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91564374-f631-41c6-a9eb-b8498486ae8a | facegan-facial-attribute-controllable | 2011.04439 | null | https://arxiv.org/abs/2011.04439v1 | https://arxiv.org/pdf/2011.04439v1.pdf | FACEGAN: Facial Attribute Controllable rEenactment GAN | The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have demonstrated high quality results by combining the facial landmark based motion representations with the generative adversarial networks. These models perform best if the source and driving images depict the same person or if the facial structures are otherwise very similar. However, if the identity differs, the driving facial structures leak to the output distorting the reenactment result. We propose a novel Facial Attribute Controllable rEenactment GAN (FACEGAN), which transfers the facial motion from the driving face via the Action Unit (AU) representation. Unlike facial landmarks, the AUs are independent of the facial structure preventing the identity leak. Moreover, AUs provide a human interpretable way to control the reenactment. FACEGAN processes background and face regions separately for optimized output quality. The extensive quantitative and qualitative comparisons show a clear improvement over the state-of-the-art in a single source reenactment task. The results are best illustrated in the reenactment video provided in the supplementary material. The source code will be made available upon publication of the paper. | ['Esa Rahtu', 'Juho Kannala', 'Soumya Tripathy'] | 2020-11-09 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.96741664e-01 4.87746865e-01 5.96907474e-02 -2.00423852e-01
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bd884041-01fc-40b7-a4a7-c079ae465286 | self-supervised-context-aware-style | 2206.12559 | null | https://arxiv.org/abs/2206.12559v1 | https://arxiv.org/pdf/2206.12559v1.pdf | Self-supervised Context-aware Style Representation for Expressive Speech Synthesis | Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner. It leverages an emotion lexicon and uses contrastive learning and deep clustering. We further integrate the style representation as a conditioned embedding in a multi-style Transformer TTS. Comparing with multi-style TTS by predicting style tags trained on the same dataset but with human annotations, our method achieves improved results according to subjective evaluations on both in-domain and out-of-domain test sets in audiobook speech. Moreover, with implicit context-aware style representation, the emotion transition of synthesized audio in a long paragraph appears more natural. The audio samples are available on the demo web. | ['Jian-Yun Nie', 'Ruihua Song', 'Lei He', 'Shaofei Zhang', 'Xi Wang', 'Yihan Wu'] | 2022-06-25 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.68039787e-01 9.78319719e-03 3.62090603e-03 -6.27679288e-01
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97edeff5-7045-4353-b910-cf30a81e2ac5 | stage-span-tagging-and-greedy-inference | 2211.15003 | null | https://arxiv.org/abs/2211.15003v3 | https://arxiv.org/pdf/2211.15003v3.pdf | STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE. | ['Dangyang Chen', 'Rui Fang', 'Yuanyuan Fu', 'Xian-Ling Mao', 'Wei Wei', 'Shuo Liang'] | 2022-11-28 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 0.33006763 -0.30675486 -0.38783488 -0.40465072 -0.6531443 -0.69028074
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dab0de67-9db5-48ec-a8ad-49ca3249c77a | soundstorm-efficient-parallel-audio | 2305.09636 | null | https://arxiv.org/abs/2305.09636v1 | https://arxiv.org/pdf/2305.09636v1.pdf | SoundStorm: Efficient Parallel Audio Generation | We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach of AudioLM, our model produces audio of the same quality and with higher consistency in voice and acoustic conditions, while being two orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5 seconds on a TPU-v4. We demonstrate the ability of our model to scale audio generation to longer sequences by synthesizing high-quality, natural dialogue segments, given a transcript annotated with speaker turns and a short prompt with the speakers' voices. | ['Marco Tagliasacchi', 'Neil Zeghidour', 'Eugene Kharitonov', 'Damien Vincent', 'Matt Sharifi', 'Zalán Borsos'] | 2023-05-16 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.17527920e-01 5.79669178e-01 4.57616538e-01 -2.97402203e-01
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98b83178-511b-46dc-9d68-5b38c47c8fec | a-joint-convolution-auto-encoder-network-for | 2201.10736 | null | https://arxiv.org/abs/2201.10736v1 | https://arxiv.org/pdf/2201.10736v1.pdf | A Joint Convolution Auto-encoder Network for Infrared and Visible Image Fusion | Background: Leaning redundant and complementary relationships is a critical step in the human visual system. Inspired by the infrared cognition ability of crotalinae animals, we design a joint convolution auto-encoder (JCAE) network for infrared and visible image fusion. Methods: Our key insight is to feed infrared and visible pair images into the network simultaneously and separate an encoder stream into two private branches and one common branch, the private branch works for complementary features learning and the common branch does for redundant features learning. We also build two fusion rules to integrate redundant and complementary features into their fused feature which are then fed into the decoder layer to produce the final fused image. We detail the structure, fusion rule and explain its multi-task loss function. Results: Our JCAE network achieves good results in terms of both subjective effect and objective evaluation metrics. | ['Xiao-Jun Wu', 'Xiaoqing Luo', 'Mengyu Xiong', 'Yuanhao Gao', 'Zhancheng Zhang'] | 2022-01-26 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.87022680e-01 -1.95167542e-01 8.27736408e-02 -5.41997313e-01
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a4bb807e-3bb6-4134-8cc0-b315e0b798d9 | towards-direct-comparison-of-community | 2209.12841 | null | https://arxiv.org/abs/2209.12841v1 | https://arxiv.org/pdf/2209.12841v1.pdf | Towards Direct Comparison of Community Structures in Social Networks | Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely \emph{Topological Variance (TV)} is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms. | ['Anupam Biswas', 'Soumita Das'] | 2022-09-26 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.45303264e-01 -9.98493750e-04 2.94941843e-01 -4.71678227e-02
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02b55186-ba82-498e-94f3-746a83f8b943 | a-modulation-front-end-for-music-audio | 2105.11836 | null | https://arxiv.org/abs/2105.11836v1 | https://arxiv.org/pdf/2105.11836v1.pdf | A Modulation Front-End for Music Audio Tagging | Convolutional Neural Networks have been extensively explored in the task of automatic music tagging. The problem can be approached by using either engineered time-frequency features or raw audio as input. Modulation filter bank representations that have been actively researched as a basis for timbre perception have the potential to facilitate the extraction of perceptually salient features. We explore end-to-end learned front-ends for audio representation learning, ModNet and SincModNet, that incorporate a temporal modulation processing block. The structure is effectively analogous to a modulation filter bank, where the FIR filter center frequencies are learned in a data-driven manner. The expectation is that a perceptually motivated filter bank can provide a useful representation for identifying music features. Our experimental results provide a fully visualisable and interpretable front-end temporal modulation decomposition of raw audio. We evaluate the performance of our model against the state-of-the-art of music tagging on the MagnaTagATune dataset. We analyse the impact on performance for particular tags when time-frequency bands are subsampled by the modulation filters at a progressively reduced rate. We demonstrate that modulation filtering provides promising results for music tagging and feature representation, without using extensive musical domain knowledge in the design of this front-end. | ['György Fazekas', 'Charalampos Saitis', 'Cyrus Vahidi'] | 2021-05-25 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.68157613e-01 1.21669960e-03 -6.57962710e-02 -2.05475956e-01
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07252850-8623-4f51-88f4-2c6fb08d9d01 | a-machine-learning-data-fusion-model-for-soil | 2206.09649 | null | https://arxiv.org/abs/2206.09649v2 | https://arxiv.org/pdf/2206.09649v2.pdf | A Machine Learning Data Fusion Model for Soil Moisture Retrieval | We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.727 and ubRMSE of 0.054, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 13 other soil moisture works at different locations, and an ablation study was used to identify important predictors. | ['Varun Gulshan', 'Grey Nearing', 'Vishal Batchu'] | 2022-06-20 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 2.34096006e-01 3.96643952e-02 -3.35033774e-01 -4.56477642e-01
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3f0a3148-0eb8-4ddd-9224-20adffaaa139 | multimodal-explanations-justifying-decisions | 1802.08129 | null | http://arxiv.org/abs/1802.08129v1 | http://arxiv.org/pdf/1802.08129v1.pdf | Multimodal Explanations: Justifying Decisions and Pointing to the Evidence | Deep models that are both effective and explainable are desirable in many
settings; prior explainable models have been unimodal, offering either
image-based visualization of attention weights or text-based generation of
post-hoc justifications. We propose a multimodal approach to explanation, and
argue that the two modalities provide complementary explanatory strengths. We
collect two new datasets to define and evaluate this task, and propose a novel
model which can provide joint textual rationale generation and attention
visualization. Our datasets define visual and textual justifications of a
classification decision for activity recognition tasks (ACT-X) and for visual
question answering tasks (VQA-X). We quantitatively show that training with the
textual explanations not only yields better textual justification models, but
also better localizes the evidence that supports the decision. We also
qualitatively show cases where visual explanation is more insightful than
textual explanation, and vice versa, supporting our thesis that multimodal
explanation models offer significant benefits over unimodal approaches. | ['Marcus Rohrbach', 'Anna Rohrbach', 'Zeynep Akata', 'Trevor Darrell', 'Lisa Anne Hendricks', 'Dong Huk Park', 'Bernt Schiele'] | 2018-02-15 | multimodal-explanations-justifying-decisions-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Park_Multimodal_Explanations_Justifying_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Park_Multimodal_Explanations_Justifying_CVPR_2018_paper.pdf | cvpr-2018-6 | ['explainable-models'] | ['computer-vision'] | [ 2.88890451e-01 8.11598480e-01 -2.74765491e-01 -5.65972745e-01
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acf439f7-3295-4484-be57-a42961473025 | enhanced-temporal-knowledge-embeddings-with | 2203.09590 | null | https://arxiv.org/abs/2203.09590v5 | https://arxiv.org/pdf/2203.09590v5.pdf | ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations | Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts. However, existing enhancement approaches cannot apply to temporal knowledge graphs (tKGs), which contain time-dependent event knowledge with complex temporal dynamics. Specifically, existing enhancement approaches often assume knowledge embedding is time-independent. In contrast, the entity embedding in tKG models usually evolves, which poses the challenge of aligning temporally relevant texts with entities. To this end, we propose to study enhancing temporal knowledge embedding with textual data in this paper. As an approach to this task, we propose Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA), which takes the temporal aspect into account and injects textual information into temporal knowledge embedding. To evaluate ECOLA, we introduce three new datasets for training and evaluating ECOLA. Extensive experiments show that ECOLA significantly enhances temporal KG embedding models with up to 287% relative improvements regarding Hits@1 on the link prediction task. The code and models are publicly available on https://anonymous.4open.science/r/ECOLA. | ['Yujia Gu', 'Jindong Gu', 'Hinrich Schütze', 'Heinz Köppl', 'Volker Tresp', 'Zifeng Ding', 'Yao Zhang', 'Ruotong Liao', 'Zhen Han'] | 2022-03-17 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.40473020e-01 -2.03402653e-01 -5.07776558e-01 -4.57541980e-02
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394825ef-ceee-4e2d-82a2-46e15afe7e59 | quality-assessment-for-tone-mapped-hdr-images | 1810.08339 | null | https://arxiv.org/abs/1810.08339v2 | https://arxiv.org/pdf/1810.08339v2.pdf | Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information | Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tone-mapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance. | ['Ming Jiang', 'Tingting Jiang', 'Dingquan Li', 'Qin He'] | 2018-10-19 | null | null | null | null | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 5.72414815e-01 -6.47531271e-01 6.10089079e-02 -5.55117071e-01
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6f6dc8be-8655-4a67-88bb-d2b486f9e092 | pac-man-pete-an-extensible-framework-for | 2211.14385 | null | https://arxiv.org/abs/2211.14385v1 | https://arxiv.org/pdf/2211.14385v1.pdf | Pac-Man Pete: An extensible framework for building AI in VEX Robotics | This technical report details VEX Robotics team BLRSAI's development of a fully autonomous robot for VEX Robotics' Tipping Point AI Competition. We identify and develop three separate critical components. This includes a Unity simulation and reinforcement learning model training pipeline, a malleable computer vision pipeline, and a data transfer pipeline to offload large computations from the VEX V5 Brain/micro-controller to an external computer. We give the community access to all of these components in hopes they can reuse and improve upon them in the future, and that it'll spark new ideas for autonomy as well as the necessary infrastructure and programs for AI in educational robotics. | ['Sagar Patil', 'Will Xu', 'Manish Pylla', 'Aref Malek', 'Cole Roberts', 'Nicholas Wade', 'Jacob Zietek'] | 2022-11-25 | null | null | null | null | ['unity'] | ['computer-vision'] | [-4.54899430e-01 5.49286902e-01 -2.14473102e-02 -4.46292579e-01
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4d9b1e32-37b3-48e4-b0e0-1c71818a1d54 | modeling-temporal-concept-receptive-field | 2111.11653 | null | https://arxiv.org/abs/2111.11653v1 | https://arxiv.org/pdf/2111.11653v1.pdf | Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis | Event analysis in untrimmed videos has attracted increasing attention due to the application of cutting-edge techniques such as CNN. As a well studied property for CNN-based models, the receptive field is a measurement for measuring the spatial range covered by a single feature response, which is crucial in improving the image categorization accuracy. In video domain, video event semantics are actually described by complex interaction among different concepts, while their behaviors vary drastically from one video to another, leading to the difficulty in concept-based analytics for accurate event categorization. To model the concept behavior, we study temporal concept receptive field of concept-based event representation, which encodes the temporal occurrence pattern of different mid-level concepts. Accordingly, we introduce temporal dynamic convolution (TDC) to give stronger flexibility to concept-based event analytics. TDC can adjust the temporal concept receptive field size dynamically according to different inputs. Notably, a set of coefficients are learned to fuse the results of multiple convolutions with different kernel widths that provide various temporal concept receptive field sizes. Different coefficients can generate appropriate and accurate temporal concept receptive field size according to input videos and highlight crucial concepts. Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis. Experiment results on FCVID and ActivityNet show that TDCMN demonstrates adaptive event recognition ability conditioned on different inputs, and improve the event recognition performance of Concept-based methods by a large margin. Code is available at https://github.com/qzhb/TDCMN. | ['Qingming Huang', 'Weigang Zhang', 'Li Su', 'Chi Su', 'Shuhui Wang', 'Zhaobo Qi'] | 2021-11-23 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.02406107e-02 -7.50724554e-01 -5.83797246e-02 -3.90139043e-01
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440eeb66-c878-42ad-b5db-a21c752bb5da | pivoine-instruction-tuning-for-open-world | 2305.14898 | null | https://arxiv.org/abs/2305.14898v1 | https://arxiv.org/pdf/2305.14898v1.pdf | PIVOINE: Instruction Tuning for Open-world Information Extraction | We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional closed-world setting of Information Extraction (IE), Open-world IE considers a more general situation where entities and relations could be beyond a predefined ontology. More importantly, we seek to develop a large language model (LLM) that is able to perform Open-world IE to extract desirable entity profiles characterized by (possibly fine-grained) natural language instructions. We achieve this by finetuning LLMs using instruction tuning. In particular, we construct INSTRUCTOPENWIKI, a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions. We finetune the pretrained BLOOM models on INSTRUCTOPENWIKI and obtain PIVOINE, an LLM for Open-world IE with strong instruction-following capabilities. Our experiments demonstrate that PIVOINE significantly outperforms traditional closed-world methods and other LLM baselines, displaying impressive generalization capabilities on both unseen instructions and out-of-ontology cases. Consequently, PIVOINE emerges as a promising solution to tackle the open-world challenge in IE effectively. | ['Jianshu Chen', 'Dong Yu', 'Hongming Zhang', 'Kaiqiang Song', 'Xiaoman Pan', 'Keming Lu'] | 2023-05-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-1.02541838e-02 4.65714186e-01 -5.90229809e-01 -1.88969761e-01
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5d6a51a3-88c4-44dd-bc3e-7ed6b2887d45 | training-energy-based-models-with-diffusion | 2307.01668 | null | https://arxiv.org/abs/2307.01668v1 | https://arxiv.org/pdf/2307.01668v1.pdf | Training Energy-Based Models with Diffusion Contrastive Divergences | Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which leads to an irreconcilable trade-off between the computational burden and the validity of the CD. Running MCMCs till convergence is computationally intensive. On the other hand, short-run MCMC brings in an extra non-negligible parameter gradient term that is difficult to handle. In this paper, we provide a general interpretation of CD, viewing it as a special instance of our proposed Diffusion Contrastive Divergence (DCD) family. By replacing the Langevin dynamic used in CD with other EBM-parameter-free diffusion processes, we propose a more efficient divergence. We show that the proposed DCDs are both more computationally efficient than the CD and are not limited to a non-negligible gradient term. We conduct intensive experiments, including both synthesis data modeling and high-dimensional image denoising and generation, to show the advantages of the proposed DCDs. On the synthetic data learning and image denoising experiments, our proposed DCD outperforms CD by a large margin. In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs. | ['Zhihua Zhang', 'Zhenguo Li', 'Jiacheng Sun', 'Tianyang Hu', 'Hao Jiang', 'Weijian Luo'] | 2023-07-04 | null | null | null | null | ['image-denoising', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 2.03000903e-01 -3.69582117e-01 4.14193094e-01 -7.76943266e-02
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5a38aab3-3df4-4854-845b-c5614fe70b5a | an-experimental-study-on-pretraining | 2301.10444 | null | https://arxiv.org/abs/2301.10444v1 | https://arxiv.org/pdf/2301.10444v1.pdf | An Experimental Study on Pretraining Transformers from Scratch for IR | Finetuning Pretrained Language Models (PLM) for IR has been de facto the standard practice since their breakthrough effectiveness few years ago. But, is this approach well understood? In this paper, we study the impact of the pretraining collection on the final IR effectiveness. In particular, we challenge the current hypothesis that PLM shall be trained on a large enough generic collection and we show that pretraining from scratch on the collection of interest is surprisingly competitive with the current approach. We benchmark first-stage ranking rankers and cross-encoders for reranking on the task of general passage retrieval on MSMARCO, Mr-Tydi for Arabic, Japanese and Russian, and TripClick for specific domain. Contrary to popular belief, we show that, for finetuning first-stage rankers, models pretrained solely on their collection have equivalent or better effectiveness compared to more general models. However, there is a slight effectiveness drop for rerankers pretrained only on the target collection. Overall, our study sheds a new light on the role of the pretraining collection and should make our community ponder on building specialized models by pretraining from scratch. Last but not least, doing so could enable better control of efficiency, data bias and replicability, which are key research questions for the IR community. | ['Stéphane Clinchant', 'Hervé Déjean', 'Carlos Lassance'] | 2023-01-25 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.33647233e-01 6.51137382e-02 -4.42543834e-01 -2.75964051e-01
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0124740a-15e9-4cee-a201-f64ca3086b48 | identity-encoder-for-personalized-diffusion | 2304.07429 | null | https://arxiv.org/abs/2304.07429v1 | https://arxiv.org/pdf/2304.07429v1.pdf | Identity Encoder for Personalized Diffusion | Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization by fine-tuning one model for each person. While being successful, this approach incurs additional computation and storage overhead for each new identity. Furthermore, it usually expects tens or hundreds of examples per identity to achieve the best performance. To overcome these challenges, we propose an encoder-based approach for personalization. We learn an identity encoder which can extract an identity representation from a set of reference images of a subject, together with a diffusion generator that can generate new images of the subject conditioned on the identity representation. Once being trained, the model can be used to generate images of arbitrary identities given a few examples even if the model hasn't been trained on the identity. Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications. Empirical results show that our approach consistently outperforms existing fine-tuning based approach in both image generation and reconstruction, and the outputs is preferred by users more than 95% of the time compared with the best performing baseline. | ['Xuhui Jia', 'Huisheng Wang', 'Boqing Gong', 'Han Zhang', 'Yang Zhao', 'Yandong Li', 'Kelvin C. K. Chan', 'Yu-Chuan Su'] | 2023-04-14 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 3.75658810e-01 1.92765117e-01 1.95599627e-02 -3.70463401e-01
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7df20c39-74ce-4690-8542-5eb1c84504bb | complex-qa-and-language-models-hybrid | 2302.09051 | null | https://arxiv.org/abs/2302.09051v4 | https://arxiv.org/pdf/2302.09051v4.pdf | Complex QA and language models hybrid architectures, Survey | This paper reviews the state-of-the-art of language models architectures and strategies for "complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large Language Models (LLM) are good at leveraging public data on standard problems but once you want to tackle more specific complex questions or problems (e.g. How does the concept of personal freedom vary between different cultures ? What is the best mix of power generation methods to reduce climate change ?) you may need specific architecture, knowledge, skills, methods, sensitive data protection, explainability, human approval and versatile feedback... Recent projects like ChatGPT and GALACTICA have allowed non-specialists to grasp the great potential as well as the equally strong limitations of LLM in complex QA. In this paper, we start by reviewing required skills and evaluation techniques. We integrate findings from the robust community edited research papers BIG, BLOOM and HELM which open source, benchmark and analyze limits and challenges of LLM in terms of tasks complexity and strict evaluation on accuracy (e.g. fairness, robustness, toxicity, ...) as a baseline. We discuss some challenges associated with complex QA, including domain adaptation, decomposition and efficient multi-step QA, long form and non-factoid QA, safety and multi-sensitivity data protection, multimodal search, hallucinations, explainability and truthfulness, temporal reasoning. We analyze current solutions and promising research trends, using elements such as: hybrid LLM architectural patterns, training and prompting strategies, active human reinforcement learning supervised with AI, neuro-symbolic and structured knowledge grounding, program synthesis, iterated decomposition and others. | ['Elisabeth Murisasco', 'Vincent Martin', 'Emmanuel Bruno', 'Patrice Bellot', 'Xavier Daull'] | 2023-02-17 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.93888143e-01 4.35629308e-01 9.96500179e-02 -3.08986932e-01
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71d00fee-35ff-4a87-94f5-bc6959bf51e3 | emg-based-feature-extraction-and | 2107.00733 | null | https://arxiv.org/abs/2107.00733v1 | https://arxiv.org/pdf/2107.00733v1.pdf | EMG-Based Feature Extraction and Classification for Prosthetic Hand Control | In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing approach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to $95.5\%$ for $800$ msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods. | ['Mehrdad Nourani', 'Mohammad Esmaeili', 'Reza Bagherian Azhiri'] | 2021-07-01 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 6.67899787e-01 -4.22215939e-01 4.79453057e-02 -2.12911800e-01
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afc543de-042a-48e7-b3e5-60252a4c47a6 | counterfactual-vqa-a-cause-effect-look-at | 2006.04315 | null | https://arxiv.org/abs/2006.04315v4 | https://arxiv.org/pdf/2006.04315v4.pdf | Counterfactual VQA: A Cause-Effect Look at Language Bias | VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data. The code is available at https://github.com/yuleiniu/cfvqa. | ['Ji-Rong Wen', 'Xian-Sheng Hua', 'Zhiwu Lu', 'Hanwang Zhang', 'Yulei Niu', 'Kaihua Tang'] | 2020-06-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.pdf | cvpr-2021-1 | ['counterfactual-inference'] | ['miscellaneous'] | [-1.11603409e-01 7.48607144e-02 -6.76518738e-01 -5.94932199e-01
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ed91a0ee-d41d-4178-abf8-b27bf1e5ad48 | compfeat-comprehensive-feature-aggregation | 2012.03400 | null | https://arxiv.org/abs/2012.03400v1 | https://arxiv.org/pdf/2012.03400v1.pdf | CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation | Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects and they suffer in the video scenario due to several distinct challenges such as motion blur and drastic appearance change. To eliminate ambiguities introduced by only using single-frame features, we propose a novel comprehensive feature aggregation approach (CompFeat) to refine features at both frame-level and object-level with temporal and spatial context information. The aggregation process is carefully designed with a new attention mechanism which significantly increases the discriminative power of the learned features. We further improve the tracking capability of our model through a siamese design by incorporating both feature similarities and spatial similarities. Experiments conducted on the YouTube-VIS dataset validate the effectiveness of proposed CompFeat. Our code will be available at https://github.com/SHI-Labs/CompFeat-for-Video-Instance-Segmentation. | ['Humphrey Shi', 'Thomas S. Huang', 'Ding Liu', 'Linjie Yang', 'Yang Fu'] | 2020-12-07 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-6.69058189e-02 -5.58679402e-01 -1.77082449e-01 -3.45141500e-01
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cf754898-55fd-4599-9a1c-2935f37bd083 | generalized-iris-presentation-attack | 2010.13244 | null | https://arxiv.org/abs/2010.13244v1 | https://arxiv.org/pdf/2010.13244v1.pdf | Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings | Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been used. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm. | ['Richa Singh', 'Mayank Vatsa', 'Akshay Agarwal', 'Vishal Singh', 'Mehak Gupta'] | 2020-10-25 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.84058711e-01 -4.80927914e-01 8.90163258e-02 -5.08287475e-02
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789de4fa-ccf3-4097-ba17-d4e93153db80 | thu_ngn-at-semeval-2018-task-1-fine-grained | null | null | https://aclanthology.org/S18-1028 | https://aclanthology.org/S18-1028.pdf | THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM | Traditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can{'}t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is aimed to automatically determine the intensity of emotions or sentiment of tweets to mine fine-grained sentiment information. In order to address this task, we propose a system based on an attention CNN-LSTM model. In our model, LSTM is used to extract the long-term contextual information from texts. We apply attention techniques to selecting this information. A CNN layer with different size of kernels is used to extract local features. The dense layers take the pooled CNN feature maps and predict the intensity scores. Our system reaches average Pearson correlation score of 0.722 (ranked 12/48) in emotion intensity regression task, and 0.810 in valence regression task (ranked 15/38). It indicates that our system can be further extended. | ['Yongfeng Huang', 'Junxin Liu', 'Zhigang Yuan', 'Fangzhao Wu', 'Sixing Wu', 'Chuhan Wu'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-9.71135199e-02 -5.68086132e-02 -3.62078846e-01 -8.65822792e-01
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6d9682ae-48dd-4c90-a58a-622550d820b2 | privacy-preserving-representation-learning | 2302.04383 | null | https://arxiv.org/abs/2302.04383v1 | https://arxiv.org/pdf/2302.04383v1.pdf | Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes | Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy concerns when nodes represent people or human-related variables. Moreover, standard NRLs that leverage structural information from a graph proceed by first encoding pairwise relationships into learned representations and then analysing its properties. This approach is fundamentally misaligned with problems where the relationships involve multiple points, and topological structure must be encoded beyond pairwise interactions. Fortunately, the machinery of topological data analysis (TDA) and, in particular, simplicial neural networks (SNNs) offer a mathematically rigorous framework to learn higher-order interactions between nodes. It is critical to investigate if the representation outputs from SNNs are more vulnerable compared to regular representation outputs from graph neural networks (GNNs) via pairwise interactions. In my dissertation, I will first study learning the representations with text attributes for simplicial complexes (RT4SC) via SNNs. Then, I will conduct research on two potential attacks on the representation outputs from SNNs: (1) membership inference attack, which infers whether a certain node of a graph is inside the training data of the GNN model; and (2) graph reconstruction attacks, which infer the confidential edges of a text-attributed network. Finally, I will study a privacy-preserving deterministic differentially private alternating direction method of multiplier to learn secure representation outputs from SNNs that capture multi-scale relationships and facilitate the passage from local structure to global invariant features on text-attributed networks. | ['Victor S. Sheng', 'Huixin Zhan'] | 2023-02-09 | null | null | null | null | ['inference-attack', 'membership-inference-attack', 'topological-data-analysis', 'graph-reconstruction', 'learning-network-representations'] | ['adversarial', 'computer-vision', 'graphs', 'graphs', 'methodology'] | [ 7.55951524e-01 6.28657699e-01 -3.25787306e-01 -2.58901715e-01
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eaff1117-798d-42d5-8af3-6b7d913e5ea1 | megan-memory-enhanced-graph-attention-network | 2110.15327 | null | https://arxiv.org/abs/2110.15327v2 | https://arxiv.org/pdf/2110.15327v2.pdf | MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution | Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspired by the recent success to consider spatial-temporal information for space-time super-resolution, our main goal in this work is to take full considerations of spatial and temporal correlations within the video sequences of fast dynamic events. To this end, we propose a novel one-stage memory enhanced graph attention network (MEGAN) for space-time video super-resolution. Specifically, we build a novel long-range memory graph aggregation (LMGA) module to dynamically capture correlations along the channel dimensions of the feature maps and adaptively aggregate channel features to enhance the feature representations. We introduce a non-local residual block, which enables each channel-wise feature to attend global spatial hierarchical features. In addition, we adopt a progressive fusion module to further enhance the representation ability by extensively exploiting spatial-temporal correlations from multiple frames. Experiment results demonstrate that our method achieves better results compared with the state-of-the-art methods quantitatively and visually. | ['Wei Fan', 'Hui Tang', 'Ruihan Zhao', 'Aosong Feng', 'Lianyi Han', 'Chenyu You'] | 2021-10-28 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.52390528e-01 -4.17023659e-01 3.21853757e-02 -2.07207263e-01
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1f659cf9-2e52-49ed-a28a-ee27a5cb67bb | domain-adaptive-full-face-gaze-estimation-via | 2305.16140 | null | https://arxiv.org/abs/2305.16140v1 | https://arxiv.org/pdf/2305.16140v1.pdf | Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement | Along with the recent development of deep neural networks, appearance-based gaze estimation has succeeded considerably when training and testing within the same domain. Compared to the within-domain task, the variance of different domains makes the cross-domain performance drop severely, preventing gaze estimation deployment in real-world applications. Among all the factors, ranges of head pose and gaze are believed to play a significant role in the final performance of gaze estimation, while collecting large ranges of data is expensive. This work proposes an effective model training pipeline consisting of a training data synthesis and a gaze estimation model for unsupervised domain adaptation. The proposed data synthesis leverages the single-image 3D reconstruction to expand the range of the head poses from the source domain without requiring a 3D facial shape dataset. To bridge the inevitable gap between synthetic and real images, we further propose an unsupervised domain adaptation method suitable for synthetic full-face data. We propose a disentangling autoencoder network to separate gaze-related features and introduce background augmentation consistency loss to utilize the characteristics of the synthetic source domain. Through comprehensive experiments, we show that the model only using monocular-reconstructed synthetic training data can perform comparably to real data with a large label range. Our proposed domain adaptation approach further improves the performance on multiple target domains. The code and data will be available at \url{https://github.com/ut-vision/AdaptiveGaze}. | ['Yusuke Sugano', 'Xucong Zhang', 'Takuru Shimoyama', 'Jiawei Qin'] | 2023-05-25 | null | null | null | null | ['gaze-estimation', '3d-reconstruction', 'novel-view-synthesis', 'unsupervised-domain-adaptation', 'disentanglement'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 1.88317105e-01 4.59988527e-02 -7.10405931e-02 -6.59120142e-01
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e9480f33-6b76-4500-b1ef-a429240652e3 | generalized-active-learning-and-design-of | 1904.03909 | null | http://arxiv.org/abs/1904.03909v1 | http://arxiv.org/pdf/1904.03909v1.pdf | Generalized active learning and design of statistical experiments for manifold-valued data | Characterizing the appearance of real-world surfaces is a fundamental problem
in multidimensional reflectometry, computer vision and computer graphics. For
many applications, appearance is sufficiently well characterized by the
bidirectional reflectance distribution function (BRDF). We treat BRDF
measurements as samples of points from high-dimensional non-linear non-convex
manifolds. BRDF manifolds form an infinite-dimensional space, but typically the
available measurements are very scarce for complicated problems such as BRDF
estimation. Therefore, an efficient learning strategy is crucial when
performing the measurements.
In this paper, we build the foundation of a mathematical framework that
allows to develop and apply new techniques within statistical design of
experiments and generalized proactive learning, in order to establish more
efficient sampling and measurement strategies for BRDF data manifolds. | ['Mikhail A. Langovoy'] | 2019-04-08 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 1.75356403e-01 -3.33212793e-01 -1.94840096e-02 -3.49913567e-01
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3a264df7-61ea-40ca-9256-b93f343fc941 | convergence-rates-of-kernel-conjugate | 1607.02387 | null | http://arxiv.org/abs/1607.02387v1 | http://arxiv.org/pdf/1607.02387v1.pdf | Convergence rates of Kernel Conjugate Gradient for random design regression | We prove statistical rates of convergence for kernel-based least squares
regression from i.i.d. data using a conjugate gradient algorithm, where
regularization against overfitting is obtained by early stopping. This method
is related to Kernel Partial Least Squares, a regression method that combines
supervised dimensionality reduction with least squares projection. Following
the setting introduced in earlier related literature, we study so-called "fast
convergence rates" depending on the regularity of the target regression
function (measured by a source condition in terms of the kernel integral
operator) and on the effective dimensionality of the data mapped into the
kernel space. We obtain upper bounds, essentially matching known minimax lower
bounds, for the $\mathcal{L}^2$ (prediction) norm as well as for the stronger
Hilbert norm, if the true regression function belongs to the reproducing kernel
Hilbert space. If the latter assumption is not fulfilled, we obtain similar
convergence rates for appropriate norms, provided additional unlabeled data are
available. | ['Nicole Krämer', 'Gilles Blanchard'] | 2016-07-08 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.01791987e-01 3.28754574e-01 -1.86010540e-01 -2.58240819e-01
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522d285a-2d2c-4b50-9677-3c64f7f3cbda | multimodal-recurrent-neural-networks-with | 1803.04687 | null | http://arxiv.org/abs/1803.04687v1 | http://arxiv.org/pdf/1803.04687v1.pdf | Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling | This paper proposes a new method called Multimodal RNNs for RGB-D scene
semantic segmentation. It is optimized to classify image pixels given two input
sources: RGB color channels and Depth maps. It simultaneously performs training
of two recurrent neural networks (RNNs) that are crossly connected through
information transfer layers, which are learnt to adaptively extract relevant
cross-modality features. Each RNN model learns its representations from its own
previous hidden states and transferred patterns from the other RNNs previous
hidden states; thus, both model-specific and crossmodality features are
retained. We exploit the structure of quad-directional 2D-RNNs to model the
short and long range contextual information in the 2D input image. We carefully
designed various baselines to efficiently examine our proposed model structure.
We test our Multimodal RNNs method on popular RGB-D benchmarks and show how it
outperforms previous methods significantly and achieves competitive results
with other state-of-the-art works. | ['Lap-Pui Chau', 'Abrar H. Abdulnabi', 'Gang Wang', 'Bing Shuai', 'Zhen Zuo'] | 2018-03-13 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.43821728e-01 2.13394046e-01 -5.60257971e-01 -7.51225770e-01
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4f333b0c-74ab-4e8e-a4f3-0672d5d97223 | face-transfer-with-generative-adversarial | 1710.06090 | null | http://arxiv.org/abs/1710.06090v1 | http://arxiv.org/pdf/1710.06090v1.pdf | Face Transfer with Generative Adversarial Network | Face transfer animates the facial performances of the character in the target
video by a source actor. Traditional methods are typically based on face
modeling. We propose an end-to-end face transfer method based on Generative
Adversarial Network. Specifically, we leverage CycleGAN to generate the face
image of the target character with the corresponding head pose and facial
expression of the source. In order to improve the quality of generated videos,
we adopt PatchGAN and explore the effect of different receptive field sizes on
generated images. | ['Wei-Nan Zhang', 'Zhiming Zhou', 'Runze Xu', 'Yong Yu'] | 2017-10-17 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [ 7.75992051e-02 4.12289053e-01 3.56218368e-01 -2.84290791e-01
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bdb2bf43-6495-466e-906b-d4e9a4c478a3 | learn-to-not-link-exploring-nil-prediction-in | 2305.15725 | null | https://arxiv.org/abs/2305.15725v1 | https://arxiv.org/pdf/2305.15725v1.pdf | Learn to Not Link: Exploring NIL Prediction in Entity Linking | Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to identify mentions without a corresponding entity in the knowledge base, has received insufficient attention. We categorize mentions linking to NIL into Missing Entity and Non-Entity Phrase, and propose an entity linking dataset NEL that focuses on the NIL prediction problem. NEL takes ambiguous entities as seeds, collects relevant mention context in the Wikipedia corpus, and ensures the presence of mentions linking to NIL by human annotation and entity masking. We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction. Our code and dataset can be accessed at https://github.com/solitaryzero/NIL_EL | ['Zhifang Sui', 'Lei Hou', 'Juanzi Li', 'Hailong Jin', 'Jifan Yu', 'Fangwei Zhu'] | 2023-05-25 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-4.88012999e-01 3.35600048e-01 -5.75962722e-01 -4.17621315e-01
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c523a067-3d7b-48ca-9ef9-eacac4289485 | single-image-reflection-removal-using-deep | 1802.00094 | null | http://arxiv.org/abs/1802.00094v1 | http://arxiv.org/pdf/1802.00094v1.pdf | Single Image Reflection Removal Using Deep Encoder-Decoder Network | Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer vision. In this paper, we propose a novel deep convolutional
encoder-decoder method to remove the objectionable reflection by learning a map
between image pairs with and without reflection. For training the neural
network, we model the physical formation of reflections in images and
synthesize a large number of photo-realistic reflection-tainted images from
reflection-free images collected online. Extensive experimental results show
that, although the neural network learns only from synthetic data, the proposed
method is effective on real-world images, and it significantly outperforms the
other tested state-of-the-art techniques. | ['Xiaolin Wu', 'Xiao Shu', 'Jinjin Gu', 'Zhixiang Chi'] | 2018-01-31 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 1.03735971e+00 1.64177567e-01 8.15041780e-01 -3.69555593e-01
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a90bdb6b-38ed-456b-b065-4fb397e9a280 | deer-detection-agnostic-end-to-end-recognizer | 2203.05122 | null | https://arxiv.org/abs/2203.05122v1 | https://arxiv.org/pdf/2203.05122v1.pdf | DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting | Recent end-to-end scene text spotters have achieved great improvement in recognizing arbitrary-shaped text instances. Common approaches for text spotting use region of interest pooling or segmentation masks to restrict features to single text instances. However, this makes it hard for the recognizer to decode correct sequences when the detection is not accurate i.e. one or more characters are cropped out. Considering that it is hard to accurately decide word boundaries with only the detector, we propose a novel Detection-agnostic End-to-End Recognizer, DEER, framework. The proposed method reduces the tight dependency between detection and recognition modules by bridging them with a single reference point for each text instance, instead of using detected regions. The proposed method allows the decoder to recognize the texts that are indicated by the reference point, with features from the whole image. Since only a single point is required to recognize the text, the proposed method enables text spotting without an arbitrarily-shaped detector or bounding polygon annotations. Experimental results present that the proposed method achieves competitive results on regular and arbitrarily-shaped text spotting benchmarks. Further analysis shows that DEER is robust to the detection errors. The code and dataset will be publicly available. | ['Youngmin Baek', 'Bado Lee', 'Seunghyun Park', 'Jaeheung Surh', 'Taeho Kil', 'Han-Cheol Cho', 'Yoonsik Kim', 'Seung Shin', 'Seonghyeon Kim'] | 2022-03-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 7.29881704e-01 -2.29273707e-01 4.16286178e-02 -2.76585251e-01
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b7b4808a-f9a4-4a52-90b1-90978690d42a | diffmic-dual-guidance-diffusion-network-for | 2303.10610 | null | https://arxiv.org/abs/2303.10610v3 | https://arxiv.org/pdf/2303.10610v3.pdf | DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification | Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation. To achieve this goal, we devise a dual conditional guidance strategy that conditions each diffusion step with multiple granularities to improve step-wise regional attention. Furthermore, we propose learning the mutual information in each granularity by enforcing Maximum-Mean Discrepancy regularization during the diffusion forward process. We evaluate the effectiveness of our DiffMIC on three medical classification tasks with different image modalities, including placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin, indicating the universality and effectiveness of the proposed model. Our code will be publicly available at https://github.com/scott-yjyang/DiffMIC. | ['Angelica I. Aviles-Rivero', 'Lei Zhu', 'Carola-Bibiane Schönlieb', 'Huazhu Fu', 'Yijun Yang'] | 2023-03-19 | null | null | null | null | ['skin-lesion-classification', 'diabetic-retinopathy-grading'] | ['medical', 'medical'] | [ 2.89087147e-01 2.81762898e-01 -2.03059644e-01 -5.59684396e-01
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b01198d7-52e1-4733-a433-40175f31c41b | robust-registration-of-multimodal-remote | 2103.16871 | null | https://arxiv.org/abs/2103.16871v1 | https://arxiv.org/pdf/2103.16871v1.pdf | Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity | Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation, and use the extended model to build HOPCncc. Then a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images, and has been tested with a variety of optical, LiDAR, SAR and map data. The results show that HOPCncc is robust against complex non-linear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration. | ['Li Shen', 'Lorenzo Bruzzone', 'Jie Shan', 'Yuanxin Ye'] | 2021-03-31 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 5.07041395e-01 -7.04889596e-01 1.34481028e-01 -2.72859484e-01
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aae3b30c-c3e3-4cf0-af36-316872b4e4a3 | scenereplica-benchmarking-real-world-robot | 2306.15620 | null | https://arxiv.org/abs/2306.15620v1 | https://arxiv.org/pdf/2306.15620v1.pdf | SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Reproducible Scenes | We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods. | ['Yu Xiang', 'Balakrishnan Prabhakaran', 'Jishnu Jaykumar P', 'Yangxiao Lu', 'Sai Haneesh Allu', 'Ninad Khargonkar'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking', 'robotic-grasping', 'robot-manipulation', 'motion-planning'] | ['miscellaneous', 'robots', 'robots', 'robots', 'robots'] | [-1.14261888e-01 -5.50310671e-01 -3.45845759e-01 -2.85953671e-01
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7e2a2295-0928-41e3-aa13-2a719ed02134 | read-look-and-detect-bounding-box-annotation | 2306.06149 | null | https://arxiv.org/abs/2306.06149v1 | https://arxiv.org/pdf/2306.06149v1.pdf | Read, look and detect: Bounding box annotation from image-caption pairs | Various methods have been proposed to detect objects while reducing the cost of data annotation. For instance, weakly supervised object detection (WSOD) methods rely only on image-level annotations during training. Unfortunately, data annotation remains expensive since annotators must provide the categories describing the content of each image and labeling is restricted to a fixed set of categories. In this paper, we propose a method to locate and label objects in an image by using a form of weaker supervision: image-caption pairs. By leveraging recent advances in vision-language (VL) models and self-supervised vision transformers (ViTs), our method is able to perform phrase grounding and object detection in a weakly supervised manner. Our experiments demonstrate the effectiveness of our approach by achieving a 47.51% recall@1 score in phrase grounding on Flickr30k Entities and establishing a new state-of-the-art in object detection by achieving 21.1 mAP 50 and 10.5 mAP 50:95 on MS COCO when exclusively relying on image-caption pairs. | ['Eduardo Hugo Sanchez'] | 2023-06-09 | null | null | null | null | ['weakly-supervised-object-detection', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 2.13411868e-01 3.25181872e-01 -2.89937109e-01 -3.02077532e-01
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8277d371-a575-4168-8480-46e09e3cefcd | cfad-coarse-to-fine-action-detector-for | 2008.08332 | null | https://arxiv.org/abs/2008.08332v1 | https://arxiv.org/pdf/2008.08332v1.pdf | CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization | Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD),an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes' location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, theproposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3x faster than the nearest competitors. | ['Shugong Xu', 'Ke Yan', 'John See', 'Yuxi Li', 'Weiyao Lin', 'Ning Xu', 'Cong Yang'] | 2020-08-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2588_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610494.pdf | eccv-2020-8 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 1.48605630e-01 -3.75635743e-01 -5.70546567e-01 -2.22029120e-01
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a870281a-f68f-46b4-b88d-4b501f9312c7 | variational-imbalanced-regression | 2306.06599 | null | https://arxiv.org/abs/2306.06599v1 | https://arxiv.org/pdf/2306.06599v1.pdf | Variational Imbalanced Regression | Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's variational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation. Experiments in several real-world datasets show that our VIR can outperform state-of-the-art imbalanced regression models in terms of both accuracy and uncertainty estimation. | ['Hao Wang', 'Ziyan Wang'] | 2023-06-11 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-4.53531116e-01 1.90101430e-01 -5.71434081e-01 -7.96674192e-01
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49b80201-0398-400a-ba3f-ea871382c2ee | error-detection-for-text-to-sql-semantic | 2305.13683 | null | https://arxiv.org/abs/2305.13683v1 | https://arxiv.org/pdf/2305.13683v1.pdf | Error Detection for Text-to-SQL Semantic Parsing | Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. At the same time, modern deep learning based text-to-SQL parsers are often over-confident and thus casting doubt on their trustworthiness when deployed for real use. To that end, we propose to build a parser-independent error detection model for text-to-SQL semantic parsing. The proposed model is based on pre-trained language model of code and is enhanced with structural features learned by graph neural networks. We train our model on realistic parsing errors collected from a cross-domain setting. Experiments with three strong text-to-SQL parsers featuring different decoding mechanisms show that our approach outperforms parser-dependent uncertainty metrics and could effectively improve the performance and usability of text-to-SQL semantic parsers regardless of their architectures. | ['Yu Su', 'Huan Sun', 'Ziru Chen', 'Shijie Chen'] | 2023-05-23 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-9.88275334e-02 5.61744273e-01 -9.04778281e-05 -9.95128036e-01
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cac3405d-2962-4b43-bcfd-82f14b8c2a00 | spatio-temporal-pixel-level-contrastive | 2303.14361 | null | https://arxiv.org/abs/2303.14361v1 | https://arxiv.org/pdf/2303.14361v1.pdf | Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation | Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data. However, the access to source data is often restricted or infeasible in real-world scenarios. Under the source data restrictive circumstances, UDA is less practical. To address this, recent works have explored solutions under the Source-Free Domain Adaptation (SFDA) setup, which aims to adapt a source-trained model to the target domain without accessing source data. Still, existing SFDA approaches use only image-level information for adaptation, making them sub-optimal in video applications. This paper studies SFDA for Video Semantic Segmentation (VSS), where temporal information is leveraged to address video adaptation. Specifically, we propose Spatio-Temporal Pixel-Level (STPL) contrastive learning, a novel method that takes full advantage of spatio-temporal information to tackle the absence of source data better. STPL explicitly learns semantic correlations among pixels in the spatio-temporal space, providing strong self-supervision for adaptation to the unlabeled target domain. Extensive experiments show that STPL achieves state-of-the-art performance on VSS benchmarks compared to current UDA and SFDA approaches. Code is available at: https://github.com/shaoyuanlo/STPL | ['Vishal M. Patel', 'Alejandro Galindo', 'Sumanth Chennupati', 'Poojan Oza', 'Shao-Yuan Lo'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-semantic-segmentation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.56981069e-01 -1.45688862e-01 -6.07783198e-01 -4.16001618e-01
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06f310c0-67ec-4d00-90cb-ba5486b1c80a | efficient-lifting-of-symmetry-breaking | 2205.07129 | null | https://arxiv.org/abs/2205.07129v1 | https://arxiv.org/pdf/2205.07129v1.pdf | Efficient lifting of symmetry breaking constraints for complex combinatorial problems | Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily-solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned. | ['Konstantin Schekotihin', 'Mark Law', 'Martin Gebser', 'Alice Tarzariol'] | 2022-05-14 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 3.43104631e-01 3.85141641e-01 -6.81520760e-01 -2.83363700e-01
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0f8ba65e-57ae-40f5-90de-b0aca25efa24 | histopathology-whole-slide-image-analysis-1 | 2307.04189 | null | https://arxiv.org/abs/2307.04189v1 | https://arxiv.org/pdf/2307.04189v1.pdf | Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning | Graph-based methods have been extensively applied to whole-slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different entities. However, most of the existing methods focus on modeling WSIs with homogeneous graphs (e.g., with homogeneous node type). Despite their successes, these works are incapable of mining the complex structural relations between biological entities (e.g., the diverse interaction among different cell types) in the WSI. We propose a novel heterogeneous graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic similarity attribute to each edge. We then present a new heterogeneous-graph edge attribute transformer (HEAT) to take advantage of the edge and node heterogeneity during massage aggregating. Further, we design a new pseudo-label-based semantic-consistent pooling mechanism to obtain graph-level features, which can mitigate the over-parameterization issue of conventional cluster-based pooling. Additionally, observing the limitations of existing association-based localization methods, we propose a causal-driven approach attributing the contribution of each node to improve the interpretability of our framework. Extensive experiments on three public TCGA benchmark datasets demonstrate that our framework outperforms the state-of-the-art methods with considerable margins on various tasks. Our codes are available at https://github.com/HKU-MedAI/WSI-HGNN. | ['Lequan Yu', 'Guosheng Yin', 'Lan Ma', 'Fernando Julio Cendra', 'Tsai Hor Chan'] | 2023-07-09 | histopathology-whole-slide-image-analysis | http://openaccess.thecvf.com//content/CVPR2023/html/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['representation-learning', 'graph-representation-learning', 'pseudo-label', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 9.64757949e-02 6.30855709e-02 -5.78465402e-01 -2.13634372e-01
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95f715c9-16ba-48b7-96db-d3738ed1e987 | investigating-math-word-problems-using-1 | null | null | https://openreview.net/forum?id=jMI7ZlAC_J | https://openreview.net/pdf?id=jMI7ZlAC_J | Investigating Math Word Problems using Pretrained Multilingual Language Models | In this paper, we revisit math word problems~(MWPs) from the {\em cross-lingual} and {\em multilingual} perspective.We construct our MWP solvers over pretrained multilingual language models using the sequence-to-sequence model with copy mechanism.We compare how the MWP solvers perform in cross-lingual and multilingual scenarios.To facilitate the comparison of cross-lingual performance, we first adapt the large-scale English dataset MathQA as a counterpart of the Chinese dataset Math23K.Then we extend several English datasets to bilingual datasets through machine translation plus human annotation.Our experiments show that the MWP solvers may not be transferred to a different language even if the target expressions share the same numerical constants and operator set.However, it can be better generalized if problem types exist on both source language and target language. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.84896576e-01 -2.32397437e-01 -1.82836801e-01 -3.97413641e-01
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571bee40-e084-4d37-a9d6-353c666b8687 | on-regularization-parameter-estimation-under | 1608.00250 | null | http://arxiv.org/abs/1608.00250v1 | http://arxiv.org/pdf/1608.00250v1.pdf | On Regularization Parameter Estimation under Covariate Shift | This paper identifies a problem with the usual procedure for
L2-regularization parameter estimation in a domain adaptation setting. In such
a setting, there are differences between the distributions generating the
training data (source domain) and the test data (target domain). The usual
cross-validation procedure requires validation data, which can not be obtained
from the unlabeled target data. The problem is that if one decides to use
source validation data, the regularization parameter is underestimated. One
possible solution is to scale the source validation data through importance
weighting, but we show that this correction is not sufficient. We conclude the
paper with an empirical analysis of the effect of several importance weight
estimators on the estimation of the regularization parameter. | ['Marco Loog', 'Wouter M. Kouw'] | 2016-07-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.05868125e-01 2.65579700e-01 -3.33948851e-01 -5.19927740e-01
-1.01389909e+00 -5.50296485e-01 3.54784876e-01 7.82651454e-02
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e580bd8e-45c2-4a2c-8850-e593c4546c76 | detecting-adversarial-samples-using-density | 1705.02224 | null | http://arxiv.org/abs/1705.02224v4 | http://arxiv.org/pdf/1705.02224v4.pdf | Detecting Adversarial Samples Using Density Ratio Estimates | Machine learning models, especially based on deep architectures are used in
everyday applications ranging from self driving cars to medical diagnostics. It
has been shown that such models are dangerously susceptible to adversarial
samples, indistinguishable from real samples to human eye, adversarial samples
lead to incorrect classifications with high confidence. Impact of adversarial
samples is far-reaching and their efficient detection remains an open problem.
We propose to use direct density ratio estimation as an efficient model
agnostic measure to detect adversarial samples. Our proposed method works
equally well with single and multi-channel samples, and with different
adversarial sample generation methods. We also propose a method to use density
ratio estimates for generating adversarial samples with an added constraint of
preserving density ratio. | ['Lovedeep Gondara'] | 2017-05-05 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.54371911e-01 4.49236453e-01 1.02340944e-01 -6.69440255e-02
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153b6f4a-5101-4137-9276-2c69541191a5 | a-flexible-convolutional-solver-for-fast | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Puy_A_Flexible_Convolutional_Solver_for_Fast_Style_Transfers_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Puy_A_Flexible_Convolutional_Solver_for_Fast_Style_Transfers_CVPR_2019_paper.pdf | A Flexible Convolutional Solver for Fast Style Transfers | We propose a new flexible deep convolutional neural network (convnet) to perform fast neural style transfers. Our network is trained to solve approximately, but rapidly, the artistic style transfer problem of [Gatys et al.] for arbritary styles. While solutions already exist, our network is uniquely flexible by design: it can be manipulated at runtime to enforce new constraints on the final output. As examples, we show that it can be modified to perform tasks such as fast photorealistic style transfer, or fast video style transfer with short term consistency, with no retraining. This flexibility stems from the proposed architecture which is obtained by unrolling the gradient descent algorithm used in [Gatys et al.]. Regularisations added to [Gatys et al.] to solve a new task can be reported on-the-fly in our network, even after training.
| [' Patrick Perez', 'Gilles Puy'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['video-style-transfer'] | ['computer-vision'] | [ 4.31341588e-01 3.13670754e-01 3.28483880e-01 -3.37929040e-01
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87e4f0a5-d9ab-4b99-9449-83c210785122 | learning-multi-view-camera-relocalization | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.pdf | Learning Multi-View Camera Relocalization With Graph Neural Networks | We propose to construct a view graph to excavate the information of the whole given sequence for absolute camera pose estimation. Specifically, we harness GNNs to model the graph, allowing even non-consecutive frames to exchange information with each other. Rather than adopting the regular GNNs directly, we redefine the nodes, edges, and embedded functions to fit the relocalization task. Redesigned GNNs cooperate with CNNs in guiding knowledge propagation and feature extraction respectively to process multi-view high-dimension image features iteratively at different levels. Besides, a general graph-based loss function beyond constraints between consecutive views is employed for training the network in an end-to-end fashion. Extensive experiments conducted on both indoor and outdoor datasets demonstrate that our method outperforms previous approaches especially in large-scale and challenging scenarios.
| [' Junqiu Wang', ' Shaojun Cai', ' Xin Wu', 'Fei Xue'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['camera-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [-2.11808801e-01 2.31332064e-01 1.17303044e-01 -3.28306943e-01
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babe1ae2-b61d-4738-9c57-8343099940af | local-causal-discovery-for-estimating-causal | 2302.08070 | null | https://arxiv.org/abs/2302.08070v3 | https://arxiv.org/pdf/2302.08070v3.pdf | Local Causal Discovery for Estimating Causal Effects | Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can identify this class under strong faithfulness assumptions, it can be computationally prohibitive. Fortunately, only the local graph structure around the treatment is required to identify the set of possible ATE values, a fact exploited by local discovery algorithms to improve computational efficiency. In this paper, we introduce Local Discovery using Eager Collider Checks (LDECC), a new local causal discovery algorithm that leverages unshielded colliders to orient the treatment's parents differently from existing methods. We show that there exist graphs where LDECC exponentially outperforms existing local discovery algorithms and vice versa. Moreover, we show that LDECC and existing algorithms rely on different faithfulness assumptions, leveraging this insight to weaken the assumptions for identifying the set of possible ATE values. | ['Zachary C. Lipton', 'David Childers', 'Shantanu Gupta'] | 2023-02-16 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.09424010e-01 4.69446480e-01 -1.05487239e+00 1.15540472e-03
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8fd4ad94-9e14-4ba2-a3e6-c3aeddb4accf | multimodal-and-multilingual-embeddings-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/8466f9ace6a9acbe71f75762ffc890f1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8466f9ace6a9acbe71f75762ffc890f1-Paper.pdf | Multimodal and Multilingual Embeddings for Large-Scale Speech Mining | We present an approach to encode a speech signal into a fixed-size representation which minimizes the cosine loss with the existing massively multilingual LASER text embedding space. Sentences are close in this embedding space, independently of their language and modality, either text or audio. Using a similarity metric in that multimodal embedding space, we perform mining of audio in German, French, Spanish and English from Librivox against billions of sentences from Common Crawl. This yielded more than twenty thousand hours of aligned speech translations. To evaluate the automatically mined speech/text corpora, we train neural speech translation systems for several languages pairs. Adding the mined data, achieves significant improvements in the BLEU score on the CoVoST2 and the MUST-C test sets with respect to a very competitive baseline. Our approach can also be used to directly perform speech-to-speech mining, without the need to first transcribe or translate the data. We obtain more than one thousand three hundred hours of aligned speech in French, German, Spanish and English. This speech corpus has the potential to boost research in speech-to-speech translation which suffers from scarcity of natural end-to-end training data. All the mined multimodal corpora will be made freely available. | ['Holger Schwenk', 'Hongyu Gong', 'Paul-Ambroise Duquenne'] | 2021-12-01 | null | https://openreview.net/forum?id=6fmgB38rLI1 | https://openreview.net/pdf?id=6fmgB38rLI1 | neurips-2021-12 | ['speech-to-speech-translation'] | ['speech'] | [ 7.76506662e-02 2.44774237e-01 1.13748737e-01 -4.62346911e-01
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469b922a-190c-42ce-9807-4bda7a840771 | multimodal-knowledge-learning-for-named | null | null | https://openreview.net/forum?id=-0pzbYBTRmt | https://openreview.net/pdf?id=-0pzbYBTRmt | Multimodal Knowledge Learning for Named Entity Disambiguation | With the popularity of online social medias in recent years, massive-scale multimodal information has brought new challenges to traditional Named Entity Disambiguation (NED) tasks. Recently, Multimodal Named Entity Disambiguation (MNED) is proposed to link ambiguous mentions with the textual and visual contexts to a predefined knowledge graph. Recent attempts handle these issues mainly by annotating multimodal mentions and adding multimodal features to traditional NED models. These methods still suffer from 1) lack of multimodal annotation data against the huge scale of unlabeled corpus and 2) failing to model multimodal information at knowledge level. In this paper, we explore a pioneer study on leveraging multimodal knowledge learning to address the MNED task. Specifically, we propose a knowledge-guided transfer learning strategy to extract unified representation from different modalities and enrich multimodal lnowledge in a Meta Learning way which is much easier than collecting ambiguous mention corpus. Then we propose an Interactive Multimodal Learning Network (IMN), which is capable of fully utilizing the multimodal information in both mention and knowledge side. To verify the validity of the proposed method, we implemented comparisons on a public large-scale MNED dataset based on Twitter KB. Experimental results show that our method is superior to the state-of-the-art multimodal methods | ['Anonymous'] | 2021-08-17 | null | null | null | acl-arr-august-2021-8 | ['entity-disambiguation'] | ['natural-language-processing'] | [-2.03713581e-01 1.99087262e-01 -4.45971221e-01 -5.44973686e-02
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12802577-c12e-4175-8f30-57a9bf97d15f | large-language-models-can-be-used-to | 2305.06972 | null | https://arxiv.org/abs/2305.06972v2 | https://arxiv.org/pdf/2305.06972v2.pdf | Large Language Models Can Be Used To Effectively Scale Spear Phishing Campaigns | Recent progress in artificial intelligence (AI), particularly in the domain of large language models (LLMs), has resulted in powerful and versatile dual-use systems. Indeed, cognition can be put towards a wide variety of tasks, some of which can result in harm. This study investigates how LLMs can be used for spear phishing, a form of cybercrime that involves manipulating targets into divulging sensitive information. I first explore LLMs' ability to assist with the reconnaissance and message generation stages of a successful spear phishing attack, where I find that advanced LLMs are capable of improving cybercriminals' efficiency during these stages. To explore how LLMs can be used to scale spear phishing campaigns, I then create unique spear phishing messages for over 600 British Members of Parliament using OpenAI's GPT-3.5 and GPT-4 models. My findings reveal that these messages are not only realistic but also cost-effective, with each email costing only a fraction of a cent to generate. Next, I demonstrate how basic prompt engineering can circumvent safeguards installed in LLMs by the reinforcement learning from human feedback fine-tuning process, highlighting the need for more robust governance interventions aimed at preventing misuse. To address these evolving risks, I propose two potential solutions: structured access schemes, such as application programming interfaces, and LLM-based defensive systems. | ['Julian Hazell'] | 2023-05-11 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.61390334e-01 4.00399625e-01 -1.57406971e-01 9.46203619e-02
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6e427450-06d5-4759-bc71-3bb432183ea1 | multi-objective-distributed-optimization-for | 2202.09762 | null | https://arxiv.org/abs/2202.09762v1 | https://arxiv.org/pdf/2202.09762v1.pdf | Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids | The issue of voltage variations caused by integration of renewables has been addressed in this paper through distributed management of Microgrids (MGs). The distribution network (DN) takes the network losses and voltage quality as objectives, an alternating direction method of multipliers (ADMM) with adaptive penalty modulation has been proposed to realize distributed optimization with improved convergence. On the basis of satisfying the optimization target on the system level, the individual MG manages the controllable resources inside the MG to minimize the operating cost on the lower level. The feasibility and effectiveness of the proposed method has been demonstrated on a modified IEEE 33-bus system. | ['Lingxu Guo', 'Rujing Wang', 'Zuozheng Liu', 'Lemeng Liang', 'Tao Xu'] | 2022-02-20 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.71939087e-01 -1.15738250e-01 -2.92562425e-01 5.91241345e-02
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87c649ee-aaf0-40df-b550-280a820a25a5 | positive-unlabeled-learning-with-tensor | 2211.14085 | null | https://arxiv.org/abs/2211.14085v2 | https://arxiv.org/pdf/2211.14085v2.pdf | Positive unlabeled learning with tensor networks | Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. We apply the locally purified state tensor network to the positive unlabeled learning problem and test our model on the MNIST image and 15 categorical/mixed datasets. On the MNIST dataset, we obtain close to the state-of-the-art results even with very few labeled positive samples. We significantly improve the state-of-the-art on categorical datasets. Further, we show that the agreement fraction between outputs of different models on unlabeled samples is a good indicator of the model's performance. Finally, our method can generate new positive and negative instances, which we demonstrate on simple synthetic datasets. | ['Bojan Žunkovič'] | 2022-11-25 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.09063768e-01 1.06143236e-01 -9.08390999e-01 -7.71612942e-01
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b2f6ffc4-b47d-48b7-ba70-6ac02d6ecf2f | extract-denoise-and-enforce-evaluating-and | 2104.08724 | null | https://arxiv.org/abs/2104.08724v2 | https://arxiv.org/pdf/2104.08724v2.pdf | Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation | Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important concepts in the source input, as seq2seq learning does not have explicit focus on the concepts and commonly used evaluation metrics also treat concepts equally important as other tokens. In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial. We answer the above questions by conducting extensive analytical experiments on four representative text-to-text generation tasks. Based on the observations, we then propose a simple yet effective framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. This new method performs comparably or better than its unconstrained counterpart on automatic metrics, demonstrates higher coverage for concept preservation, and receives better ratings in the human evaluation. Our code is available at https://github.com/morningmoni/EDE. | ['Xiang Ren', 'Jiawei Han', 'Deren Lei', 'Wenchang Ma', 'Yuning Mao'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.413 | https://aclanthology.org/2021.emnlp-main.413.pdf | emnlp-2021-11 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.36824411e-01 3.45125705e-01 -2.32055232e-01 -2.32135445e-01
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36c7cf86-827a-4e1b-943c-73df08b4f600 | annotation-and-analysis-of-extractive | null | null | https://aclanthology.org/L18-1508 | https://aclanthology.org/L18-1508.pdf | Annotation and Analysis of Extractive Summaries for the Kyutech Corpus | null | ['Takashi Yamamura', 'Kazutaka Shimada'] | 2018-05-01 | annotation-and-analysis-of-extractive-1 | https://aclanthology.org/L18-1508 | https://aclanthology.org/L18-1508.pdf | lrec-2018-5 | ['meeting-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7818057d-1fc6-444c-8eee-bc68d85ea964 | neural-message-passing-for-quantum-chemistry | 1704.01212 | null | http://arxiv.org/abs/1704.01212v2 | http://arxiv.org/pdf/1704.01212v2.pdf | Neural Message Passing for Quantum Chemistry | Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm and aggregation procedure to compute a function of their
entire input graph. At this point, the next step is to find a particularly
effective variant of this general approach and apply it to chemical prediction
benchmarks until we either solve them or reach the limits of the approach. In
this paper, we reformulate existing models into a single common framework we
call Message Passing Neural Networks (MPNNs) and explore additional novel
variations within this framework. Using MPNNs we demonstrate state of the art
results on an important molecular property prediction benchmark; these results
are strong enough that we believe future work should focus on datasets with
larger molecules or more accurate ground truth labels. | ['Samuel S. Schoenholz', 'Patrick F. Riley', 'Justin Gilmer', 'George E. Dahl', 'Oriol Vinyals'] | 2017-04-04 | neural-message-passing-for-quantum-chemistry-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=529 | http://proceedings.mlr.press/v70/gilmer17a/gilmer17a.pdf | icml-2017-8 | ['graph-regression', 'formation-energy'] | ['graphs', 'miscellaneous'] | [ 6.57929122e-01 2.05640748e-01 -7.91568935e-01 -4.80807126e-01
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ff8dc5ff-2c83-4337-9af0-d6e8d4646ab4 | queaco-borrowing-treasures-from-weakly | 2108.08468 | null | https://arxiv.org/abs/2108.08468v3 | https://arxiv.org/pdf/2108.08468v3.pdf | QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction | We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: {named entity recognition (NER)} and {attribute value normalization (AVN)}. However, existing works only focus on the NER phase but neglect equally important AVN. To bridge this gap, this paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO, which involves both two phases. Moreover, by leveraging large-scale weakly-labeled behavior data, we further improve the extraction performance with less supervision cost. Specifically, for the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels to refine the weakly-labeled data for training a student network. Meanwhile, the teacher network can be dynamically adapted by the feedback of the student's performance on strongly-labeled data to maximally denoise the noisy supervisions from the weak labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products. Extensive experiments on a real-world large-scale E-commerce dataset demonstrate the effectiveness of QUEACO. | ['Qiang Yang', 'Tuo Zhao', 'Bing Yin', 'Yiwei Song', 'Hanqing Lu', 'Tony Wu', 'Chen Luo', 'Tianyu Cao', 'Zheng Li', 'Danqing Zhang'] | 2021-08-19 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.60143541e-02 3.92569676e-02 -7.01632202e-01 -9.33512330e-01
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a39f7d85-8009-4dd6-8623-9ddee7684f8a | anomaly-detection-via-oversampling-principal | null | null | https://link.springer.com/chapter/10.1007/978-3-642-00909-9_43 | https://github.com/SohanLalYadav2304/Anomaly-detection-via-oversampling-principal-component-analysis/blob/main/C18_Anomaly%20Detection%20via%20Over-sampling%20Principal%20Component%20Analysis.pdf | Anomaly Detection via oversampling Principal Component Analysis | Abstract Outlier detection is an important issue in data mining and has been studied
in different research areas. It can be used for detecting the small amount of deviated
data. In this article, we use “Leave One Out” procedure to check each individual
point the “with or without” effect on the variation of principal directions. Based on
this idea, an over-sampling principal component analysis outlier detection method
is proposed for emphasizing the influence of an abnormal instance (or an outlier).
Except for identifying the suspicious outliers, we also design an on-line anomaly
detection to detect the new arriving anomaly. In addition, we also study the quick
updating of the principal directions for the effective computation and satisfying the
on-line detecting demand. Numerical experiments show that our proposed method
is effective in computation time and anomaly detection. | ['Yuh-Jye Lee', 'Zheng-Yi Lee', 'Yi-Ren Yeh'] | 2013-07-01 | null | null | null | 11th-ieee-international-conference-on | ['anomaly-detection', 'outlier-detection'] | ['methodology', 'methodology'] | [-2.57783383e-01 -6.75721228e-01 2.52373010e-01 -9.78129953e-02
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ef4c7105-11ac-44ec-ba5f-ff420c3c8a95 | sslayout360-semi-supervised-indoor-layout | 2103.13696 | null | https://arxiv.org/abs/2103.13696v3 | https://arxiv.org/pdf/2103.13696v3.pdf | SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama | Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to learn representations of room corners and boundaries by using a combination of labeled and unlabeled data for improved layout estimation in a 360-degree panoramic scene. Through extensive comparative experiments, we demonstrate that our approach can advance layout estimation of complex indoor scenes using as few as 20 labeled examples. When coupled with a layout predictor pre-trained on synthetic data, our semi-supervised method matches the fully supervised counterpart using only 12% of the labels. Our work takes an important first step towards robust semi-supervised layout estimation that can enable many applications in 3D perception with limited labeled data. | ['Phi Vu Tran'] | 2021-03-25 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 4.06323612e-01 1.52593464e-01 4.87756394e-02 -7.99454153e-01
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1a7ae3d7-236d-4435-84c1-8973a1eb5daa | an-energy-approach-describes-spine | null | null | https://link.springer.com/article/10.1007/s10237-020-01390-9 | https://link.springer.com/article/10.1007/s10237-020-01390-9 | An energy approach describes spine equilibrium in adolescent idiopathic scoliosis | The adolescent idiopathic scoliosis (AIS) is a 3D deformity of the spine whose origin is unknown and clinical evolution unpredictable. In this work, a mixed theoretical and numerical approach based on energetic considerations is proposed to study the global spine deformations. The introduced mechanical model aims at overcoming the limitations of computational cost and high variability in physical parameters. The model is constituted of rigid vertebral bodies associated with 3D effective stiffness tensors. The spine equilibrium is found using minimization methods of the mechanical total energy which circumvents forces and loading calculation. The values of the model parameters exhibited in the stiffness tensor are retrieved using a combination of clinical images post-processing and inverse algorithms implementation. Energy distribution patterns can then be evaluated at the global spine scale to investigate given time patient-specific features. To verify the reliability of the numerical methods, a simplified model of spine was implemented. The methodology was then applied to a clinical case of AIS (13-year-old girl, Lenke 1A). Comparisons of the numerical spine geometry with clinical data equilibria showed numerical calculations were performed with great accuracy. The patient follow-up allowed us to highlight the energetic role of the apical and junctional zones of the deformed spine, the repercussion of sagittal bending in sacro-illiac junctions and the significant role of torsion with scoliosis aggravation. Tangible comparisons of output measures with clinical pathology knowledge provided a reliable basis for further use of those numerical developments in AIS classification, scoliosis evolution prediction and potentially surgical planning. | ['Roxane Compagnon & Pascal Swider', 'Jérôme Sales de Gauzy', 'Franck Accadbled', 'Vincent Doyeux', 'Pauline Assemat', 'Baptiste Brun-Cottan'] | 2020-10-02 | null | null | null | biomechanics-and-modeling-in-mechanobiology | ['total-energy'] | ['miscellaneous'] | [-1.94908321e-01 4.03324097e-01 -1.11791305e-01 7.03295246e-02
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af3a07b5-15b7-4ab2-a76a-dd566018272a | an-overview-of-facial-micro-expression | 2012.11307 | null | https://arxiv.org/abs/2012.11307v1 | https://arxiv.org/pdf/2012.11307v1.pdf | An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge | Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. | ['Wen-Huang Cheng', 'Hong-Han Shuai', 'Ling Lo', 'Hong-Xia Xie'] | 2020-12-21 | null | null | null | null | ['micro-expression-spotting', 'micro-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.47837770e-01 -9.59757194e-02 -4.05998826e-01 -8.92215371e-01
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5e4deef2-88f8-46e1-9bb0-d45001fbd909 | child-face-recognition-at-scale-synthetic | 2304.11685 | null | https://arxiv.org/abs/2304.11685v1 | https://arxiv.org/pdf/2304.11685v1.pdf | Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark | We address the need for a large-scale database of children's faces by using generative adversarial networks (GANs) and face age progression (FAP) models to synthesize a realistic dataset referred to as HDA-SynChildFaces. To this end, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which are subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, the presented pipeline allows to evenly distribute the races of subjects, allowing to generate a balanced and fair dataset with respect to race distribution. The created HDA-SynChildFaces consists of 1,652 subjects and a total of 188,832 images, each subject being present at various ages and with many different intra-subject variations. Subsequently, we evaluates the performance of various facial recognition systems on the generated database and compare the results of adults and children at different ages. The study reveals that children consistently perform worse than adults, on all tested systems, and the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and Black subjects and females performing worse than White and Latino Hispanic subjects and males. | ['Christian Rathgeb', 'Mathias Ibsen', 'Anders Bensen Ottsen', 'Magnus Falkenberg'] | 2023-04-23 | null | null | null | null | ['face-recognition', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 3.57627533e-02 2.66840279e-01 3.39806587e-01 -7.04956293e-01
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030ca9a5-db8b-45e7-9d60-358f910973e4 | graph-neural-processes-for-spatio-temporal | 2305.18719 | null | https://arxiv.org/abs/2305.18719v1 | https://arxiv.org/pdf/2305.18719v1.pdf | Graph Neural Processes for Spatio-Temporal Extrapolation | We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Networks or statistical approaches like Gaussian Processes for this task. However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. Specifically, we first learn deterministic spatio-temporal representations by stacking layers of causal convolutions and cross-set graph neural networks. Then, we learn latent variables for target locations through vertical latent state transitions along layers and obtain extrapolations. Importantly during the transitions, we propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that aggregates contexts considering uncertainties in context data and graph structure. Extensive experiments show that STGNP has desirable properties such as uncertainty estimates and strong learning capabilities, and achieves state-of-the-art results by a clear margin. | ['Roger Zimmermann', 'Yu Zheng', 'Hongyang Chen', 'Zhencheng Fan', 'Yuxuan Liang', 'Junfeng Hu'] | 2023-05-30 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 3.50089036e-02 2.23834470e-01 -1.30356357e-01 -3.68865639e-01
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a1beab54-b844-4240-8dcd-840ccb40f1ba | advancing-the-state-of-the-art-for-ecg | 2211.07579 | null | https://arxiv.org/abs/2211.07579v1 | https://arxiv.org/pdf/2211.07579v1.pdf | Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models | The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced structured state space models (SSMs) as a particularly promising approach due to its ability to capture long-term dependencies in time series. We demonstrate that this approach leads to significant improvements over the current state-of-the-art for ECG classification, which we trace back to individual pathologies. Furthermore, the model's ability to capture long-term dependencies allows to shed light on long-standing questions in the literature such as the optimal sampling rate or window size to train classification models. Interestingly, we find no evidence for using data sampled at 500Hz as opposed to 100Hz and no advantages from extending the model's input size beyond 3s. Based on this very promising first assessment, SSMs could develop into a new modeling paradigm for ECG analysis. | ['Nils Strodthoff', 'Temesgen Mehari'] | 2022-11-14 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.24827170e-01 -1.89786479e-02 -1.57284200e-01 -2.81212032e-01
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8b7ec351-b3ae-4740-ba7a-b52ea7cf69d0 | bio-joie-joint-representation-learning-of | 2103.04283 | null | https://arxiv.org/abs/2103.04283v1 | https://arxiv.org/pdf/2103.04283v1.pdf | Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases | The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease. | ['Wei Wang', 'Carlo Zaniolo', 'Yizhou Sun', 'Muhao Chen', 'Chelsea Ju', 'Junheng Hao'] | 2021-03-07 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.30945519e-01 -4.56361519e-03 -2.95435667e-01 -2.91339129e-01
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52e5be3c-bfeb-4948-9b12-423c782ea346 | the-gesture-authoring-space-authoring | 2207.01092 | null | https://arxiv.org/abs/2207.01092v1 | https://arxiv.org/pdf/2207.01092v1.pdf | The Gesture Authoring Space: Authoring Customised Hand Gestures for Grasping Virtual Objects in Immersive Virtual Environments | Natural user interfaces are on the rise. Manufacturers for Augmented, Virtual, and Mixed Reality head mounted displays are increasingly integrating new sensors into their consumer grade products, allowing gesture recognition without additional hardware. This offers new possibilities for bare handed interaction within virtual environments. This work proposes a hand gesture authoring tool for object specific grab gestures allowing virtual objects to be grabbed as in the real world. The presented solution uses template matching for gesture recognition and requires no technical knowledge to design and create custom tailored hand gestures. In a user study, the proposed approach is compared with the pinch gesture and the controller for grasping virtual objects. The different grasping techniques are compared in terms of accuracy, task completion time, usability, and naturalness. The study showed that gestures created with the proposed approach are perceived by users as a more natural input modality than the others. | ['Didier Stricker', 'Gerd Reis', 'Alexander Schäfer'] | 2022-07-03 | null | null | null | null | ['template-matching', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.18463598e-01 -6.29221499e-02 -9.91772786e-02 -1.43150717e-01
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1a2c1c46-dfa2-4cc5-b8db-a2f48a7eede7 | viewpoint-estimation-insights-model | 1807.01312 | null | http://arxiv.org/abs/1807.01312v1 | http://arxiv.org/pdf/1807.01312v1.pdf | Viewpoint Estimation-Insights & Model | This paper addresses the problem of viewpoint estimation of an object in a
given image. It presents five key insights that should be taken into
consideration when designing a CNN that solves the problem. Based on these
insights, the paper proposes a network in which (i) The architecture jointly
solves detection, classification, and viewpoint estimation. (ii) New types of
data are added and trained on. (iii) A novel loss function, which takes into
account both the geometry of the problem and the new types of data, is propose.
Our network improves the state-of-the-art results for this problem by 9.8%. | ['Ayellet Tal', 'Gilad Divon'] | 2018-07-03 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.02710673e-01 1.99181437e-02 1.78283170e-01 -4.75495964e-01
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7795c483-9bc9-4173-9b74-739bc9b95ff5 | dani-net-uncalibrated-photometric-stereo-by | 2303.15101 | null | https://arxiv.org/abs/2303.15101v2 | https://arxiv.org/pdf/2303.15101v2.pdf | DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering | Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance. | ['Xudong Jiang', 'Gang Pan', 'Boxin Shi', 'Qian Zheng', 'Zongrui Li'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.pdf | cvpr-2023-1 | ['inverse-rendering'] | ['computer-vision'] | [ 9.26692069e-01 5.16100526e-02 6.52325094e-01 -6.01497114e-01
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d2471696-dd55-4c66-9194-d603c048705c | thompson-sampling-for-high-dimensional-sparse | 2211.05964 | null | https://arxiv.org/abs/2211.05964v2 | https://arxiv.org/pdf/2211.05964v2.pdf | Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits | We consider the stochastic linear contextual bandit problem with high-dimensional features. We analyze the Thompson sampling algorithm using special classes of sparsity-inducing priors (e.g., spike-and-slab) to model the unknown parameter and provide a nearly optimal upper bound on the expected cumulative regret. To the best of our knowledge, this is the first work that provides theoretical guarantees of Thompson sampling in high-dimensional and sparse contextual bandits. For faster computation, we use variational inference instead of Markov Chain Monte Carlo (MCMC) to approximate the posterior distribution. Extensive simulations demonstrate the improved performance of our proposed algorithm over existing ones. | ['Ambuj Tewari', 'Saptarshi Roy', 'Sunrit Chakraborty'] | 2022-11-11 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 6.53720051e-02 -5.00432029e-02 -8.97050142e-01 -4.37605351e-01
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796d4196-0155-4985-98b8-a65bf538e4b2 | lip-reading-sentences-in-the-wild | 1611.05358 | null | http://arxiv.org/abs/1611.05358v2 | http://arxiv.org/pdf/1611.05358v2.pdf | Lip Reading Sentences in the Wild | The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos.
Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS)
network that learns to transcribe videos of mouth motion to characters; (2) a
curriculum learning strategy to accelerate training and to reduce overfitting;
(3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition,
consisting of over 100,000 natural sentences from British television.
The WLAS model trained on the LRS dataset surpasses the performance of all
previous work on standard lip reading benchmark datasets, often by a
significant margin. This lip reading performance beats a professional lip
reader on videos from BBC television, and we also demonstrate that visual
information helps to improve speech recognition performance even when the audio
is available. | ['Joon Son Chung', 'Andrew Zisserman', 'Oriol Vinyals', 'Andrew Senior'] | 2016-11-16 | lip-reading-sentences-in-the-wild-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Chung_Lip_Reading_Sentences_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chung_Lip_Reading_Sentences_CVPR_2017_paper.pdf | cvpr-2017-7 | ['lipreading'] | ['computer-vision'] | [ 6.22202158e-01 3.81345272e-01 -4.75983590e-01 -3.09197068e-01
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e7b320ce-773d-4131-a9de-314c7197e3b9 | phonocardiographic-sensing-using-deep | 1801.08322 | null | https://arxiv.org/abs/1801.08322v4 | https://arxiv.org/pdf/1801.08322v4.pdf | Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection | Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution. Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models deliver the abnormal heartbeat classification score with significant improvement. Our proposed approach using RNNs can be potentially be used for real-time abnormal heartbeat detection in the Internet of Medical Things for remote monitoring applications. | ['Muhammad Usman', 'Siddique Latif', 'Junaid Qadir', 'Rajib Rana'] | 2018-01-25 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 3.55114579e-01 2.16264687e-02 5.96882164e-01 -2.80855484e-02
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3c62ef77-9d5f-4ad3-94a8-db9cfd8f5255 | coupling-machine-learning-and-crop-modeling | 2008.04060 | null | https://arxiv.org/abs/2008.04060v2 | https://arxiv.org/pdf/2008.04060v2.pdf | Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt | This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions. | ['Sotirios V. Archontoulis', 'Guiping Hu', 'Mohsen Shahhosseini', 'Isaiah Huber'] | 2020-07-28 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.56127289e-01 -1.33202463e-01 -8.22781503e-01 -2.39014924e-01
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a3546c0c-4c91-4065-b8d3-2f31693ef48b | gpt4graph-can-large-language-models | 2305.15066 | null | https://arxiv.org/abs/2305.15066v2 | https://arxiv.org/pdf/2305.15066v2.pdf | GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking | Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language processing tasks. In the real world, graph data is ubiquitous and an essential part of AGI and prevails in domains like social network analysis, bioinformatics and recommender systems. The training corpus of large language models often includes some algorithmic components, which allows them to achieve certain effects on some graph data-related problems. However, there is still little research on their performance on a broader range of graph-structured data. In this study, we conduct an extensive investigation to assess the proficiency of LLMs in comprehending graph data, employing a diverse range of structural and semantic-related tasks. Our analysis encompasses 10 distinct tasks that evaluate the LLMs' capabilities in graph understanding. Through our study, we not only uncover the current limitations of language models in comprehending graph structures and performing associated reasoning tasks but also emphasize the necessity for further advancements and novel approaches to enhance their graph processing capabilities. Our findings contribute valuable insights towards bridging the gap between language models and graph understanding, paving the way for more effective graph mining and knowledge extraction. | ['Shi Han', 'Xinyi He', 'Mengyu Zhou', 'Hengyu Liu', 'Lun Du', 'Jiayan Guo'] | 2023-05-24 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.07112676e-01 5.14835656e-01 -2.58598298e-01 -1.07481457e-01
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a5c1577c-2554-4e9e-8a39-f1a49fe5af9b | chmusic-a-traditional-chinese-music-dataset | 2108.08470 | null | https://arxiv.org/abs/2108.08470v2 | https://arxiv.org/pdf/2108.08470v2.pdf | ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition | Musical instruments recognition is a widely used application for music information retrieval. As most of previous musical instruments recognition dataset focus on western musical instruments, it is difficult for researcher to study and evaluate the area of traditional Chinese musical instrument recognition. This paper propose a traditional Chinese music dataset for training model and performance evaluation, named ChMusic. This dataset is free and publicly available, 11 traditional Chinese musical instruments and 55 traditional Chinese music excerpts are recorded in this dataset. Then an evaluation standard is proposed based on ChMusic dataset. With this standard, researchers can compare their results following the same rule, and results from different researchers will become comparable. | ['Haoran Wei', 'Haidi Zhu', 'Yuxiang Zhu', 'Xia Gong'] | 2021-08-19 | null | null | null | null | ['instrument-recognition', 'music-information-retrieval'] | ['audio', 'music'] | [ 3.79610881e-02 -1.09471583e+00 -4.09838825e-01 1.71307772e-01
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9fb4cf4c-5adf-4bb8-90da-e1f31659c262 | few-shot-weakly-supervised-cybersecurity | 2304.07470 | null | https://arxiv.org/abs/2304.07470v1 | https://arxiv.org/pdf/2304.07470v1.pdf | Few-shot Weakly-supervised Cybersecurity Anomaly Detection | With increased reliance on Internet based technologies, cyberattacks compromising users' sensitive data are becoming more prevalent. The scale and frequency of these attacks are escalating rapidly, affecting systems and devices connected to the Internet. The traditional defense mechanisms may not be sufficiently equipped to handle the complex and ever-changing new threats. The significant breakthroughs in the machine learning methods including deep learning, had attracted interests from the cybersecurity research community for further enhancements in the existing anomaly detection methods. Unfortunately, collecting labelled anomaly data for all new evolving and sophisticated attacks is not practical. Training and tuning the machine learning model for anomaly detection using only a handful of labelled data samples is a pragmatic approach. Therefore, few-shot weakly supervised anomaly detection is an encouraging research direction. In this paper, we propose an enhancement to an existing few-shot weakly-supervised deep learning anomaly detection framework. This framework incorporates data augmentation, representation learning and ordinal regression. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT. | ['Vrizlynn L. L. Thing', 'Rahul Kale'] | 2023-04-15 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-1.93177640e-01 -4.51458722e-01 -1.44607022e-01 -3.08301568e-01
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5de1d25a-7ac6-4eab-85cc-e4b0f085c0b2 | margin-mixup-a-method-for-robust-speaker | 2304.03515 | null | https://arxiv.org/abs/2304.03515v1 | https://arxiv.org/pdf/2304.03515v1.pdf | Margin-Mixup: A Method for Robust Speaker Verification in Multi-Speaker Audio | This paper is concerned with the task of speaker verification on audio with multiple overlapping speakers. Most speaker verification systems are designed with the assumption of a single speaker being present in a given audio segment. However, in a real-world setting this assumption does not always hold. In this paper, we demonstrate that current speaker verification systems are not robust against audio with noticeable speaker overlap. To alleviate this issue, we propose margin-mixup, a simple training strategy that can easily be adopted by existing speaker verification pipelines to make the resulting speaker embeddings robust against multi-speaker audio. In contrast to other methods, margin-mixup requires no alterations to regular speaker verification architectures, while attaining better results. On our multi-speaker test set based on VoxCeleb1, the proposed margin-mixup strategy improves the EER on average with 44.4% relative to our state-of-the-art speaker verification baseline systems. | ['Kris Demuynck', 'Nilesh Madhu', 'Jenthe Thienpondt'] | 2023-04-07 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 2.01578513e-01 1.84284430e-02 1.58133224e-01 -6.26622319e-01
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b210f7bc-88aa-42d4-ac6a-4adb0ce9306c | interactive-portrait-harmonization | 2203.08216 | null | https://arxiv.org/abs/2203.08216v1 | https://arxiv.org/pdf/2203.08216v1.pdf | Interactive Portrait Harmonization | Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected \emph{region} in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. Project Webpage at \href{https://jeya-maria-jose.github.io/IPH-web/}{https://jeya-maria-jose.github.io/IPH-web/} | ['Vishal M. Patel', 'Kalyan Sunkavalli', 'Zijun Wei', 'Yinglan Ma', 'Jose Echevarria', 'Zhe Lin', 'Yilin Wang', 'Jianming Zhang', 'He Zhang', 'Jeya Maria Jose Valanarasu'] | 2022-03-15 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.28910044e-01 -3.06730807e-01 -1.40227200e-02 -1.96090773e-01
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31013d4a-9867-4dd4-a4cb-9cd2fe828ead | visolo-grid-based-space-time-aggregation-for | 2112.04177 | null | https://arxiv.org/abs/2112.04177v2 | https://arxiv.org/pdf/2112.04177v2.pdf | VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation | For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous methods follow a two-stage approach requiring additional computations such as RPN and RoIAlign, and do not fully exploit the available information in the video for all subtasks in VIS. In this paper, we propose a novel single-stage framework for online VIS built based on the grid structured feature representation. The grid-based features allow us to employ fully convolutional networks for real-time processing, and also to easily reuse and share features within different components. We also introduce cooperatively operating modules that aggregate information from available frames, in order to enrich the features for all subtasks in VIS. Our design fully takes advantage of previous information in a grid form for all tasks in VIS in an efficient way, and we achieved the new state-of-the-art accuracy (38.6 AP and 36.9 AP) and speed (40.0 FPS) on YouTube-VIS 2019 and 2021 datasets among online VIS methods. The code is available at https://github.com/SuHoHan95/VISOLO. | ['Seon Joo Kim', 'Min-Jung Kim', 'Hyunwoo Kim', 'Yeonchool Park', 'Seoung Wug Oh', 'Sukjun Hwang', 'Su Ho Han'] | 2021-12-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Han_VISOLO_Grid-Based_Space-Time_Aggregation_for_Efficient_Online_Video_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Han_VISOLO_Grid-Based_Space-Time_Aggregation_for_Efficient_Online_Video_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-2.91173190e-01 -2.70091712e-01 -2.07896873e-01 -2.39181221e-01
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1d2693a5-ac86-4882-9d63-a81f95d7197f | show-dont-tell-demonstrations-outperform | null | null | https://aclanthology.org/2022.naacl-main.336 | https://aclanthology.org/2022.naacl-main.336.pdf | Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue | Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge. Recent works have leveraged natural language descriptions of schema elements to enable such systems; however, descriptions only indirectly convey schema semantics. In this work, we propose Show, Don’t Tell, which prompts seq2seq models with a labeled example dialogue to show the semantics of schema elements rather than tell the model through descriptions. While requiring similar effort from service developers as generating descriptions, we show that using short examples as schema representations with large language models results in state-of-the-art performance on two popular dialogue state tracking benchmarks designed to measure zero-shot generalization - the Schema-Guided Dialogue dataset and the MultiWOZ leave-one-out benchmark. | ['Yonghui Wu', 'Abhinav Rastogi', 'Yuan Cao', 'Jeffrey Zhao', 'Harrison Lee', 'Raghav Gupta'] | null | null | null | null | naacl-2022-7 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.66303024e-01 6.90144718e-01 -3.95551175e-01 -8.96009922e-01
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2f239409-5c55-457b-9cb1-d5cdc353b11e | a-one-class-classification-method-based-on | null | null | https://doi.org/10.1016/j.inffus.2022.07.023 | https://doi.org/10.1016/j.inffus.2022.07.023 | A One-Class Classification method based on Expanded Non-Convex Hulls | This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method begins by reducing the dimensionality of the data to two-dimensional spaces using random projections. After that, an iterative process based on Delaunay triangulations is applied to these spaces to obtain simple polygons that characterizes the non-convex shape of the normal class data. In addition, the method subdivides the non- convex hulls to represent separate regions in space if necessary. The method has been evaluated and compared to several main algorithms of the field using real data sets. In contrast to other methods, OCENCH can deal with non-convex and disjointed shapes. Finally, its execution can be carried out in a parallel way, which is interesting to reduce the execution time. | ['Bertha Guijarro-Berdiñas', 'Oscar Fontenla-Romero', 'David Novoa-Paradela'] | 2022-08-01 | null | null | null | information-fusion-2022-8 | ['supervised-anomaly-detection', 'one-class-classification'] | ['computer-vision', 'miscellaneous'] | [ 8.60456899e-02 3.54748100e-01 2.00205445e-01 -2.13235945e-01
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6a296ea7-4eed-4668-b230-887b6778ab68 | impact-of-naturalistic-field-acoustic | 2201.13246 | null | https://arxiv.org/abs/2201.13246v1 | https://arxiv.org/pdf/2201.13246v1.pdf | Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System | Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process. Forensic speech data as potential evidence can be obtained in random naturalistic environments resulting in variable data quality. Speech samples may include variability due to vocal efforts such as yelling over 911 emergency calls, whereas others might be whisper or situational stressed voice in a field location or interview room. Such speech variability consists of intrinsic and extrinsic characteristics and makes forensic speaker verification a complicated and daunting task. Extrinsic properties include recording equipment such as microphone type and placement, ambient noise, room configuration including reverberation, and other environmental scenario-based issues. Some factors, such as noise and non-target speech, will impact the verification system performance by their mere presence. To investigate the impact of field acoustic environments, we performed a speaker verification study based on the CRSS-Forensic corpus with audio collected from 8 field locations including police interviews. This investigation includes an analysis of the impact of seven unseen acoustic environments on speaker verification system performance using an x-Vector system. | ['John H. L. Hansen', 'Zhenyu Wang'] | 2022-01-28 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.22754388e-01 -6.34503663e-01 8.64958942e-01 -4.61428791e-01
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57e9419a-88ee-4ac9-9845-9c7bd1d4fc73 | activation-template-matching-loss-for | 2207.02179 | null | https://arxiv.org/abs/2207.02179v1 | https://arxiv.org/pdf/2207.02179v1.pdf | Activation Template Matching Loss for Explainable Face Recognition | Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss. | ['Linlin Shen', 'Qiufu Li', 'Haozhe Liu', 'Huawei Lin'] | 2022-07-05 | null | null | null | null | ['template-matching', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [ 5.94947971e-02 6.11796498e-01 -2.65300184e-01 -9.66151834e-01
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20d3d700-65c8-4ff2-9528-06731ee4784f | structure-transformed-texture-enhanced | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Structure-Transformed_Texture-Enhanced_Network_for_Person_Image_Synthesis_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Structure-Transformed_Texture-Enhanced_Network_for_Person_Image_Synthesis_ICCV_2021_paper.pdf | Structure-Transformed Texture-Enhanced Network for Person Image Synthesis | Pose-guided virtual try-on task aims to modify the fashion item based on pose transfer task. These two tasks that belong to person image synthesis have strong correlations and similarities. However, existing methods treat them as two individual tasks and do not explore correlations between them. Moreover, these two tasks are challenging due to large misalignment and occlusions, thus most of these methods are prone to generate unclear human body structure and blurry fine-grained textures. In this paper, we devise a structure-transformed texture-enhanced network to generate high-quality person images and construct the relationships between two tasks. It consists of two modules: structure-transformed renderer and texture-enhanced stylizer. The structure-transformed renderer is introduced to transform the source person structure to the target one, while the texture-enhanced stylizer is served to enhance detailed textures and controllably inject the fashion style founded on the structural transformation. With the two modules, our model can generate photorealistic person images in diverse poses and even with various fashion styles. Extensive experiments demonstrate that our approach achieves state-of-the-art results on two tasks. | ['Ge Li', 'Thomas H. Li', 'Shan Liu', 'Yuanqi Chen', 'Munan Xu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 2.94846177e-01 -5.28150760e-02 2.06477165e-01 -2.53464371e-01
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56d7d829-e4a0-44df-a68e-03e90de73b3f | image-clustering-with-contrastive-learning | 2207.07173 | null | https://arxiv.org/abs/2207.07173v1 | https://arxiv.org/pdf/2207.07173v1.pdf | Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks | Deep clustering has recently attracted significant attention. Despite the remarkable progress, most of the previous deep clustering works still suffer from two limitations. First, many of them focus on some distribution-based clustering loss, lacking the ability to exploit sample-wise (or augmentation-wise) relationships via contrastive learning. Second, they often neglect the indirect sample-wise structure information, overlooking the rich possibilities of multi-scale neighborhood structure learning. In view of this, this paper presents a new deep clustering approach termed Image clustering with contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN), which bridges the gap between convolutional neural network (CNN) and graph convolutional network (GCN) as well as the gap between contrastive learning and multi-scale neighborhood structure learning for the image clustering task. The proposed IcicleGCN framework consists of four main modules, namely, the CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster Structure Learning and Instance reconstruction Module (JC-SLIM), and the Multi-scale GCN module (M-GCN). Specifically, with two random augmentations performed on each image, the backbone network with two weight-sharing views is utilized to learn the representations for the augmented samples, which are then fed to ISM and JC-SLIM for instance-level and cluster-level contrastive learning, respectively. Further, to enforce multi-scale neighborhood structure learning, two streams of GCNs and an auto-encoder are simultaneously trained via (i) the layer-wise interaction with representation fusion and (ii) the joint self-adaptive learning that ensures their last-layer output distributions to be consistent. Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art. | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Dong Huang', 'Yuanku Xu'] | 2022-07-14 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 1.87394582e-02 -1.36388287e-01 -5.63931428e-02 -3.12108099e-01
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50ded73c-aed5-4fb9-927b-ec073d43fc75 | spatio-temporal-graph-mixformer-for-traffic | null | null | https://doi.org/10.1016/j.eswa.2023.120281 | https://doi.org/10.1016/j.eswa.2023.120281 | Spatio-Temporal Graph Mixformer for Traffic Forecasting | Traffic forecasting is of great importance for intelligent transportation systems (ITS). Because of the intricacy implied in traffic behavior and the non-Euclidean nature of traffic data, it is challenging to give an accurate traffic prediction. Despite that previous studies considered the relationship between different nodes, the majority have relied on a static representation and failed to capture the dynamic node interactions over time. Additionally, prior studies employed RNN-based models to capture the temporal dependency. While RNNs are a popular choice for forecasting problems, they tend to be memory hungry and slow to train. Furthermore, recent studies start utilizing similarity algorithms to better express the implication of a node over the other. However, to our knowledge, none have explored the contribution of node $𝑖$’s past, over the future state of node $𝑗$. In this paper, we propose a Spatio-Temporal Graph Mixformer (STGM) network, a highly optimized model with low memory footprint. We address the aforementioned limits by utilizing a novel attention mechanism to capture the correlation between temporal and spatial dependencies. Specifically, we use convolution layers with a variable field of view for each head to capture long–short term temporal dependency. Additionally, we train an estimator model that express the contribution of a node over the desired prediction. The estimation is fed alongside a distance matrix to the attention mechanism. Meanwhile, we use a gated mechanism and a mixer layer to further select and incorporate the different perspectives. Extensive experiments show that the proposed model enjoys a performance gain compared to the baselines while maintaining the lowest parameter counts. | ['Yanming Shen', 'Mourad Lablack'] | 2023-10-15 | null | null | null | expert-systems-with-applications-2023-10 | ['traffic-prediction'] | ['time-series'] | [-2.13337421e-01 -2.84364879e-01 -3.34924221e-01 -5.02421856e-01
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392d2cf6-e2f7-4ab6-86fc-030f0ad6eb27 | identifying-implicitly-abusive-remarks-about | null | null | https://aclanthology.org/2022.naacl-main.410 | https://aclanthology.org/2022.naacl-main.410.pdf | Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach | We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”). Implicitly abusive language are utterances not conveyed by abusive words (e.g. “bimbo” or “scum”). So far, the detection of such utterances could not be properly addressed since existing datasets displaying a high degree of implicit abuse are fairly biased. Following the recently-proposed strategy to solve implicit abuse by separately addressing its different subtypes, we present a new focused and less biased dataset that consists of the subtype of atomic negative sentences about identity groups. For that task, we model components that each address one facet of such implicit abuse, i.e. depiction as perpetrators, aspectual classification and non-conformist views. The approach generalizes across different identity groups and languages. | ['Josef Ruppenhofer', 'Elisabeth Eder', 'Michael Wiegand'] | null | null | null | null | naacl-2022-7 | ['abusive-language'] | ['natural-language-processing'] | [ 1.73710302e-01 2.87197709e-01 -3.86363834e-01 -6.66560709e-01
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bc8c2857-8f5d-42f9-908c-3a26ae4b9481 | lightweight-sound-event-detection-model-with | null | null | https://aclanthology.org/2022.rocling-1.17 | https://aclanthology.org/2022.rocling-1.17.pdf | Lightweight Sound Event Detection Model with RepVGG Architecture | In this paper, we proposed RepVGGRNN, which is a light weight sound event detection model. We use RepVGG convolution blocks in the convolution part to improve performance, and re-parameterize the RepVGG blocks after the model is trained to reduce the parameters of the convolution layers. To further improve the accuracy of the model, we incorporated both the mean teacher method and knowledge distillation to train the lightweight model. The proposed system achieves PSDS (Polyphonic sound event detection score)-scenario 1, 2 of 40.8% and 67.7% outperforms the baseline system of 34.4% and 57.2% on the DCASE 2022 Task4 validation dataset. The quantity of the parameters in the proposed system is about 49.6K, only 44.6
% of the baseline system. | ['Wei-Yu Chen', 'Hsiang-Feng Chuang', 'Yu-Han Cheng', 'Bo-Cheng Chan', 'Chung-Li Lu', 'Chia-Ping Chen', 'Sung-Jen Huang', 'Chia-Chuan Liu'] | null | null | null | null | rocling-2022-11 | ['sound-event-detection'] | ['audio'] | [-2.48294026e-01 6.50528818e-03 3.07717949e-01 -1.65084392e-01
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ae546a4f-3dd2-447f-900a-09357f072e1b | symmetry-informed-geometric-representation | 2306.09375 | null | https://arxiv.org/abs/2306.09375v1 | https://arxiv.org/pdf/2306.09375v1.pdf | Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials | Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery. For these scientific problems, molecules serve as the fundamental building blocks, and machine learning has emerged as a highly effective and powerful tool for modeling their geometric structures. Nevertheless, due to the rapidly evolving process of the field and the knowledge gap between science (e.g., physics, chemistry, & biology) and machine learning communities, a benchmarking study on geometrical representation for such data has not been conducted. To address such an issue, in this paper, we first provide a unified view of the current symmetry-informed geometric methods, classifying them into three main categories: invariance, equivariance with spherical frame basis, and equivariance with vector frame basis. Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets, including small molecules, proteins, and crystalline materials. We hope that Geom3D can, on the one hand, eliminate barriers for machine learning researchers interested in exploring scientific problems; and, on the other hand, provide valuable guidance for researchers in computational chemistry, structural biology, and materials science, aiding in the informed selection of representation techniques for specific applications. | ['Jian Tang', 'Hongyu Guo', 'Jennifer Chayes', 'Christian Borgs', 'Anima Anandkumar', 'Omar Yaghi', 'ZhiMing Ma', 'Chenru Duan', 'Zhiling Zheng', 'Zhuoxinran Li', 'Yanjing Li', 'Weitao Du', 'Shengchao Liu'] | 2023-06-15 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 8.61102864e-02 -1.90478668e-01 -4.70701009e-01 -8.46119970e-02
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d08e181b-2d4c-4ae0-9691-df710bf60268 | deciwatch-a-simple-baseline-for-10x-efficient | 2203.08713 | null | https://arxiv.org/abs/2203.08713v2 | https://arxiv.org/pdf/2203.08713v2.pdf | DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation | This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch. | ['Qiang Xu', 'Bo Dai', 'Xizhou Zhu', 'Ruiyuan Gao', 'Lei Yang', 'Xuan Ju', 'Ailing Zeng'] | 2022-03-16 | null | null | null | null | ['3d-pose-estimation', '2d-human-pose-estimation', '3d-shape-reconstruction-from-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.25023199e-02 2.71003358e-02 -1.81140363e-01 -1.42951146e-01
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efd994b7-b628-4a0c-bd90-48d4f4458c9f | open-world-text-specified-object-counting | 2306.01851 | null | https://arxiv.org/abs/2306.01851v1 | https://arxiv.org/pdf/2306.01851v1.pdf | Open-world Text-specified Object Counting | Our objective is open-world object counting in images, where the target object class is specified by a text description. To this end, we propose CounTX, a class-agnostic, single-stage model using a transformer decoder counting head on top of pre-trained joint text-image representations. CounTX is able to count the number of instances of any class given only an image and a text description of the target object class, and can be trained end-to-end. To the best of our knowledge, we are the first to tackle the open-world counting problem in this way. In addition to this model, we make the following contributions: (i) we compare the performance of CounTX to prior work on open-world object counting, and show that our approach exceeds the state of the art on all measures on the FSC-147 benchmark for methods that use text to specify the task; (ii) we present and release FSC-147-D, an enhanced version of FSC-147 with text descriptions, so that object classes can be described with more detailed language than their simple class names. FSC-147-D is available at https://github.com/niki-amini-naieni/CounTX/. | ['Andrew Zisserman', 'Tengda Han', 'Kiana Amini-Naieni', 'Niki Amini-Naieni'] | 2023-06-02 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.94865823e-01 -2.66303629e-01 -1.44721746e-01 -5.24385095e-01
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4375b4ae-a43f-48a0-84ac-ab116be8ae1a | ebsr-enhanced-binary-neural-network-for-image | 2303.12270 | null | https://arxiv.org/abs/2303.12270v1 | https://arxiv.org/pdf/2303.12270v1.pdf | EBSR: Enhanced Binary Neural Network for Image Super-Resolution | While the performance of deep convolutional neural networks for image super-resolution (SR) has improved significantly, the rapid increase of memory and computation requirements hinders their deployment on resource-constrained devices. Quantized networks, especially binary neural networks (BNN) for SR have been proposed to significantly improve the model inference efficiency but suffer from large performance degradation. We observe the activation distribution of SR networks demonstrates very large pixel-to-pixel, channel-to-channel, and image-to-image variation, which is important for high performance SR but gets lost during binarization. To address the problem, we propose two effective methods, including the spatial re-scaling as well as channel-wise shifting and re-scaling, which augments binary convolutions by retaining more spatial and channel-wise information. Our proposed models, dubbed EBSR, demonstrate superior performance over prior art methods both quantitatively and qualitatively across different datasets and different model sizes. Specifically, for x4 SR on Set5 and Urban100, EBSRlight improves the PSNR by 0.31 dB and 0.28 dB compared to SRResNet-E2FIF, respectively, while EBSR outperforms EDSR-E2FIF by 0.29 dB and 0.32 dB PSNR, respectively. | ['Ru Huang', 'Runsheng Wang', 'Yuchen Fan', 'Meng Li', 'Zechun Liu', 'Shuwen Zhang', 'Renjie Wei'] | 2023-03-22 | null | null | null | null | ['image-super-resolution', 'image-variation'] | ['computer-vision', 'computer-vision'] | [ 7.01208293e-01 -3.32796574e-01 -1.57504052e-01 -4.46386188e-01
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01bcccc1-5c89-47aa-a068-47135a807368 | contrastive-mixture-of-posteriors-for | 2106.08161 | null | https://arxiv.org/abs/2106.08161v4 | https://arxiv.org/pdf/2106.08161v4.pdf | Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness | Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology. Adopting a Conditional VAE framework, we show that marginal independence between the representation and a condition variable plays a key role in both of these challenges. We propose the Contrastive Mixture of Posteriors (CoMP) method that uses a novel misalignment penalty defined in terms of mixtures of the variational posteriors to enforce this independence in latent space. We show that CoMP has attractive theoretical properties compared to previous approaches, and we prove counterfactual identifiability of CoMP under additional assumptions. We demonstrate state-of-the-art performance on a set of challenging tasks including aligning human tumour samples with cancer cell-lines, predicting transcriptome-level perturbation responses, and batch correction on single-cell RNA sequencing data. We also find parallels to fair representation learning and demonstrate that CoMP is competitive on a common task in the field. | ['Aaron Sim', 'Sam Abujudeh', 'Páidí Creed', 'Craig A Glastonbury', 'Árpi Vezér', 'Adam Foster'] | 2021-06-15 | null | https://openreview.net/forum?id=AjZCiKuWQ9n | https://openreview.net/pdf?id=AjZCiKuWQ9n | neurips-2021-12 | ['data-integration', 'counterfactual-inference'] | ['knowledge-base', 'miscellaneous'] | [ 9.75536168e-01 -2.47676969e-02 -2.12397039e-01 -3.37479174e-01
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4273db44-2b1c-49fb-a2d0-0fee2db79c0b | graph-sampling-based-meta-learning-for | 2306.16780 | null | https://arxiv.org/abs/2306.16780v1 | https://arxiv.org/pdf/2306.16780v1.pdf | Graph Sampling-based Meta-Learning for Molecular Property Prediction | Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. First, we construct a Molecule-Property relation Graph (MPG): molecule and properties are nodes, while property labels decide edges. Then, to utilize the topological information of MPG, we reformulate an episode in meta-learning as a subgraph of the MPG, containing a target property node, molecule nodes, and auxiliary property nodes. Third, as episodes in the form of subgraphs are no longer independent of each other, we propose to schedule the subgraph sampling process with a contrastive loss function, which considers the consistency and discrimination of subgraphs. Extensive experiments on 5 commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art methods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta. | ['Huajun Chen', 'Yin Fang', 'Keyan Ding', 'Bin Wu', 'Qiang Zhang', 'Xiang Zhuang'] | 2023-06-29 | null | null | null | null | ['graph-sampling', 'property-prediction', 'meta-learning', 'molecular-property-prediction'] | ['graphs', 'medical', 'methodology', 'miscellaneous'] | [ 3.08970690e-01 -1.66254893e-01 -8.20513070e-01 -2.56988019e-01
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f5ec1e31-348f-4a6b-ae7d-f397f793b67a | splicecombo-a-hybrid-technique-efficiently | 1907.09401 | null | http://arxiv.org/abs/1907.09401v1 | http://arxiv.org/pdf/1907.09401v1.pdf | SpliceCombo: A Hybrid Technique efficiently use for Principal Component Analysis of Splice Site Prediction | The primary step in search of the gene prediction is an identification of the
coding region from genomic DNA sequence. Gene structure in the case of a
eukaryotic organism is composed of promoter, intron, start codon, exons, stop
codon, etc. Splice site prediction, which separates the junction between exon
and intron, though the sequence beside. The splice sites have huge
preservation, however, the precision of the tool exhibits less than 90%. The
main objective of this work to exhibits a hybrid technique that efficiently
improves the existing gene recognition technique. Therefore to enhance the
identification of splice sites, the respective algorithm needs to be improved.
Over the last decade, the researcher paid more attention to improve the
accuracy of a predicted model in this domain. Our proposed method, SpliceCombo
involves three stages. At initial stage, which considers the principal
Component Analysis, based on the feature extracted. In the intermediate stage,
i.e.,, the second stage Case- Based Reasoning is done, i.e., feature selection.
The third stage uses support vector machine based along with polynomial kernel
function for final classification. In comparison with other methods, the
proposed SpliceCombo model outperforms other prediction models with respect to
prediction accuracies. Particularly for donor splice site the methodology
exhibits sensitivity is 97.25% accurate and specificity is 97.46% accurate. For
acceptor Splice Site the sensitivity is 96.51% and Specificity is 94.48%
correct. | [] | 2019-07-19 | null | null | null | null | ['splice-site-prediction'] | ['medical'] | [ 3.54395419e-01 -7.38437101e-02 4.92549911e-02 -1.34571344e-01
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32ec48be-7099-4b6d-9c77-8be6ba3f16ec | reliable-multimodal-trajectory-prediction-via | 2212.04812 | null | https://arxiv.org/abs/2212.04812v1 | https://arxiv.org/pdf/2212.04812v1.pdf | Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization | Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is challenging as ground truth for uncertainty estimates is not available. Ideally, in a well-calibrated model, uncertainty estimates should perfectly correlate with model error. We propose a novel error aligned uncertainty optimization method and introduce a trainable loss function to guide the models to yield good quality uncertainty estimates aligning with the model error. Our approach targets continuous structured prediction and regression tasks, and is evaluated on multiple datasets including a large-scale vehicle motion prediction task involving real-world distributional shifts. We demonstrate that our method improves average displacement error by 1.69% and 4.69%, and the uncertainty correlation with model error by 17.22% and 19.13% as quantified by Pearson correlation coefficient on two state-of-the-art baselines. | ['Michael Paulitsch', 'Omesh Tickoo', 'Akash Dhamasia', 'Ranganath Krishnan', 'Neslihan Kose'] | 2022-12-09 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-1.48061246e-01 1.88341156e-01 -3.94186646e-01 -9.96490836e-01
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eb978c2f-54dd-4033-93bb-008d5a21d952 | deep-partial-multi-label-learning-with-graph | 2305.05882 | null | https://arxiv.org/abs/2305.05882v1 | https://arxiv.org/pdf/2305.05882v1.pdf | Deep Partial Multi-Label Learning with Graph Disambiguation | In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods. | ['Gang Chen', 'Songhe Feng', 'Ke Chen', 'Tianlei Hu', 'Weiwei Liu', 'Gengyu Lyu', 'Shisong Yang', 'Haobo Wang'] | 2023-05-10 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 3.05664480e-01 1.28024489e-01 -4.17165875e-01 -6.90974653e-01
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82b8abad-01f9-4abe-a9ce-474f12990c7e | mapconnet-self-supervised-3d-pose-transfer | 2304.13819 | null | https://arxiv.org/abs/2304.13819v1 | https://arxiv.org/pdf/2304.13819v1.pdf | MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning | 3D pose transfer is a challenging generation task that aims to transfer the pose of a source geometry onto a target geometry with the target identity preserved. Many prior methods require keypoint annotations to find correspondence between the source and target. Current pose transfer methods allow end-to-end correspondence learning but require the desired final output as ground truth for supervision. Unsupervised methods have been proposed for graph convolutional models but they require ground truth correspondence between the source and target inputs. We present a novel self-supervised framework for 3D pose transfer which can be trained in unsupervised, semi-supervised, or fully supervised settings without any correspondence labels. We introduce two contrastive learning constraints in the latent space: a mesh-level loss for disentangling global patterns including pose and identity, and a point-level loss for discriminating local semantics. We demonstrate quantitatively and qualitatively that our method achieves state-of-the-art results in supervised 3D pose transfer, with comparable results in unsupervised and semi-supervised settings. Our method is also generalisable to unseen human and animal data with complex topologies. | ['Tae-Kyun Kim', 'Zhixiang Chen', 'Jiaze Sun'] | 2023-04-26 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.88044333e-01 4.63900059e-01 -1.83316424e-01 -5.88896871e-01
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bd9a5791-f8a0-4b50-bacf-c9693a4ca433 | time-distributed-feature-learning-in-network | 2109.14696 | null | https://arxiv.org/abs/2109.14696v1 | https://arxiv.org/pdf/2109.14696v1.pdf | Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things | The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%. | ['Xiao-Ping Zhang', 'Sihao Zhao', 'Yoga Suhas Kuruba Manjunath'] | 2021-09-29 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-5.97454654e-03 -7.12365031e-01 -4.76230323e-01 -6.37625337e-01
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2f6fa269-d819-4c2e-9f51-8430a058a7d5 | factorising-meaning-and-form-for-intent | 2105.15053 | null | https://arxiv.org/abs/2105.15053v1 | https://arxiv.org/pdf/2105.15053v1.pdf | Factorising Meaning and Form for Intent-Preserving Paraphrasing | We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an encoder-decoder model to reconstruct a question from a paraphrase with the same meaning and an exemplar with the same surface form, leading to separated encoding spaces. We use a Vector-Quantized Variational Autoencoder to represent the surface form as a set of discrete latent variables, allowing us to use a classifier to select a different surface form at test time. Crucially, our method does not require access to an external source of target exemplars. Extensive experiments and a human evaluation show that we are able to generate paraphrases with a better tradeoff between semantic preservation and syntactic novelty compared to previous methods. | ['Mirella Lapata', 'Tom Hosking'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-long.112 | https://aclanthology.org/2021.acl-long.112.pdf | acl-2021-5 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.47846738e-01 4.29860771e-01 -1.73051253e-01 -4.31878030e-01
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