-ec10efc2f9ea | niki-neural-inverse-kinematics-with | 2305.08590 | null | https://arxiv.org/abs/2305.08590v1 | https://arxiv.org/pdf/2305.08590v1.pdf | NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation | With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://github.com/Jeff-sjtu/NIKI | ['Cewu Lu', 'Fan Wang', 'Jiasheng Tang', 'Qi Liu', 'Siyuan Bian', 'Jiefeng Li'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.12549058e-02 1.08627744e-01 -2.53954548e-02 -3.13737571e-01
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fa1209dc-12cd-49e2-8426-6cfd44de81dc | learning-by-tracking-siamese-cnn-for-robust | 1604.07866 | null | http://arxiv.org/abs/1604.07866v3 | http://arxiv.org/pdf/1604.07866v3.pdf | Learning by tracking: Siamese CNN for robust target association | This paper introduces a novel approach to the task of data association within
the context of pedestrian tracking, by introducing a two-stage learning scheme
to match pairs of detections. First, a Siamese convolutional neural network
(CNN) is trained to learn descriptors encoding local spatio-temporal structures
between the two input image patches, aggregating pixel values and optical flow
information. Second, a set of contextual features derived from the position and
size of the compared input patches are combined with the CNN output by means of
a gradient boosting classifier to generate the final matching probability. This
learning approach is validated by using a linear programming based multi-person
tracker showing that even a simple and efficient tracker may outperform much
more complex models when fed with our learned matching probabilities. Results
on publicly available sequences show that our method meets state-of-the-art
standards in multiple people tracking. | ['Cristian Canton Ferrer', 'Laura Leal-Taixé', 'Konrad Schindler'] | 2016-04-26 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [ 6.38464885e-03 -4.63876992e-01 -1.18763909e-01 -3.69067848e-01
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89387ea8-2a72-492b-8196-0bdf8f1cfd05 | polite-teacher-semi-supervised-instance | 2211.03850 | null | https://arxiv.org/abs/2211.03850v1 | https://arxiv.org/pdf/2211.03850v1.pdf | Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding | We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learning framework. To filter out noisy pseudo-labels, we use confidence thresholding for bounding boxes and mask scoring for masks. The approach has been tested with CenterMask, a single-stage anchor-free detector. Tested on the COCO 2017 val dataset, our architecture significantly (approx. +8 pp. in mask AP) outperforms the baseline at different supervision regimes. To the best of our knowledge, this is one of the first works tackling the problem of semi-supervised instance segmentation and the first one devoted to an anchor-free detector. | ['Marek Cygan', 'Anna Fensel', 'Piotr Tempczyk', 'Andrzej Zapała', 'Dominik Filipiak'] | 2022-11-07 | null | null | null | null | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.65951717e-01 6.64528131e-01 -2.63754398e-01 -5.05287409e-01
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2a116527-c557-4730-8c66-4cb0a7b15e7f | sivd-dataset-of-iranian-vehicles-for-real | null | null | https://ieeexplore.ieee.org/document/10043932 | https://www.researchgate.net/profile/Farbod-Siahkali/publication/368731575_SIVD_Dataset_of_Iranian_Vehicles_for_Real-Time_Multi-Camera_Video_Tracking_and_Recognition/links/63fb9251b1704f343f84f46d/SIVD-Dataset-of-Iranian-Vehicles-for-Real-Time-Multi-Camera-Video-Tracking-and-Recognition.pdf?origin=publication_detail | SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition | In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks. | ['Mehdi Tale Masouleh', 'Seyed Amirmahdi Alavi', 'Farbod Siahkali'] | 2023-02-22 | null | null | null | icspis-2023-2 | ['object-tracking', 'vehicle-re-identification'] | ['computer-vision', 'computer-vision'] | [-3.78394783e-01 -5.99239528e-01 -2.59850830e-01 -2.03999937e-01
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6939abc7-2709-4074-bd28-80834ca28cad | dynamic-slam-semantic-monocular-visual | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0921889018308029 | https://www.researchgate.net/profile/Linhui-Xiao/publication/332149941_Dynamic-SLAM_Semantic_monocular_visual_localization_and_mapping_based_on_deep_learning_in_dynamic_environment/links/6013f1fa45851517ef22eb7d/Dynamic-SLAM-Semantic-monocular-visual-localization-and-mapping-based-on-deep-learning-in-dynamic-environment.pdf | Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment | When working in dynamic environment, traditional SLAM framework performs poorly due to interference from dynamic objects. By taking advantages of deep learning in object detection, a semantic simultaneous localization and mapping framework named Dynamic-SLAM is proposed, in order to solve the problem of SLAM in dynamic environment. First, based on the convolutional neural network, an SSD object detector which combines prior knowledge is constructed to detect dynamic objects in the newly detection thread at semantic level. Then, in view of low recall rate of the existing SSD object detection network, a missed detection compensation algorithm based on the speed invariance in adjacent frames is proposed, which greatly improves the recall rate of detection. Finally, a feature-based visual SLAM system is constructed, which processes the feature points of dynamic objects through a selective tracking algorithm in the tracking thread, to significantly reduce the error of pose estimation caused by incorrect matching. The recall rate of the system is increased from 82.3% to 99.8% compared with the original SSD network. Several experiments show that the localization accuracy of Dynamic-SLAM is higher than the state-of-the-art systems. The system successfully localizes and constructs an accurate environmental map in real-world dynamic environment by using a mobile robot. In sum, our experimental demonstrations verify that Dynamic-SLAM shows improved accuracy and robustness in robot localization and mapping comparing to the state-of-the-art SLAM system in dynamic environment. | ['Xudong Zou', 'Zheng Rong', 'Xiaosong Qiu', 'Jinge Wang', 'Linhui Xiao'] | 2019-07-06 | null | null | null | robotics-and-autonomous-systems-2019-7 | ['semantic-slam'] | ['computer-vision'] | [-2.83880055e-01 -4.28668171e-01 1.30943015e-01 -2.01783374e-01
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6799f6bc-cdf9-461c-9c4e-19adafd232a5 | visual-representation-learning-from-unlabeled | 2303.12001 | null | https://arxiv.org/abs/2303.12001v1 | https://arxiv.org/pdf/2303.12001v1.pdf | Visual Representation Learning from Unlabeled Video using Contrastive Masked Autoencoders | Masked Autoencoders (MAEs) learn self-supervised representations by randomly masking input image patches and a reconstruction loss. Alternatively, contrastive learning self-supervised methods encourage two versions of the same input to have a similar representation, while pulling apart the representations for different inputs. We propose ViC-MAE, a general method that combines both MAE and contrastive learning by pooling the local feature representations learned under the MAE reconstruction objective and leveraging this global representation under a contrastive objective across video frames. We show that visual representations learned under ViC-MAE generalize well to both video classification and image classification tasks. Using a backbone ViT-B/16 network pre-trained on the Moments in Time (MiT) dataset, we obtain state-of-the-art transfer learning from video to images on Imagenet-1k by improving 1.58% in absolute top-1 accuracy from a recent previous work. Moreover, our method maintains a competitive transfer-learning performance of 81.50% top-1 accuracy on the Kinetics-400 video classification benchmark. In addition, we show that despite its simplicity, ViC-MAE yields improved results compared to combining MAE pre-training with previously proposed contrastive objectives such as VicReg and SiamSiam. | ['Vicente Ordonez', 'Ruben Villegas', 'Jefferson Hernandez'] | 2023-03-21 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 3.32070619e-01 -1.42203197e-02 -3.70307356e-01 -3.20433557e-01
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9291034b-21b7-458a-ab2b-6a7cba6e8f06 | optimising-2d-pose-representation-improve | 2209.00618 | null | https://arxiv.org/abs/2209.00618v1 | https://arxiv.org/pdf/2209.00618v1.pdf | Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation | This paper addresses the problem of 2D pose representation during unsupervised 2D to 3D pose lifting to improve the accuracy, stability and generalisability of 3D human pose estimation (HPE) models. All unsupervised 2D-3D HPE approaches provide the entire 2D kinematic skeleton to a model during training. We argue that this is sub-optimal and disruptive as long-range correlations are induced between independent 2D key points and predicted 3D ordinates during training. To this end, we conduct the following study. With a maximum architecture capacity of 6 residual blocks, we evaluate the performance of 5 models which each represent a 2D pose differently during the adversarial unsupervised 2D-3D HPE process. Additionally, we show the correlations between 2D key points which are learned during the training process, highlighting the unintuitive correlations induced when an entire 2D pose is provided to a lifting model. Our results show that the most optimal representation of a 2D pose is that of two independent segments, the torso and legs, with no shared features between each lifting network. This approach decreased the average error by 20\% on the Human3.6M dataset when compared to a model with a near identical parameter count trained on the entire 2D kinematic skeleton. Furthermore, due to the complex nature of adversarial learning, we show how this representation can also improve convergence during training allowing for an optimum result to be obtained more often. | ['Hansung Kim', 'Srinandan Dasmahapatra', 'Peter Hardy'] | 2022-09-01 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 1.13160104e-01 6.56728268e-01 -4.23215926e-02 -2.78109275e-02
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67374fdc-5c0a-4f91-9ba8-e188e3cbf4ba | do-machine-learning-models-learn-common-sense | 2303.01433 | null | https://arxiv.org/abs/2303.01433v2 | https://arxiv.org/pdf/2303.01433v2.pdf | Do Machine Learning Models Learn Statistical Rules Inferred from Data? | Machine learning models can make critical errors that are easily hidden within vast amounts of data. Such errors often run counter to rules based on human intuition. However, rules based on human knowledge are challenging to scale or to even formalize. We thereby seek to infer statistical rules from the data and quantify the extent to which a model has learned them. We propose a framework SQRL that integrates logic-based methods with statistical inference to derive these rules from a model's training data without supervision. We further show how to adapt models at test time to reduce rule violations and produce more coherent predictions. SQRL generates up to 300K rules over datasets from vision, tabular, and language settings. We uncover up to 158K violations of those rules by state-of-the-art models for classification, object detection, and data imputation. Test-time adaptation reduces these violations by up to 68.7% with relative performance improvement up to 32%. SQRL is available at https://github.com/DebugML/sqrl. | ['Eric Wong', 'Mayur Naik', 'Yinjun Wu', 'Aaditya Naik'] | 2023-03-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.89249301e-01 4.57611501e-01 -3.72345120e-01 -9.11429882e-01
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5b85c55e-d891-4698-ab5b-3627dd119612 | jointly-modeling-aspect-and-polarity-for | 2109.07680 | null | https://arxiv.org/abs/2109.07680v3 | https://arxiv.org/pdf/2109.07680v3.pdf | Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews | Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to specify which aspect of the entity comes up in opinion while ACP aims to specify the polarity of each aspect category from the ACD task. The previous works mostly propose separate solutions for these two sub-tasks. This paper focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The proposed method carries out multi-label classification where four different deep models were employed and comparatively evaluated to examine their performance. A dataset of Persian reviews was collected from CinemaTicket website including 2200 samples from 14 categories. The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics. The results indicate the high applicability and preference of the CNN and GRU models in comparison to LSTM and Bi-LSTM. | ['Jafar Razmara', 'Milad Vazan'] | 2021-09-16 | null | null | null | null | ['aspect-category-polarity', 'persian-sentiment-anlysis', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.08694223e-02 -1.75747886e-01 -1.73067912e-01 -6.31437480e-01
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02fd5844-12fc-470d-9614-227247a97e7c | representation-learning-on-hyper-relational | 2305.18256 | null | https://arxiv.org/abs/2305.18256v2 | https://arxiv.org/pdf/2305.18256v2.pdf | Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers | A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets. | ['Joyce Jiyoung Whang', 'Jaejun Lee', 'Chanyoung Chung'] | 2023-05-29 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [-1.22548454e-01 4.24650311e-01 -6.88970029e-01 -6.17618084e-01
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4fdc350b-967f-4119-820b-298d1138fe1a | lukthung-classification-using-neural-networks | 1908.08769 | null | https://arxiv.org/abs/1908.08769v2 | https://arxiv.org/pdf/1908.08769v2.pdf | Lukthung Classification Using Neural Networks on Lyrics and Audios | Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done on western songs which differ from Thai songs. Lukthung, a distinctive and long-established type of Thai music, is one of the most popular music genres in Thailand and has a specific group of listeners. In this paper, we develop neural networks to classify such Lukthung genre from others using both lyrics and audios. Words used in Lukthung songs are particularly poetical, and their musical styles are uniquely composed of traditional Thai instruments. We leverage these two main characteristics by building a lyrics model based on bag-of-words (BoW), and an audio model using a convolutional neural network (CNN) architecture. We then aggregate the intermediate features learned from both models to build a final classifier. Our results show that the proposed three models outperform all of the standard classifiers where the combined model yields the best $F_1$ score of 0.86, allowing Lukthung classification to be applicable to personalized recommendation for Thai audience. | ['Kasina Euchukanonchai', 'Naruemon Pratanwanich', 'Kawisorn Kamtue', 'Dittaya Wanvarie'] | 2019-08-23 | null | null | null | null | ['genre-classification', 'music-classification'] | ['computer-vision', 'music'] | [-9.26576778e-02 -7.47345567e-01 -5.34369588e-01 -9.69236046e-02
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78a7591c-172c-42f2-b76c-189c7e6b4605 | ihs-rd-belarus-at-semeval-2016-task-9 | null | null | https://aclanthology.org/S16-1187 | https://aclanthology.org/S16-1187.pdf | IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping. | null | ['Maria Yermakovich', 'Artsiom Artsymenia', 'Palina Dounar'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f4354f48-37b5-4acf-890f-a13053171108 | judge-localize-and-edit-ensuring-visual | 2212.03507 | null | https://arxiv.org/abs/2212.03507v2 | https://arxiv.org/pdf/2212.03507v2.pdf | Judge, Localize, and Edit: Ensuring Visual Commonsense Morality for Text-to-Image Generation | Text-to-image generation methods produce high-resolution and high-quality images, but these methods should not produce immoral images that may contain inappropriate content from the commonsense morality perspective. Conventional approaches often neglect these ethical concerns, and existing solutions are limited in avoiding immoral image generation. In this paper, we aim to automatically judge the immorality of synthesized images and manipulate these images into a moral alternative. To this end, we build a model that has the three main primitives: (1) our model recognizes the visual commonsense immorality of a given image, (2) our model localizes or highlights immoral visual (and textual) attributes that make the image immoral, and (3) our model manipulates a given immoral image into a morally-qualifying alternative. We experiment with the state-of-the-art Stable Diffusion text-to-image generation model and show the effectiveness of our ethical image manipulation. Our human study confirms that ours is indeed able to generate morally-satisfying images from immoral ones. Our implementation will be publicly available upon publication to be widely used as a new safety checker for text-to-image generation models. | ['Jinkyu Kim', 'Suhong Moon', 'Seongbeom Park'] | 2022-12-07 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.02096438e-01 4.51022685e-01 7.37449527e-02 -2.08111003e-01
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4449b304-7a3f-422b-909a-febbfa3f267a | multi-task-learning-for-sparsity-pattern | 2212.08697 | null | https://arxiv.org/abs/2212.08697v1 | https://arxiv.org/pdf/2212.08697v1.pdf | Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach | We extend best-subset selection to linear Multi-Task Learning (MTL), where a set of linear models are jointly trained on a collection of datasets (``tasks''). Allowing the regression coefficients of tasks to have different sparsity patterns (i.e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together. This allows models to borrow strength during variable selection even when the coefficient values differ markedly between tasks. We express our modeling framework as a Mixed-Integer Program, and propose efficient and scalable algorithms based on block coordinate descent and combinatorial local search. We show our estimator achieves statistically optimal prediction rates. Importantly, our theory characterizes how our estimator leverages the shared support information across tasks to achieve better variable selection performance. We evaluate the performance of our method in simulations and two biology applications. Our proposed approaches outperform other sparse MTL methods in variable selection and prediction accuracy. Interestingly, penalties that shrink the supports together often outperform penalties that shrink the coefficient values together. We will release an R package implementing our methods. | ['Rahul Mazumder', 'Giovanni Parmigiani', 'Kenneth T. Kishida', 'Kayhan Behdin', 'Gabriel Loewinger'] | 2022-12-16 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.95162910e-01 -2.47226357e-01 -8.06918383e-01 -7.41919339e-01
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f07db327-89ad-4c7e-919b-5050d3581d4b | domain-generalization-for-domain-linked | 2306.00879 | null | https://arxiv.org/abs/2306.00879v1 | https://arxiv.org/pdf/2306.00879v1.pdf | Domain Generalization for Domain-Linked Classes | Domain generalization (DG) focuses on transferring domain-invariant knowledge from multiple source domains (available at train time) to an, a priori, unseen target domain(s). This requires a class to be expressed in multiple domains for the learning algorithm to break the spurious correlations between domain and class. However, in the real-world, classes may often be domain-linked, i.e. expressed only in a specific domain, which leads to extremely poor generalization performance for these classes. In this work, we aim to learn generalizable representations for these domain-linked classes by transferring domain-invariant knowledge from classes expressed in multiple source domains (domain-shared classes). To this end, we introduce this task to the community and propose a Fair and cONtrastive feature-space regularization algorithm for Domain-linked DG, FOND. Rigorous and reproducible experiments with baselines across popular DG tasks demonstrate our method and its variants' ability to accomplish state-of-the-art DG results for domain-linked classes. We also provide practical insights on data conditions that increase domain-linked class generalizability to tackle real-world data scarcity. | ['Sirisha Rambhatla', 'Saad Hossain', 'Kimathi Kaai'] | 2023-06-01 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 4.23542053e-01 1.95551589e-02 -3.29595029e-01 -6.96774244e-01
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bdc744c4-c93e-40cc-ad75-b8c90da78b0c | data-types-as-a-more-ergonomic-frontend-for | 2210.04826 | null | https://arxiv.org/abs/2210.04826v1 | https://arxiv.org/pdf/2210.04826v1.pdf | Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming | Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine the domain of valid program solutions. Backus Normal Form is the most popular interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its derivatives have the disadvantage of interleaving the grammar language and the target language of the program. We propose to embed the grammar as an internal Domain-Specific Language in the host language of the framework. This approach has the same expressive power as BNF and EBNF while using the host language type-system to take advantage of all the existing tooling: linters, formatters, type-checkers, autocomplete, and legacy code support. These tools have a practical utility in designing software in general, and GP systems in particular. We also present Meta-Handlers, user-defined overrides of the tree-generation system. This technique extends our object-oriented encoding with more practicability and expressive power than existing CFG approaches, achieving the same expressive power of Attribute Grammars, but without the grammar vs target language duality. Furthermore, we evidence that this approach is feasible, showing an example Python implementation as proof. We also compare our approach against textual BNF-representations w.r.t. expressive power and ergonomics. These advantages do not come at the cost of performance, as shown by our empirical evaluation on 5 benchmarks of our example implementation against PonyGE2. We conclude that our approach has better ergonomics with the same expressive power and performance of textual BNF-based grammar encodings. | ['Alcides Fonseca', 'Pedro Barbosa', 'Paulo Canelas', 'Leon Ingelse', 'Guilherme Espada'] | 2022-10-10 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.47093046e-02 4.73786116e-01 -9.50413719e-02 -1.57224804e-01
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2f0aa9dd-2175-45e1-bc3e-d222fb0a2cf3 | video-relation-detection-with-trajectory | 2101.08165 | null | https://arxiv.org/abs/2101.08165v1 | https://arxiv.org/pdf/2101.08165v1.pdf | Video Relation Detection with Trajectory-aware Multi-modal Features | Video relation detection problem refers to the detection of the relationship between different objects in videos, such as spatial relationship and action relationship. In this paper, we present video relation detection with trajectory-aware multi-modal features to solve this task. Considering the complexity of doing visual relation detection in videos, we decompose this task into three sub-tasks: object detection, trajectory proposal and relation prediction. We use the state-of-the-art object detection method to ensure the accuracy of object trajectory detection and multi-modal feature representation to help the prediction of relation between objects. Our method won the first place on the video relation detection task of Video Relation Understanding Grand Challenge in ACM Multimedia 2020 with 11.74\% mAP, which surpasses other methods by a large margin. | ['Si Liu', 'Guanghui Ren', 'Wentao Xie'] | 2021-01-20 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [ 6.65546358e-02 -2.49494329e-01 -2.76475430e-01 -2.97258310e-02
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9b8c7e97-28b4-4338-adfa-6aa45c0d2f1a | emotion-recognition-from-multiple-modalities | 2108.10152 | null | https://arxiv.org/abs/2108.10152v1 | https://arxiv.org/pdf/2108.10152v1.pdf | Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies | Humans are emotional creatures. Multiple modalities are often involved when we express emotions, whether we do so explicitly (e.g., facial expression, speech) or implicitly (e.g., text, image). Enabling machines to have emotional intelligence, i.e., recognizing, interpreting, processing, and simulating emotions, is becoming increasingly important. In this tutorial, we discuss several key aspects of multi-modal emotion recognition (MER). We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks, followed by the description of main challenges in MER. Furthermore, we present some representative approaches on representation learning of each affective modality, feature fusion of different affective modalities, classifier optimization for MER, and domain adaptation for MER. Finally, we outline several real-world applications and discuss some future directions. | ['Kurt Keutzer', 'Guiguang Ding', 'Jufeng Yang', 'Guoli Jia', 'Sicheng Zhao'] | 2021-08-18 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 3.39635164e-01 -1.55130759e-01 -5.10841608e-02 -8.36963177e-01
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e0c655a1-77ad-49dc-872a-4049d8efc47c | improving-supervised-drug-protein-relation | null | null | https://aclanthology.org/2022.bionlp-1.16 | https://aclanthology.org/2022.bionlp-1.16.pdf | Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models | This paper proposes novel drug-protein relation extraction models that indirectly utilize distant supervision data. Concretely, instead of adding distant supervision data to the manually annotated training data, our models incorporate distantly supervised models that are relation extraction models trained with distant supervision data. Distantly supervised learning has been proposed to generate a large amount of pseudo-training data at low cost. However, there is still a problem of low prediction performance due to the inclusion of mislabeled data. Therefore, several methods have been proposed to suppress the effects of noisy cases by utilizing some manually annotated training data. However, their performance is lower than that of supervised learning on manually annotated data because mislabeled data that cannot be fully suppressed becomes noise when training the model. To overcome this issue, our methods indirectly utilize distant supervision data with manually annotated training data. The experimental results on the DrugProt corpus in the BioCreative VII Track 1 showed that our proposed model can consistently improve the supervised models in different settings. | ['Yutaka Sasaki', 'Makoto Miwa', 'Naoki Iinuma'] | null | null | null | null | bionlp-acl-2022-5 | ['drugprot'] | ['natural-language-processing'] | [ 2.92718261e-01 5.05550683e-01 -6.11125529e-01 -7.06491530e-01
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c6a34b34-05e3-4448-abdd-1729491aed04 | implicit-spoken-language-diarization | 2306.12913 | null | https://arxiv.org/abs/2306.12913v1 | https://arxiv.org/pdf/2306.12913v1.pdf | Implicit spoken language diarization | Spoken language diarization (LD) and related tasks are mostly explored using the phonotactic approach. Phonotactic approaches mostly use explicit way of language modeling, hence requiring intermediate phoneme modeling and transcribed data. Alternatively, the ability of deep learning approaches to model temporal dynamics may help for the implicit modeling of language information through deep embedding vectors. Hence this work initially explores the available speaker diarization frameworks that capture speaker information implicitly to perform LD tasks. The performance of the LD system on synthetic code-switch data using the end-to-end x-vector approach is 6.78% and 7.06%, and for practical data is 22.50% and 60.38%, in terms of diarization error rate and Jaccard error rate (JER), respectively. The performance degradation is due to the data imbalance and resolved to some extent by using pre-trained wave2vec embeddings that provide a relative improvement of 30.74% in terms of JER. | ['S. R. Mahadeva Prasanna', 'Amartya Chowdhury', 'Jagabandhu Mishra'] | 2023-06-22 | null | null | null | null | ['speaker-diarization'] | ['speech'] | [-8.35962370e-02 3.30408633e-01 -9.25511494e-02 -4.54644829e-01
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2067b712-766c-46cd-8f64-ba62af100bca | results-of-semtab-2021 | null | null | http://ceur-ws.org/Vol-3103/ | http://ceur-ws.org/Vol-3103/paper0.pdf | Results of SemTab 2021 | SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems. | ['Nora Abdelmageed', 'Kavitha Srinivas', 'Juan Sequeda', 'Ernesto Jimenez-Ruiz', 'Oktie Hassanzadeh', 'Vasilis Efthymiou', 'Jiaoyan Chen', 'Vincenzo Cutrona'] | 2021-10-27 | null | null | null | iswc-2021-10 | ['ontology-matching', 'table-annotation', 'table-annotation'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-7.98054878e-03 5.49871027e-01 -5.40946662e-01 -5.08502051e-02
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102a1c55-6113-4da8-8b45-5ecf3445d73e | learning-video-object-segmentation-with | 1704.05737 | null | http://arxiv.org/abs/1704.05737v2 | http://arxiv.org/pdf/1704.05737v2.pdf | Learning Video Object Segmentation with Visual Memory | This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the evolution of objects over time. The module to build a "visual
memory" in video, i.e., a joint representation of all the video frames, is
realized with a convolutional recurrent unit learned from a small number of
training video sequences. Given a video frame as input, our approach assigns
each pixel an object or background label based on the learned spatio-temporal
features as well as the "visual memory" specific to the video, acquired
automatically without any manually-annotated frames. The visual memory is
implemented with convolutional gated recurrent units, which allows to propagate
spatial information over time. We evaluate our method extensively on two
benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show
state-of-the-art results. For example, our approach outperforms the top method
on the DAVIS dataset by nearly 6%. We also provide an extensive ablative
analysis to investigate the influence of each component in the proposed
framework. | ['Cordelia Schmid', 'Pavel Tokmakov', 'Karteek Alahari'] | 2017-04-19 | learning-video-object-segmentation-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Tokmakov_Learning_Video_Object_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tokmakov_Learning_Video_Object_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.91746637e-01 -2.84691304e-01 -3.08577955e-01 -2.27056205e-01
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7a0c6d00-1143-4c28-930e-ec644d34949b | a-crystal-specific-pre-training-framework-for | 2306.05344 | null | https://arxiv.org/abs/2306.05344v2 | https://arxiv.org/pdf/2306.05344v2.pdf | A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction | Crystal property prediction is a crucial aspect of developing novel materials. However, there are two technical challenges to be addressed for speeding up the investigation of crystals. First, labeling crystal properties is intrinsically difficult due to the high cost and time involved in physical simulations or lab experiments. Second, crystals adhere to a specific quantum chemical principle known as periodic invariance, which is often not captured by existing machine learning methods. To overcome these challenges, we propose the crystal-specific pre-training framework for learning crystal representations with self-supervision. The framework designs a mutex mask strategy for enhancing representation learning so as to alleviate the limited labels available for crystal property prediction. Moreover, we take into account the specific periodic invariance in crystal structures by developing a periodic invariance multi-graph module and periodic attribute learning within our framework. This framework has been tested on eight different tasks. The experimental results on these tasks show that the framework achieves promising prediction performance and is able to outperform recent strong baselines. | ['Bin Yang', 'Chenjuan Guo', 'Jilin Hu', 'Yanru Song', 'Haomin Yu'] | 2023-06-08 | null | null | null | null | ['property-prediction', 'physical-simulations'] | ['medical', 'miscellaneous'] | [ 4.77542698e-01 -1.43693417e-01 -5.22441685e-01 -4.83168066e-01
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fb009fd8-6099-4a35-af91-3b01ab623de0 | improved-anomaly-detection-in-crowded-scenes | 1304.0886 | null | http://arxiv.org/abs/1304.0886v1 | http://arxiv.org/pdf/1304.0886v1.pdf | Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture | A robust and efficient anomaly detection technique is proposed, capable of
dealing with crowded scenes where traditional tracking based approaches tend to
fail. Initial foreground segmentation of the input frames confines the analysis
to foreground objects and effectively ignores irrelevant background dynamics.
Input frames are split into non-overlapping cells, followed by extracting
features based on motion, size and texture from each cell. Each feature type is
independently analysed for the presence of an anomaly. Unlike most methods, a
refined estimate of object motion is achieved by computing the optical flow of
only the foreground pixels. The motion and size features are modelled by an
approximated version of kernel density estimation, which is computationally
efficient even for large training datasets. Texture features are modelled by an
adaptively grown codebook, with the number of entries in the codebook selected
in an online fashion. Experiments on the recently published UCSD Anomaly
Detection dataset show that the proposed method obtains considerably better
results than three recent approaches: MPPCA, social force, and mixture of
dynamic textures (MDT). The proposed method is also several orders of magnitude
faster than MDT, the next best performing method. | ['Vikas Reddy', 'Conrad Sanderson', 'Brian C. Lovell'] | 2013-04-03 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 1.94241732e-01 -4.10290033e-01 2.90151119e-01 3.52527872e-02
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b2ded9cf-c9e0-448d-856d-a031306b0e1b | hififace-3d-shape-and-semantic-prior-guided | 2106.09965 | null | https://arxiv.org/abs/2106.09965v1 | https://arxiv.org/pdf/2106.09965v1.pdf | HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping | In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods. | ['Rongrong Ji', 'Feiyue Huang', 'Yongjian Wu', 'Jilin Li', 'Chengjie Wang', 'Ying Tai', 'Wenqing Chu', 'Junwei Zhu', 'Xu Chen', 'YuHan Wang'] | 2021-06-18 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-0.09077884 0.14758494 0.18703309 -0.48331594 -0.31828627 -0.42190543
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4821d5bb-802c-4e00-a65e-fe6330d8736f | a-synthetic-hyperspectral-array-video | 2301.07551 | null | https://arxiv.org/abs/2301.07551v3 | https://arxiv.org/pdf/2301.07551v3.pdf | Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding | In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community. | ['André Kaup', 'Jürgen Seiler', 'Frank Sippel'] | 2023-01-18 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.50459313e-01 -4.31636989e-01 1.17006838e-01 4.02682312e-02
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9f2dfa28-7af5-4274-8854-00541eac14d3 | parallelizing-optical-flow-estimation-on-an | 2305.13055 | null | https://arxiv.org/abs/2305.13055v1 | https://arxiv.org/pdf/2305.13055v1.pdf | Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation | Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50 MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous single-core implementations. | ['Luca Benini', 'Michele Magno', 'Jonas Kühne'] | 2023-05-22 | null | null | null | null | ['autonomous-navigation'] | ['computer-vision'] | [-5.38917556e-02 -3.56894404e-01 8.06699228e-03 3.21404964e-01
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cf34c359-2695-4bcb-b38d-6337db3fe86d | speech-enhancement-in-adverse-environments | 1803.00396 | null | http://arxiv.org/abs/1803.00396v1 | http://arxiv.org/pdf/1803.00396v1.pdf | Speech Enhancement in Adverse Environments Based on Non-stationary Noise-driven Spectral Subtraction and SNR-dependent Phase Compensation | A two-step enhancement method based on spectral subtraction and phase
spectrum compensation is presented in this paper for noisy speeches in adverse
environments involving non-stationary noise and medium to low levels of SNR.
The magnitude of the noisy speech spectrum is modified in the first step of the
proposed method by a spectral subtraction approach, where a new noise
estimation method based on the low frequency information of the noisy speech is
introduced. We argue that this method of noise estimation is capable of
estimating the non-stationary noise accurately. The phase spectrum of the noisy
speech is modified in the second step consisting of phase spectrum
compensation, where an SNR-dependent approach is incorporated to determine the
amount of compensation to be imposed on the phase spectrum. A modified complex
spectrum is obtained by aggregating the magnitude from the spectral subtraction
step and modified phase spectrum from the phase compensation step, which is
found to be a better representation of enhanced speech spectrum. Speech files
available in the NOIZEUS database are used to carry extensive simulations for
evaluation of the proposed method. | [] | 2018-02-19 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 8.56123805e-01 -3.44240516e-01 5.73582172e-01 -3.41432840e-02
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60fac3b7-dc43-43b1-9147-c9204352b8c4 | evaluating-variants-of-wav2vec-2-0-on | null | null | https://ieeexplore.ieee.org/document/10096552 | https://ieeexplore.ieee.org/document/10096552 | Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst Tasks | The search for emotional biomarkers within the human voice is a challenging research area. Previous studies focused on predicting affective state from speech; this study explores various tasks on affective vocal bursts. Borrowing the success of self-supervised learning in automatic speech recognition, we extracted acoustic embedding using variants of wav2vec 2.0 for four affective vocal bursts tasks: High, Two, Culture, and Type. Using a similar architecture for all tasks, the evaluation of acoustic embeddings reveals the potential use of wav2vec 2.0 variants over the conventional acoustic features in affective vocal bursts tasks. We evaluated both conventional acoustic features and these acoustic embeddings on the different number of twenty seeds evaluation and reported the maximum and average scores with their standard deviations in the validation set. Three high scores from these validations for all tasks assist the generation of predictions for the test set. We compared the test scores with previous studies and obtained remarkable improvements. | ['Akira Sasou', 'Bagus Tris Atmaja'] | 2023-05-05 | null | null | null | icassp-2023-5 | ['culture', 'type'] | ['speech', 'speech'] | [-2.47771785e-01 2.97064126e-01 2.23338544e-01 -3.77354383e-01
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16e0b0bb-bd8c-4f0c-89f9-0bb19681a5ab | roomdreamer-text-driven-3d-indoor-scene | 2305.11337 | null | https://arxiv.org/abs/2305.11337v1 | https://arxiv.org/pdf/2305.11337v1.pdf | RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture | The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose "RoomDreamer", which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our work addresses the challenge of synthesizing both geometry and texture aligned to the input scene structure and prompt simultaneously. The key insight is that a scene should be treated as a whole, taking into account both scene texture and geometry. The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization. Geometry Guided Diffusion for 3D Scene guarantees the consistency of the scene style by applying the 2D prior to the entire scene simultaneously. Mesh Optimization improves the geometry and texture jointly and eliminates the artifacts in the scanned scene. To validate the proposed method, real indoor scenes scanned with smartphones are used for extensive experiments, through which the effectiveness of our method is demonstrated. | ['Yang Zhao', 'Junsong Yuan', 'Feng Tang', 'Kai Kang', 'Hongyu Xu', 'Liangliang Cao', 'Liangchen Song'] | 2023-05-18 | null | null | null | null | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 6.21787548e-01 -4.91789021e-02 4.20427471e-01 -4.03477073e-01
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d5d5a083-f61c-4a9e-9443-2d431918cf4e | summarizing-and-exploring-tabular-data-in | 2005.11490 | null | https://arxiv.org/abs/2005.11490v3 | https://arxiv.org/pdf/2005.11490v3.pdf | Summarizing and Exploring Tabular Data in Conversational Search | Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset. It includes annotated table summaries, which not only answer questions but also help people explore other information in the table. We utilize this dataset to develop automatic table summarization systems as SOTA baselines. Based on the experimental results, we identify challenges and point out future research directions that this resource will support. | ['Zhuyun Dai', 'Jamie Callan', 'Shuo Zhang', 'Krisztian Balog'] | 2020-05-23 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 7.54549503e-02 5.64251661e-01 -5.51068127e-01 -1.73816860e-01
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fd76666b-d3b3-4606-ada1-9e2da60cf8f8 | semeval-2019-shared-task-cross-lingual | 1805.12386 | null | https://arxiv.org/abs/1805.12386v4 | https://arxiv.org/pdf/1805.12386v4.pdf | SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation | We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general. Furthermore, existing applications for semantic evaluation that are based on UCCA will greatly benefit from better automatic methods for UCCA parsing. The competition website is https://competitions.codalab.org/competitions/19160 | ['Elior Sulem', 'Daniel Hershcovich', 'Omri Abend', 'Leshem Choshen', 'Zohar Aizenbud', 'Ari Rappoport'] | 2018-05-31 | null | null | null | null | ['ucca-parsing'] | ['natural-language-processing'] | [ 5.67940697e-02 5.09091496e-01 -2.38392845e-01 -6.91092610e-01
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29add106-c125-4ed7-8a79-617011662963 | text-mining-of-stocktwits-data-for-predicting | 2103.16388 | null | https://arxiv.org/abs/2103.16388v1 | https://arxiv.org/pdf/2103.16388v1.pdf | Text Mining of Stocktwits Data for Predicting Stock Prices | Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement. | ['Matloob Khushi', 'Usman Naseem', 'Shreya Narang', 'Priyanka Mandal', 'Mukul Jaggi'] | 2021-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-9.36046243e-01 -4.65400033e-02 -5.58699548e-01 -4.80303228e-01
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539a3043-4b61-43c7-9817-8fd511d88b58 | star-sql-guided-pre-training-for-context | 2210.11888 | null | https://arxiv.org/abs/2210.11888v2 | https://arxiv.org/pdf/2210.11888v2.pdf | STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing | In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation. In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks (SParC and CoSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard. We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star. | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Weijie Li', 'Zheng Cao', 'Binhua Li', 'Bowen Li', 'Min Yang', 'Binyuan Hui', 'Xiangyu Li', 'ZeFeng Cai'] | 2022-10-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 9.96451974e-02 3.21381181e-01 -2.36976579e-01 -1.00060284e+00
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