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6cd6fc82-6a2e-4e62-a8ba-0cae7bc7b77b | dancin-seq2seq-fooling-text-classifiers-with | 1712.05419 | null | http://arxiv.org/abs/1712.05419v1 | http://arxiv.org/pdf/1712.05419v1.pdf | DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation | Machine learning models are powerful but fallible. Generating adversarial
examples - inputs deliberately crafted to cause model misclassification or
other errors - can yield important insight into model assumptions and
vulnerabilities. Despite significant recent work on adversarial example
generation targeting image classifiers, relatively little work exists exploring
adversarial example generation for text classifiers; additionally, many
existing adversarial example generation algorithms require full access to
target model parameters, rendering them impractical for many real-world
attacks. In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm
for adversarial text example generation targeting largely black-box text
classifiers. We recast adversarial text example generation as a reinforcement
learning problem, and demonstrate that our algorithm offers preliminary but
promising steps towards generating semantically meaningful adversarial text
examples in a real-world attack scenario. | ['Catherine Wong'] | 2017-12-14 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 1.03511488e+00 6.55958951e-01 6.59513753e-03 -3.35791916e-01
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050e4f84-6e58-46a1-b7e3-634f659b1af6 | niletmrg-at-semeval-2017-task-8-determining | null | null | https://aclanthology.org/S17-2082 | https://aclanthology.org/S17-2082.pdf | NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter. | Final submission for NileTMRG on RumourEval 2017. | ['Samhaa R. El-Beltagy', 'Omar Enayet'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-4.36484426e-01 5.17918110e-01 -6.36819720e-01 -2.96494454e-01
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6a048045-6f97-43f6-9c19-8467fbffb83e | semi-supervised-word-sense-disambiguation-2 | null | null | https://aclanthology.org/2020.textgraphs-1.6 | https://aclanthology.org/2020.textgraphs-1.6.pdf | Semi-supervised Word Sense Disambiguation Using Example Similarity Graph | Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD problems. However, these previ-ous supervised approaches often suffer from the lack of manually sense-tagged exam-ples. In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples. The results of the experiments show that the proposed method performs better than the previous semi-supervised WSD method. Moreover, the graph structure between examples is effective for WSD and it is effective to utilize a graph structure obtained by fine-tuning BERT in the proposed method. | ['Minoru Sasaki', 'Rie Yatabe'] | null | null | null | null | coling-textgraphs-2020-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 7.42091099e-04 -4.92730252e-02 -2.28163943e-01 -2.58869410e-01
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dd80ce6b-2ff6-4152-af04-4dab9f942be3 | barcodes-for-medical-image-retrieval-using | 1609.05112 | null | http://arxiv.org/abs/1609.05112v1 | http://arxiv.org/pdf/1609.05112v1.pdf | Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform | Using content-based binary codes to tag digital images has emerged as a
promising retrieval technology. Recently, Radon barcodes (RBCs) have been
introduced as a new binary descriptor for image search. RBCs are generated by
binarization of Radon projections and by assembling them into a vector, namely
the barcode. A simple local thresholding has been suggested for binarization.
In this paper, we put forward the idea of "autoencoded Radon barcodes". Using
images in a training dataset, we autoencode Radon projections to perform
binarization on outputs of hidden layers. We employed the mini-batch stochastic
gradient descent approach for the training. Each hidden layer of the
autoencoder can produce a barcode using a threshold determined based on the
range of the logistic function used. The compressing capability of autoencoders
apparently reduces the redundancies inherent in Radon projections leading to
more accurate retrieval results. The IRMA dataset with 14,410 x-ray images is
used to validate the performance of the proposed method. The experimental
results, containing comparison with RBCs, SURF and BRISK, show that autoencoded
Radon barcode (ARBC) has the capacity to capture important information and to
learn richer representations resulting in lower retrieval errors for image
retrieval measured with the accuracy of the first hit only. | ['Hamid. R. Tizhoosh', 'Shamak Dutta', 'Christopher Mitcheltree', 'Shujin Zhu'] | 2016-09-16 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 1.63244799e-01 -3.89651567e-01 8.24660212e-02 -3.82128000e-01
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d957ff31-76bc-41cd-8807-aaebd654298d | dialogue-enhancement-and-listening-effort-in | 2207.14240 | null | https://arxiv.org/abs/2207.14240v2 | https://arxiv.org/pdf/2207.14240v2.pdf | Dialogue Enhancement and Listening Effort in Broadcast Audio: A Multimodal Evaluation | Dialogue enhancement (DE) plays a vital role in broadcasting, enabling the personalization of the relative level between foreground speech and background music and effects. DE has been shown to improve the quality of experience, intelligibility, and self-reported listening effort (LE). A physiological indicator of LE known from audiology studies is pupil size. The relation between pupil size and LE is typically studied using artificial sentences and background noises not encountered in broadcast content. This work evaluates the effect of DE on LE in a multimodal manner that includes pupil size (tracked by a VR headset) and real-world audio excerpts from TV. Under ideal listening conditions, 28 normal-hearing participants listened to 30 audio excerpts presented in random order and processed by conditions varying the relative level between foreground and background audio. One of these conditions employed a recently proposed source separation system to attenuate the background given the original mixture as the sole input. After listening to each excerpt, subjects were asked to repeat the heard sentence and self-report the LE. Mean pupil dilation and peak pupil dilation were analyzed and compared with the self-report and the word recall rate. The multimodal evaluation shows a consistent trend of decreasing LE along with decreasing background level. DE, also when enabled by source separation, significantly reduces the pupil size as well as the self-reported LE. This highlights the benefit of personalization functionalities at the user's end. | ['Emanuël A. P. Habets', 'Thomas Robotham', 'Matteo Torcoli'] | 2022-07-28 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [ 1.85853332e-01 -4.21306072e-03 2.83347577e-01 8.81906673e-02
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8ab2c548-4479-4ab2-812a-be0107b9f68b | full-resolution-quality-assessment-for | 2108.06144 | null | https://arxiv.org/abs/2108.06144v3 | https://arxiv.org/pdf/2108.06144v3.pdf | Full-resolution quality assessment for pansharpening | A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground-truths to be used as guidance for an objective evaluation has pushed the community to resort to two approaches which can also be jointly applied. Hence, two kinds of indexes can be found in the literature: i) reference-based reduced-resolution indexes aimed to assess the synthesis ability; ii) no-reference subjective quality indexes for full-resolution datasets aimed to assess spectral and spatial consistency. Both reference-based and no-reference indexes present critical shortcomings which motivate the community to explore new solutions. In this work, we propose an alternative no-reference full-resolution assessment framework. On one side we introduce a protocol, namely the reprojection protocol, to take care of the spectral consistency issue. On the other side, a new index of the spatial consistency between the pansharpened image and the panchromatic band at full resolution is also proposed. Experimental results carried out on different datasets/sensors demonstrate the effectiveness of the proposed approach. | ['Matteo Ciotola', 'Giuseppe Scarpa'] | 2021-08-13 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 5.75819850e-01 -3.41317862e-01 -6.17792867e-02 -2.30016112e-01
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50ab34d9-9a26-4677-a5f4-cd9cc15aeeb7 | dict2vec-learning-word-embeddings-using | null | null | https://aclanthology.org/D17-1024 | https://aclanthology.org/D17-1024.pdf | Dict2vec : Learning Word Embeddings using Lexical Dictionaries | Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words {--} natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task. | ['Julien Tissier', 'Christophe Gravier', 'Amaury Habrard'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-2.49518022e-01 -1.18381597e-01 -5.78130960e-01 -4.61297274e-01
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5ed219c7-b04d-4b11-9210-390062ac4182 | a-review-of-deep-learning-powered-mesh | 2303.02879 | null | https://arxiv.org/abs/2303.02879v1 | https://arxiv.org/pdf/2303.02879v1.pdf | A Review of Deep Learning-Powered Mesh Reconstruction Methods | With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life. Yet creating 3D shapes is arduous and requires significant professional knowledge. Meanwhile, Deep learning has enabled high-quality 3D shape reconstruction from various sources, making it a viable approach to acquiring 3D shapes with minimal effort. Importantly, to be used in common 3D applications, the reconstructed shapes need to be represented as polygonal meshes, which is a challenge for neural networks due to the irregularity of mesh tessellations. In this survey, we provide a comprehensive review of mesh reconstruction methods that are powered by machine learning. We first describe various representations for 3D shapes in the deep learning context. Then we review the development of 3D mesh reconstruction methods from voxels, point clouds, single images, and multi-view images. Finally, we identify several challenges in this field and propose potential future directions. | ['Zhiqin Chen'] | 2023-03-06 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [-6.59932718e-02 -8.77242833e-02 2.55012065e-01 -2.21778616e-01
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d85305a1-8d1e-4836-ba96-d94f6658e1df | the-blessing-of-heterogeneity-in-federated-q | 2305.10697 | null | https://arxiv.org/abs/2305.10697v1 | https://arxiv.org/pdf/2305.10697v1.pdf | The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond | When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning without the need of sharing local data. In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function by periodically aggregating local Q-estimates trained on local data alone. Focusing on infinite-horizon tabular Markov decision processes, we provide sample complexity guarantees for both the synchronous and asynchronous variants of federated Q-learning. In both cases, our bounds exhibit a linear speedup with respect to the number of agents and sharper dependencies on other salient problem parameters. Moreover, existing approaches to federated Q-learning adopt an equally-weighted averaging of local Q-estimates, which can be highly sub-optimal in the asynchronous setting since the local trajectories can be highly heterogeneous due to different local behavior policies. Existing sample complexity scales inverse proportionally to the minimum entry of the stationary state-action occupancy distributions over all agents, requiring that every agent covers the entire state-action space. Instead, we propose a novel importance averaging algorithm, giving larger weights to more frequently visited state-action pairs. The improved sample complexity scales inverse proportionally to the minimum entry of the average stationary state-action occupancy distribution of all agents, thus only requiring the agents collectively cover the entire state-action space, unveiling the blessing of heterogeneity. | ['Yuejie Chi', 'Gauri Joshi', 'Jiin Woo'] | 2023-05-18 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.60474968e-01 8.23655427e-02 -4.23676997e-01 1.72415644e-01
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1a4d3b2d-e309-48de-a2ee-14ab6398412e | guided-image-synthesis-via-initial-image | 2305.03382 | null | https://arxiv.org/abs/2305.03382v1 | https://arxiv.org/pdf/2305.03382v1.pdf | Guided Image Synthesis via Initial Image Editing in Diffusion Model | Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of image generation through adjusting the denoising process, we propose a novel direction of manipulating the initial noise to control the generated image. Through experiments on stable diffusion, we show that blocks of pixels in the initial latent images have a preference for generating specific content, and that modifying these blocks can significantly influence the generated image. In particular, we show that modifying a part of the initial image affects the corresponding region of the generated image while leaving other regions unaffected, which is useful for repainting tasks. Furthermore, we find that the generation preferences of pixel blocks are primarily determined by their values, rather than their position. By moving pixel blocks with a tendency to generate user-desired content to user-specified regions, our approach achieves state-of-the-art performance in layout-to-image generation. Our results highlight the flexibility and power of initial image manipulation in controlling the generated image. | ['Kiyoharu Aizawa', 'Xueting Wang', 'Jiafeng Mao'] | 2023-05-05 | null | null | null | null | ['layout-to-image-generation', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 7.16566563e-01 5.06427176e-02 1.18080959e-01 -2.28401557e-01
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35ecbb6c-44aa-4a46-97c7-cc9a4eadc54f | visual-aware-attention-dual-stream-decoder | 2110.08578 | null | https://arxiv.org/abs/2110.08578v1 | https://arxiv.org/pdf/2110.08578v1.pdf | Visual-aware Attention Dual-stream Decoder for Video Captioning | Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence. The attention mechanism in the current video captioning method learns to assign weight to each frame, promoting the decoder dynamically. This may not explicitly model the correlation and the temporal coherence of the visual features extracted in the sequence frames.To generate semantically coherent sentences, we propose a new Visual-aware Attention (VA) model, which concatenates dynamic changes of temporal sequence frames with the words at the previous moment, as the input of attention mechanism to extract sequence features.In addition, the prevalent approaches widely use the teacher-forcing (TF) learning during training, where the next token is generated conditioned on the previous ground-truth tokens. The semantic information in the previously generated tokens is lost. Therefore, we design a self-forcing (SF) stream that takes the semantic information in the probability distribution of the previous token as input to enhance the current token.The Dual-stream Decoder (DD) architecture unifies the TF and SF streams, generating sentences to promote the annotated captioning for both streams.Meanwhile, with the Dual-stream Decoder utilized, the exposure bias problem is alleviated, caused by the discrepancy between the training and testing in the TF learning.The effectiveness of the proposed Visual-aware Attention Dual-stream Decoder (VADD) is demonstrated through the result of experimental studies on Microsoft video description (MSVD) corpus and MSR-Video to text (MSR-VTT) datasets. | ['Luo Zhong', 'Lin Li', 'Shuqin Chen', 'Xian Zhong', 'Zhixin Sun'] | 2021-10-16 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.79059255e-01 -1.91469014e-01 -1.53827071e-01 -1.91259071e-01
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fd61b8e0-f6af-4693-b34c-faf87245418d | hybrid-feature-learning-for-handwriting | 1812.02621 | null | https://arxiv.org/abs/1812.02621v1 | https://arxiv.org/pdf/1812.02621v1.pdf | Hybrid Feature Learning for Handwriting Verification | We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE). Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT). Experiments are performed by complementing one of the HEF methods with one ALF method on 150000 pairs of samples of the word "AND" cropped from handwritten notes written by 1500 writers. Our results indicate that HDL architecture with AE-GSC achieves 99.7% accuracy on seen writer dataset and 92.16% accuracy on shuffled writer dataset which out performs CEDAR-FOX, as for unseen writer dataset, AE-SIFT performs comparable to this sophisticated handwriting comparison tool. | ['Sargur Srihari', 'Jun Chu', 'Mihir Chauhan', 'Mohammad Abuzar Shaikh'] | 2018-11-19 | null | null | null | null | ['handwriting-verification'] | ['computer-vision'] | [-1.04172520e-01 -2.12035328e-01 4.24148887e-01 -4.51056182e-01
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1992c67a-c270-43aa-af46-d5d21965c62d | pretraining-on-interactions-for-learning | 2207.02272 | null | https://arxiv.org/abs/2207.02272v1 | https://arxiv.org/pdf/2207.02272v1.pdf | Pretraining on Interactions for Learning Grounded Affordance Representations | Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study of these semantic features has not yet been integrated with the "foundation" models that currently dominate language representation research. We hypothesize that predictive modeling of object state over time will result in representations that encode object affordance information "for free". We train a neural network to predict objects' trajectories in a simulated interaction and show that our network's latent representations differentiate between both observed and unobserved affordances. We find that models trained using 3D simulations from our SPATIAL dataset outperform conventional 2D computer vision models trained on a similar task, and, on initial inspection, that differences between concepts correspond to expected features (e.g., roll entails rotation). Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations. | ['Ellie Pavlick', 'Carsten Eickhoff', 'Dylan Ebert', 'Jack Merullo'] | 2022-07-05 | null | https://aclanthology.org/2022.starsem-1.23 | https://aclanthology.org/2022.starsem-1.23.pdf | sem-naacl-2022-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.06195234e-01 2.80733734e-01 -3.90917271e-01 -4.81662840e-01
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a7b74cd3-e2ff-41d2-b989-f80235f45300 | umse-unified-multi-scenario-summarization | 2305.16895 | null | https://arxiv.org/abs/2305.16895v1 | https://arxiv.org/pdf/2305.16895v1.pdf | UMSE: Unified Multi-scenario Summarization Evaluation | Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating with human-labeled reference summary; (2) reference-free: evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore training neural models built on PLMs to align with human criteria. However, the models from different scenarios are optimized individually, which may result in sub-optimal performance since they neglect the shared knowledge across different scenarios. Besides, designing individual models for each scenario caused inconvenience to the user. Inspired by this, we propose Unified Multi-scenario Summarization Evaluation Model (UMSE). More specifically, we propose a perturbed prefix tuning method to share cross-scenario knowledge between scenarios and use a self-supervised training paradigm to optimize the model without extra human labeling. Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios. Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods which are specifically designed for each scenario. | ['Zhumin Chen', 'Zhaochun Ren', 'Pengjie Ren', 'Xiuying Chen', 'Chongyang Tao', 'Zhitao Yao', 'Shen Gao'] | 2023-05-26 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 3.80868554e-01 4.95366715e-02 -2.23146290e-01 -2.67342806e-01
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f2548a8d-9ac1-4862-a594-6f6251f67321 | how-to-choose-how-to-choose-your-chatbot-a | 2305.14533 | null | https://arxiv.org/abs/2305.14533v1 | https://arxiv.org/pdf/2305.14533v1.pdf | How to Choose How to Choose Your Chatbot: A Massively Multi-System MultiReference Data Set for Dialog Metric Evaluation | We release MMSMR, a Massively Multi-System MultiReference dataset to enable future work on metrics and evaluation for dialog. Automatic metrics for dialogue evaluation should be robust proxies for human judgments; however, the verification of robustness is currently far from satisfactory. To quantify the robustness correlation and understand what is necessary in a test set, we create and release an 8-reference dialog dataset by extending single-reference evaluation sets and introduce this new language learning conversation dataset. We then train 1750 systems and evaluate them on our novel test set and the DailyDialog dataset. We release the novel test set, and model hyper parameters, inference outputs, and metric scores for each system on a variety of datasets. | ['João Sedoc', 'Edward Cohen', 'Zuhaib Akhtar', 'Huda Khayrallah'] | 2023-05-23 | null | null | null | null | ['chatbot', 'dialogue-evaluation', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.70104730e-01 2.06782088e-01 1.11709900e-01 -8.40900600e-01
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61f51547-9386-4814-8a88-d5fecd899037 | pre-trained-language-model-with-prompts-for | 2305.07912 | null | https://arxiv.org/abs/2305.07912v1 | https://arxiv.org/pdf/2305.07912v1.pdf | Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion | Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing methods focus on learning representations based on graph neural networks while inaccurately extracting information from timestamps and insufficiently utilizing the implied information in relations. To address these problems, we propose a novel TKGC model, namely Pre-trained Language Model with Prompts for TKGC (PPT). We convert a series of sampled quadruples into pre-trained language model inputs and convert intervals between timestamps into different prompts to make coherent sentences with implicit semantic information. We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models. Experiments on three benchmark datasets and extensive analysis demonstrate that our model has great competitiveness compared to other models with four metrics. Our model can effectively incorporate information from temporal knowledge graphs into the language models. | ['Min Peng', 'Xu Jia', 'Miao Peng', 'Ben Liu', 'Wenjie Xu'] | 2023-05-13 | null | null | null | null | ['knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['knowledge-base', 'knowledge-base'] | [ 5.57652786e-02 4.23668236e-01 -6.39381170e-01 -5.82753897e-01
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a78cba5f-aa7b-47c1-afde-dd17555a58d7 | model-debiasing-via-gradient-based | 2305.12178 | null | https://arxiv.org/abs/2305.12178v1 | https://arxiv.org/pdf/2305.12178v1.pdf | Model Debiasing via Gradient-based Explanation on Representation | Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches. | ['Lei Chen', 'Caleb Chen Cao', 'Yongxiang Huang', 'Dan Su', 'Luning Wang', 'Jindi Zhang'] | 2023-05-20 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 3.16476971e-02 3.07673603e-01 -7.66104937e-01 -6.16566420e-01
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555e4823-b6cc-4cab-a999-c87d9943c108 | contrastive-language-image-pre-training-for | 2108.08688 | null | https://arxiv.org/abs/2108.08688v1 | https://arxiv.org/pdf/2108.08688v1.pdf | Contrastive Language-Image Pre-training for the Italian Language | CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not trivial, since data in other languages might be not enough and the model needs high-quality translations of the texts to guarantee a good performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms the multilingual CLIP model on the tasks of image retrieval and zero-shot classification. | ['Sri Lakshmi', 'Gabriele Sarti', 'Silvia Terragni', 'Raphael Pisoni', 'Giuseppe Attanasio', 'Federico Bianchi'] | 2021-08-19 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [-1.25429686e-02 -3.70677292e-01 -2.40843967e-01 -2.83557922e-01
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96136361-1a47-49a3-9cfe-7cdb1a55f56f | automated-generation-of-accurate-fluent-1 | null | null | https://aclanthology.org/2021.emnlp-main.288 | https://aclanthology.org/2021.emnlp-main.288.pdf | Automated Generation of Accurate & Fluent Medical X-ray Reports | Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. Our generated medical reports, on the other hand, are fluent and, more importantly, clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm that contains three complementary modules: taking the chest X-ray images and clinical history document of patients as inputs, our classification module produces an internal checklist of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, to produce the medical report; meanwhile, our generator also creates a weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics. Empirical evaluations demonstrate very promising results achieved by our approach on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently observed when additional input information is available, such as the clinical document and extra scans from different views. | ['Li Cheng', 'Jason Truong', 'Yingying Zhu', 'Yujie Liu', 'Taivanbat Badamdorj', 'Dong Nie', 'Hoang Nguyen'] | null | null | null | null | emnlp-2021-11 | ['medical-report-generation'] | ['medical'] | [ 3.19939375e-01 6.28013074e-01 -1.01505876e-01 -4.24099058e-01
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873cda03-e1f8-408f-b7b6-3c556bfbb40b | alien-coding | 2301.11479 | null | https://arxiv.org/abs/2301.11479v1 | https://arxiv.org/pdf/2301.11479v1.pdf | Alien Coding | We introduce a self-learning algorithm for synthesizing programs for OEIS sequences. The algorithm starts from scratch initially generating programs at random. Then it runs many iterations of a self-learning loop that interleaves (i) training neural machine translation to learn the correspondence between sequences and the programs discovered so far, and (ii) proposing many new programs for each OEIS sequence by the trained neural machine translator. The algorithm discovers on its own programs for more than 78000 OEIS sequences, sometimes developing unusual programming methods. We analyze its behavior and the invented programs in several experiments. | ['Josef Urban', 'Miroslav Olšák', 'Thibault Gauthier'] | 2023-01-27 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.68381107e-01 4.80903625e-01 -7.37615347e-01 -4.43238825e-01
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d3fe0631-b2b9-40c5-bf2e-44a312160c4d | probabilistic-human-like-gesture-synthesis | null | null | https://openreview.net/forum?id=ykvm7OLh7B | https://openreview.net/pdf?id=ykvm7OLh7B | Probabilistic Human-like Gesture Synthesis from Speech using GRU-based WGAN | Gestures are crucial for increasing the human-likeness of agents and robots to achieve smoother interactions with humans. The realization of an effective system to model human gestures, which are matched with the speech utterances, is necessary to be embedded in these agents. In this work, we propose a GRU-based autoregressive generation model for gesture generation, which is trained with a CNN-based discriminator in an adversarial manner using a WGAN-based learning algorithm. The model is trained to output the rotation angles of the joints in the upper body, and implemented to animate a CG avatar. The motions synthesized by the proposed system are evaluated via an objective measure and a subjective experiment, showing that the proposed model outperforms a baseline model which is trained by a state-of-the-art GAN-based algorithm, using the same dataset. This result reveals that it is essential to develop a stable and robust learning algorithm for training gesture generation models. Our code can be found in https://github.com/wubowen416/gesture-generation. | ['Hiroshi Ishiguro', 'Carlos Ishi', 'Chaoran Liu', 'Bowen Wu'] | 2021-07-05 | null | null | null | acm-icmi-workshop-genea-2021-10 | ['gesture-generation'] | ['robots'] | [ 1.43715948e-01 4.44423705e-01 5.87302446e-02 -1.41493110e-02
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1355d28e-c88d-4721-9f53-6ffa3a012be9 | seqdiffuseq-text-diffusion-with-encoder | 2212.10325 | null | https://arxiv.org/abs/2212.10325v5 | https://arxiv.org/pdf/2212.10325v5.pdf | SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers | Diffusion model, a new generative modelling paradigm, has achieved great success in image, audio, and video generation. However, considering the discrete categorical nature of text, it is not trivial to extend continuous diffusion models to natural language, and text diffusion models are less studied. Sequence-to-sequence text generation is one of the essential natural language processing topics. In this work, we apply diffusion models to approach sequence-to-sequence text generation, and explore whether the superiority generation performance of diffusion model can transfer to natural language domain. We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation. SeqDiffuSeq uses an encoder-decoder Transformers architecture to model denoising function. In order to improve generation quality, SeqDiffuSeq combines the self-conditioning technique and a newly proposed adaptive noise schedule technique. The adaptive noise schedule has the difficulty of denoising evenly distributed across time steps, and considers exclusive noise schedules for tokens at different positional order. Experiment results illustrate the good performance on sequence-to-sequence generation in terms of text quality and inference time. | ['Songfang Huang', 'Fei Huang', 'Chuanqi Tan', 'Zheng Yuan', 'Hongyi Yuan'] | 2022-12-20 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 4.24688518e-01 -1.40714362e-01 1.76164165e-01 -1.28625736e-01
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0904c258-13c9-478d-8bb1-9a17141c6e1f | deep-declarative-dynamic-time-warping-for-end | 2303.10778 | null | https://arxiv.org/abs/2303.10778v1 | https://arxiv.org/pdf/2303.10778v1.pdf | Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths | This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead propose a DTW layer based around bi-level optimisation and deep declarative networks, which we name DecDTW. By formulating DTW as a continuous, inequality constrained optimisation problem, we can compute gradients for the solution of the optimal alignment (with respect to the underlying time series) using implicit differentiation. An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself. This naturally occurs, for instance, when learning to improve the accuracy of predicted alignments against ground truth alignments. We evaluate DecDTW on two such applications, namely the audio-to-score alignment task in music information retrieval and the visual place recognition task in robotics, demonstrating state-of-the-art results in both. | ['Stephen Gould', 'Michael Milford', 'Sourav Garg', 'Ming Xu'] | 2023-03-19 | null | null | null | null | ['visual-place-recognition', 'music-information-retrieval', 'dynamic-time-warping'] | ['computer-vision', 'music', 'time-series'] | [ 4.14032996e-01 1.33651227e-01 -3.14710755e-03 -4.09633189e-01
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6e06a80a-62d2-4ddc-944f-f59d1f8cc1f5 | machine-learning-friendly-biomedical-datasets | 2205.03447 | null | https://arxiv.org/abs/2205.03447v7 | https://arxiv.org/pdf/2205.03447v7.pdf | Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching | Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BioML track at OAEI 2022. | ['Ian Horrocks', 'Ali Hadian', 'Ernesto Jiménez-Ruiz', 'Hang Dong', 'Jiaoyan Chen', 'Yuan He'] | 2022-05-06 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 3.46998960e-01 3.38545382e-01 -4.08334047e-01 -2.26625204e-01
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f2129ffc-3dd3-46d4-af70-a8fa6aec0b77 | baco-a-fast-and-portable-bayesian-compiler | 2212.11142 | null | https://arxiv.org/abs/2212.11142v2 | https://arxiv.org/pdf/2212.11142v2.pdf | BaCO: A Fast and Portable Bayesian Compiler Optimization Framework | We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these parameter types and efficiently deliver high-quality code, BaCO uses Bayesian optimiza tion algorithms specialized towards the autotuning domain. We demonstrate BaCO's effectiveness on three modern compiler systems: TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For these domains, BaCO outperforms current state-of-the-art autotuners by delivering on average 1.36x-1.56x faster code with a tiny search budget, and BaCO is able to reach expert-level performance 2.9x-3.9x faster. | ['Luigi Nardi', 'Kunle Olukotun', 'Michel Steuwer', 'Fredrik Kjolstad', 'Adel Ejjeh', 'Olivia Hsu', 'Rubens Lacouture', 'Johannes Lenfers', 'Artur Souza', 'Erik Hellsten'] | 2022-12-01 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-3.73442441e-01 -4.89966512e-01 -5.05068779e-01 -1.69375181e-01
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9da764f2-b737-45ae-99f7-579eb2106494 | elastic-decision-transformer | 2307.02484 | null | https://arxiv.org/abs/2307.02484v2 | https://arxiv.org/pdf/2307.02484v2.pdf | Elastic Decision Transformer | This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. Videos are available at: https://kristery.github.io/edt/ | ['Masashi Hamaya', 'Xiaolong Wang', 'Yueh-Hua Wu'] | 2023-07-05 | null | null | null | null | ['q-learning', 'atari-games', 'd4rl'] | ['methodology', 'playing-games', 'robots'] | [-3.23819280e-01 -7.55458251e-02 -6.33034766e-01 6.39216974e-02
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a99c487f-4c42-4e33-a075-509db5447747 | multi-task-generative-adversarial-network-for | 1811.10419 | null | http://arxiv.org/abs/1811.10419v1 | http://arxiv.org/pdf/1811.10419v1.pdf | Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data | We propose a new generative adversarial architecture to mitigate imbalance
data problem for the task of medical image semantic segmentation where the
majority of pixels belong to a healthy region and few belong to lesion or
non-health region. A model trained with imbalanced data tends to bias towards
healthy data which is not desired in clinical applications. We design a new
conditional GAN with two components: a generative model and a discriminative
model to mitigate imbalanced data problem through selective weighted loss.
While the generator is trained on sequential magnetic resonance images (MRI) to
learn semantic segmentation and disease classification, the discriminator
classifies whether a generated output is real or fake. The proposed
architecture achieved state-of-the-art results on ACDC-2017 for cardiac
segmentation and diseases classification. We have achieved competitive results
on BraTS-2017 for brain tumor segmentation and brain diseases classification. | ['Mina Rezaei', 'Haojin Yang', 'Christoph Meinel'] | 2018-11-22 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 6.05519176e-01 6.82343304e-01 -1.50922760e-01 -5.15318394e-01
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3d6a5bbb-b953-4b1c-bcd3-da6366c6e2a0 | unsupervised-cd-in-satellite-image-time | 2304.11375 | null | https://arxiv.org/abs/2304.11375v1 | https://arxiv.org/pdf/2304.11375v1.pdf | Unsupervised CD in satellite image time series by contrastive learning and feature tracking | While unsupervised change detection using contrastive learning has been significantly improved the performance of literature techniques, at present, it only focuses on the bi-temporal change detection scenario. Previous state-of-the-art models for image time-series change detection often use features obtained by learning for clustering or training a model from scratch using pseudo labels tailored to each scene. However, these approaches fail to exploit the spatial-temporal information of image time-series or generalize to unseen scenarios. In this work, we propose a two-stage approach to unsupervised change detection in satellite image time-series using contrastive learning with feature tracking. By deriving pseudo labels from pre-trained models and using feature tracking to propagate them among the image time-series, we improve the consistency of our pseudo labels and address the challenges of seasonal changes in long-term remote sensing image time-series. We adopt the self-training algorithm with ConvLSTM on the obtained pseudo labels, where we first use supervised contrastive loss and contrastive random walks to further improve the feature correspondence in space-time. Then a fully connected layer is fine-tuned on the pre-trained multi-temporal features for generating the final change maps. Through comprehensive experiments on two datasets, we demonstrate consistent improvements in accuracy on fitting and inference scenarios. | ['Lorenzo Bruzzone', 'Yuxing Chen'] | 2023-04-22 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.63397229e-01 -7.40958571e-01 1.08335093e-01 -6.38195336e-01
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423bce38-6bd5-4ff7-b35d-8e2b47a17561 | valuenet-a-new-dataset-for-human-value-driven | 2112.06346 | null | https://arxiv.org/abs/2112.06346v1 | https://arxiv.org/pdf/2112.06346v1.pdf | ValueNet: A New Dataset for Human Value Driven Dialogue System | Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research. We further develop a Transformer-based value regression model on ValueNet to learn the utility distribution. Comprehensive empirical results show that the learned value model could benefit a wide range of dialogue tasks. For example, by teaching a generative agent with reinforcement learning and the rewards from the value model, our method attains state-of-the-art performance on the personalized dialog generation dataset: Persona-Chat. With values as additional features, existing emotion recognition models enable capturing rich human emotions in the context, which further improves the empathetic response generation performance in the EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems. The dataset is available at https://liang-qiu.github.io/ValueNet/. | ['Song-Chun Zhu', 'Jianfeng Gao', 'Baolin Peng', 'Pan Lu', 'Jinchao Li', 'Yizhou Zhao', 'Liang Qiu'] | 2021-12-12 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.70041192e-01 6.41011357e-01 -4.12327617e-01 -6.74259007e-01
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822d83ff-e928-43e9-90e6-d8fa96cc9175 | beats-audio-pre-training-with-acoustic | 2212.09058 | null | https://arxiv.org/abs/2212.09058v1 | https://arxiv.org/pdf/2212.09058v1.pdf | BEATs: Audio Pre-Training with Acoustic Tokenizers | The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is widely adopted for other modalities, the state-of-the-art audio SSL models still employ reconstruction loss for pre-training. Compared with reconstruction loss, semantic-rich discrete label prediction encourages the SSL model to abstract the high-level audio semantics and discard the redundant details as in human perception. However, a semantic-rich acoustic tokenizer for general audio pre-training is usually not straightforward to obtain, due to the continuous property of audio and unavailable phoneme sequences like speech. To tackle this challenge, we propose BEATs, an iterative audio pre-training framework to learn Bidirectional Encoder representation from Audio Transformers, where an acoustic tokenizer and an audio SSL model are optimized by iterations. In the first iteration, we use random projection as the acoustic tokenizer to train an audio SSL model in a mask and label prediction manner. Then, we train an acoustic tokenizer for the next iteration by distilling the semantic knowledge from the pre-trained or fine-tuned audio SSL model. The iteration is repeated with the hope of mutual promotion of the acoustic tokenizer and audio SSL model. The experimental results demonstrate our acoustic tokenizers can generate discrete labels with rich audio semantics and our audio SSL models achieve state-of-the-art results across various audio classification benchmarks, even outperforming previous models that use more training data and model parameters significantly. Specifically, we set a new state-of-the-art mAP 50.6% on AudioSet-2M for audio-only models without using any external data, and 98.1% accuracy on ESC-50. The code and pre-trained models are available at https://aka.ms/beats. | ['Furu Wei', 'Zhuo Chen', 'Daniel Tompkins', 'Shujie Liu', 'Chengyi Wang', 'Yu Wu', 'Sanyuan Chen'] | 2022-12-18 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 4.38615233e-01 1.97503284e-01 -1.70514286e-01 -4.64409977e-01
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d3e477a9-b4a3-4147-b622-a4b350cd9f6b | quantum-natural-language-generation-on-near | 2211.00727 | null | https://arxiv.org/abs/2211.00727v1 | https://arxiv.org/pdf/2211.00727v1.pdf | Quantum Natural Language Generation on Near-Term Devices | The emergence of noisy medium-scale quantum devices has led to proof-of-concept applications for quantum computing in various domains. Examples include Natural Language Processing (NLP) where sentence classification experiments have been carried out, as well as procedural generation, where tasks such as geopolitical map creation, and image manipulation have been performed. We explore applications at the intersection of these two areas by designing a hybrid quantum-classical algorithm for sentence generation. Our algorithm is based on the well-known simulated annealing technique for combinatorial optimisation. An implementation is provided and used to demonstrate successful sentence generation on both simulated and real quantum hardware. A variant of our algorithm can also be used for music generation. This paper aims to be self-contained, introducing all the necessary background on NLP and quantum computing along the way. | ['James Wootton', 'Marcel Pfaffhauser', 'Amin Karamlou'] | 2022-11-01 | null | null | null | null | ['music-generation', 'image-manipulation', 'music-generation', 'sentence-classification'] | ['audio', 'computer-vision', 'music', 'natural-language-processing'] | [ 7.61039972e-01 1.70941189e-01 5.31880975e-01 -2.00132921e-01
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c9458c6c-a03d-454b-8591-5eba671f8b9a | irnet-instance-relation-network-for | 1908.06623 | null | https://arxiv.org/abs/1908.06623v1 | https://arxiv.org/pdf/1908.06623v1.pdf | IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation | Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily rely on accurate nuclei detection results and are easily disturbed by miscellaneous objects. In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction. Specifically, we propose the Instance Relation Module to construct the cell association matrix for transferring information among individual cell-instance features. With the collaboration of different instances, the augmented features gain benefits from contextual information and improve semantic consistency. Meanwhile, we proposed a sparsity constrained Duplicate Removal Module to eliminate the misalignment between classification and localization accuracy for candidates selection. The largest cervical Pap smear (CPS) dataset with more than 8000 cell annotations in Pap smear image was constructed for comprehensive evaluation. Our method outperforms other methods by a large margin, demonstrating the effectiveness of exploring instance relation. | ['Pheng-Ann Heng', 'Qi Dou', 'Yanning Zhou', 'Hao Chen', 'Jiaqi Xu'] | 2019-08-19 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 4.02864069e-01 1.69024482e-01 -3.87747973e-01 -2.28972033e-01
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e6e25552-9288-493d-8943-829d22504b00 | 190600781 | 1906.00781 | null | https://arxiv.org/abs/1906.00781v1 | https://arxiv.org/pdf/1906.00781v1.pdf | Learning Semantic Annotations for Tabular Data | The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another. | ['Ernesto Jimenez-Ruiz', 'Jiaoyan Chen', 'Charles Sutton', 'Ian Horrocks'] | 2019-05-30 | null | null | null | null | ['type-prediction', 'table-annotation', 'table-annotation', 'column-type-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-2.59198070e-01 2.16264516e-01 -8.64710689e-01 -7.29635775e-01
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7ba21dfa-e37e-488f-9165-65d0624efa9b | deep-learning-based-face-super-resolution-a | 2101.03749 | null | https://arxiv.org/abs/2101.03749v2 | https://arxiv.org/pdf/2101.03749v2.pdf | Deep Learning-based Face Super-Resolution: A Survey | Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, then present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field. A curated list of papers and resources to face super-resolution are available at \url{https://github.com/junjun-jiang/Face-Hallucination-Benchmark} | ['Jiayi Ma', 'Xianming Liu', 'Chenyang Wang', 'Junjun Jiang'] | 2021-01-11 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.08047554e-01 -6.02321252e-02 -2.32600391e-01 -4.49138552e-01
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1537398f-7186-4ea8-8ccd-ea5df3170f6f | a-linguistic-analysis-of-visually-grounded | 2010.03127 | null | https://arxiv.org/abs/2010.03127v1 | https://arxiv.org/pdf/2010.03127v1.pdf | A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions | Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make two design choices: first, we focus on OneCommon Corpus \citep{udagawa2019natural,udagawa2020annotated}, a simple yet challenging common grounding dataset which contains minimal bias by design. Second, we analyze their linguistic structures based on \textit{spatial expressions} and provide comprehensive and reliable annotation for 600 dialogues. We show that our annotation captures important linguistic structures including predicate-argument structure, modification and ellipsis. In our experiments, we assess the model's understanding of these structures through reference resolution. We demonstrate that our annotation can reveal both the strengths and weaknesses of baseline models in essential levels of detail. Overall, we propose a novel framework and resource for investigating fine-grained language understanding in visually grounded dialogues. | ['Akiko Aizawa', 'Takato Yamazaki', 'Takuma Udagawa'] | 2020-10-07 | null | https://aclanthology.org/2020.findings-emnlp.67 | https://aclanthology.org/2020.findings-emnlp.67.pdf | findings-of-the-association-for-computational | ['spatial-relation-recognition', 'natural-language-visual-grounding'] | ['computer-vision', 'reasoning'] | [ 2.59562545e-02 6.54555023e-01 -1.15945026e-01 -3.67115557e-01
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c2050831-72b9-4c26-b2cc-017c03706e23 | dynamic-prediction-of-icu-mortality-risk | 1912.10080 | null | https://arxiv.org/abs/1912.10080v1 | https://arxiv.org/pdf/1912.10080v1.pdf | Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation | Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning. | ['Tiago Alves', 'Nivio Ziviani', 'Alberto Laender', 'Adriano Veloso'] | 2019-12-20 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 2.43344128e-01 -3.41786683e-01 -1.91701964e-01 -2.50026613e-01
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ff0e01be-40c2-4a6a-a532-15f3022b1903 | predicting-motion-plans-for-articulating | 2303.01484 | null | https://arxiv.org/abs/2303.01484v1 | https://arxiv.org/pdf/2303.01484v1.pdf | Predicting Motion Plans for Articulating Everyday Objects | Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce SeqIK+$\theta_0$, a fast and flexible representation for motion plans. Finally, we learn models that use SeqIK+$\theta_0$ to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods. | ['Saurabh Gupta', 'Max E. Shepherd', 'Arjun Gupta'] | 2023-03-02 | null | null | null | null | ['motion-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [ 1.01465881e-01 4.53661196e-02 -1.48016825e-01 9.02476162e-02
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20ef3c44-c14c-4efc-9c6b-07bb57fba6ef | snps-filtered-by-allele-frequency-improve-the | 2111.10471 | null | https://arxiv.org/abs/2111.10471v1 | https://arxiv.org/pdf/2111.10471v1.pdf | SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes | Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we studied 911 African Americans and 1,171 European Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) cohort. We built hypertension subtype classification models using both environmental variables and sets of genetic features selected based on different criteria. The fitted prediction models provided insights into the genetic landscape of hypertension subtypes, which may aid personalized diagnosis and treatment of hypertension in the future. | ['Yuan Luo', 'Ryan Irvin', 'Donna Arnett', 'Sanjiv J. Shah', 'Yiming Li'] | 2021-11-19 | null | null | null | null | ['epidemiology'] | ['medical'] | [-3.19116414e-02 -2.67362535e-01 -3.48561585e-01 -1.00124526e+00
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31e8446f-4c38-46ed-a9a8-a2f008658a70 | learning-from-peers-transfer-reinforcement | 2109.07999 | null | https://arxiv.org/abs/2109.07999v4 | https://arxiv.org/pdf/2109.07999v4.pdf | Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing | Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing.The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents' knowledge to improve their performance on current tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL-based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL. | ['Vincent Poor', 'Melike Erol-Kantarci', 'Hao Zhou'] | 2021-09-16 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-5.70159316e-01 -4.00295928e-02 -8.07943761e-01 -1.57377064e-01
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89be23c2-2a0c-40bc-815b-a7c255185052 | video-background-music-generation-dataset | 2211.11248 | null | https://arxiv.org/abs/2211.11248v1 | https://arxiv.org/pdf/2211.11248v1.pdf | Video Background Music Generation: Dataset, Method and Evaluation | Music is essential when editing videos, but selecting music manually is difficult and time-consuming. Thus, we seek to automatically generate background music tracks given video input. This is a challenging task since it requires plenty of paired videos and music to learn their correspondence. Unfortunately, there exist no such datasets. To close this gap, we introduce a dataset, benchmark model, and evaluation metric for video background music generation. We introduce SymMV, a video and symbolic music dataset, along with chord, rhythm, melody, and accompaniment annotations. To the best of our knowledge, it is the first video-music dataset with high-quality symbolic music and detailed annotations. We also propose a benchmark video background music generation framework named V-MusProd, which utilizes music priors of chords, melody, and accompaniment along with video-music relations of semantic, color, and motion features. To address the lack of objective metrics for video-music correspondence, we propose a retrieval-based metric VMCP built upon a powerful video-music representation learning model. Experiments show that with our dataset, V-MusProd outperforms the state-of-the-art method in both music quality and correspondence with videos. We believe our dataset, benchmark model, and evaluation metric will boost the development of video background music generation. | ['Si Liu', 'Xiaobo Li', 'Miao Lu', 'Chenxi Bao', 'Stanley Peng', 'Yue Liao', 'Baisen Wang', 'Zhaokai Wang', 'Le Zhuo'] | 2022-11-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.47843468e-01 -7.28489161e-01 -2.80556321e-01 8.37656260e-02
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04e7c543-3888-49b8-8d22-248ec0d06e4e | urban-traffic-prediction-from-spatio-temporal | null | null | https://www.kdd.org/kdd2019/accepted-papers/view/urban-traffic-prediction-from-spatio-temporal-data-using-deep-meta-learning | https://dl.acm.org/doi/pdf/10.1145/3292500.3330884?download=true | Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning | Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e.g., points of interests and road networks. To tackle these challenges, we proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once. ST-MetaNet employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step. In specific, the encoder and decoder have the same network structure, consisting of a recurrent neural network to encode the traffic, a meta graph attention network to capture diverse spatial correlations, and a meta recurrent neural network to consider diverse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of ST-MetaNet beyond several state-of-the-art methods. | ['Yong Yu', 'Zheyi Pan', 'Yu Zheng', 'Weifeng Wang', 'Yuxuan Liang', 'Junbo Zhang'] | 2019-07-25 | null | null | null | kdd-19-2019-7 | ['spatio-temporal-forecasting'] | ['time-series'] | [-8.93012136e-02 -2.44326755e-01 -2.60379702e-01 -3.25352520e-01
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225f80a2-d3f1-4aec-aa87-5065f4c438af | svde-scalable-value-decomposition-exploration | 2303.09058 | null | https://arxiv.org/abs/2303.09058v1 | https://arxiv.org/pdf/2303.09058v1.pdf | SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning | Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous sample consumption for training and lack of active exploration. In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. The scalable training mechanism asynchronously decouples strategy learning with environmental interaction, so as to accelerate sample generation in a MapReduce manner. For the problem of lack of exploration, an intrinsic reward design and explorative experience replay are proposed, so as to enhance exploration to produce diverse samples and filter non-novel samples, respectively. Empirically, our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games. A data-efficiency experiment also shows the acceleration of SVDE for sample collection and policy convergence, and we demonstrate the effectiveness of factors in SVDE through a set of ablation experiments. | ['Xuan Wang', 'Jing Xiao', 'Jiajia Zhang', 'Qiang Wang', 'Shuhao Zhang', 'Shuhan Qi'] | 2023-03-16 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-4.52466756e-01 -2.54765123e-01 -2.85472929e-01 1.60522133e-01
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a6d5c644-457b-40ba-8b44-842710871618 | learning-to-discriminate-information-for | 1912.04461 | null | https://arxiv.org/abs/1912.04461v3 | https://arxiv.org/pdf/1912.04461v3.pdf | Learning to Discriminate Information for Online Action Detection | From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies. | ['Chanho Jung', 'Jinyoung Moon', 'Hyunjun Eun', 'Changick Kim', 'Jongyoul Park'] | 2019-12-10 | learning-to-discriminate-information-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['online-action-detection'] | ['computer-vision'] | [ 8.77192974e-01 -2.54845828e-01 -5.79253137e-01 -1.48474686e-02
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63f25a79-49eb-42ab-9204-4c9850ac1c24 | class-specific-semantic-reconstruction-for | 2207.02158 | null | https://arxiv.org/abs/2207.02158v1 | https://arxiv.org/pdf/2207.02158v1.pdf | Class-Specific Semantic Reconstruction for Open Set Recognition | Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods basing on auto-encoder (AE) and prototype learning show great potential in handling this challenging task. In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning. Specifically, CSSR replaces prototype points with manifolds represented by class-specific AEs. Unlike conventional prototype-based methods, CSSR models each known class on an individual AE manifold, and measures class belongingness through AE's reconstruction error. Class-specific AEs are plugged into the top of the DNN backbone and reconstruct the semantic representations learned by the DNN instead of the raw image. Through end-to-end learning, the DNN and the AEs boost each other to learn both discriminative and representative information. The results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition and is sufficiently simple and flexible to incorporate into existing frameworks. | ['Ming-Ming Cheng', 'QinGhua Hu', 'Yu Wang', 'Hongzhi Huang'] | 2022-07-05 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.92870617e-01 -8.15081373e-02 -1.15873225e-01 -5.80694199e-01
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9797abaa-a562-4d40-ac4b-435938fb674e | retinal-image-restoration-using-transformer | 2303.01939 | null | https://arxiv.org/abs/2303.01939v1 | https://arxiv.org/pdf/2303.01939v1.pdf | Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network | Medical imaging plays a significant role in detecting and treating various diseases. However, these images often happen to be of too poor quality, leading to decreased efficiency, extra expenses, and even incorrect diagnoses. Therefore, we propose a retinal image enhancement method using a vision transformer and convolutional neural network. It builds a cycle-consistent generative adversarial network that relies on unpaired datasets. It consists of two generators that translate images from one domain to another (e.g., low- to high-quality and vice versa), playing an adversarial game with two discriminators. Generators produce indistinguishable images for discriminators that predict the original images from generated ones. Generators are a combination of vision transformer (ViT) encoder and convolutional neural network (CNN) decoder. Discriminators include traditional CNN encoders. The resulting improved images have been tested quantitatively using such evaluation metrics as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and qualitatively, i.e., vessel segmentation. The proposed method successfully reduces the adverse effects of blurring, noise, illumination disturbances, and color distortions while significantly preserving structural and color information. Experimental results show the superiority of the proposed method. Our testing PSNR is 31.138 dB for the first and 27.798 dB for the second dataset. Testing SSIM is 0.919 and 0.904, respectively. | ['Md Baharul Islam', 'Alnur Alimanov'] | 2023-03-03 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.18105626e-01 -1.07797217e-02 4.42894429e-01 -5.74259683e-02
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928b45c0-c4cc-4002-985e-af9257e78ebf | scida-self-correction-integrated-domain | 2108.06810 | null | https://arxiv.org/abs/2108.06810v2 | https://arxiv.org/pdf/2108.06810v2.pdf | SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images | Most publicly available datasets for image classification are with single labels, while images are inherently multi-labeled in our daily life. Such an annotation gap makes many pre-trained single-label classification models fail in practical scenarios. This annotation issue is more concerned for aerial images: Aerial data collected from sensors naturally cover a relatively large land area with multiple labels, while annotated aerial datasets, which are publicly available (e.g., UCM, AID), are single-labeled. As manually annotating multi-label aerial images would be time/labor-consuming, we propose a novel self-correction integrated domain adaptation (SCIDA) method for automatic multi-label learning. SCIDA is weakly supervised, i.e., automatically learning the multi-label image classification model from using massive, publicly available single-label images. To achieve this goal, we propose a novel Label-Wise self-Correction (LWC) module to better explore underlying label correlations. This module also makes the unsupervised domain adaptation (UDA) from single- to multi-label data possible. For model training, the proposed model only uses single-label information yet requires no prior knowledge of multi-labeled data; and it predicts labels for multi-label aerial images. In our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, the proposed model is tested directly on our collected Multi-scene Aerial Image (MAI) dataset. | ['Z. Jane Wang', 'Xiaoxiang Zhu', 'Yuansheng Hua', 'Lichao Mou', 'Jianzhe Lin', 'Tianze Yu'] | 2021-08-15 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.42724454e-01 -3.23742628e-01 -5.40938735e-01 -5.43928266e-01
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56dfef47-5d1d-4f8c-9e00-b3abdb0bfd72 | coherent-multi-sentence-video-description | 1403.6173 | null | http://arxiv.org/abs/1403.6173v1 | http://arxiv.org/pdf/1403.6173v1.pdf | Coherent Multi-Sentence Video Description with Variable Level of Detail | Humans can easily describe what they see in a coherent way and at varying
level of detail. However, existing approaches for automatic video description
are mainly focused on single sentence generation and produce descriptions at a
fixed level of detail. In this paper, we address both of these limitations: for
a variable level of detail we produce coherent multi-sentence descriptions of
complex videos. We follow a two-step approach where we first learn to predict a
semantic representation (SR) from video and then generate natural language
descriptions from the SR. To produce consistent multi-sentence descriptions, we
model across-sentence consistency at the level of the SR by enforcing a
consistent topic. We also contribute both to the visual recognition of objects
proposing a hand-centric approach as well as to the robust generation of
sentences using a word lattice. Human judges rate our multi-sentence
descriptions as more readable, correct, and relevant than related work. To
understand the difference between more detailed and shorter descriptions, we
collect and analyze a video description corpus of three levels of detail. | ['Marcus Rohrbach', 'Anna Senina', 'Wei Qiu', 'Manfred Pinkal', 'Annemarie Friedrich', 'Mykhaylo Andriluka', 'Sikandar Amin', 'Bernt Schiele'] | 2014-03-24 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.75401199e-01 3.56351659e-02 -1.41701117e-01 -7.31321275e-01
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50440894-ee4e-4a1e-b847-7654aa40baaa | disguisenet-a-contrastive-approach-for | 1804.09669 | null | http://arxiv.org/abs/1804.09669v2 | http://arxiv.org/pdf/1804.09669v2.pdf | DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild | This paper describes our approach for the Disguised Faces in the Wild (DFW)
2018 challenge. The task here is to verify the identity of a person among
disguised and impostors images. Given the importance of the task of face
verification it is essential to compare methods across a common platform. Our
approach is based on VGG-face architecture paired with Contrastive loss based
on cosine distance metric. For augmenting the data set, we source more data
from the internet. The experiments show the effectiveness of the approach on
the DFW data. We show that adding extra data to the DFW dataset with noisy
labels also helps in increasing the generalization performance of the network.
The proposed network achieves 27.13% absolute increase in accuracy over the DFW
baseline. | ['Abhinav Dhall', 'Skand Vishwanath Peri'] | 2018-04-25 | null | null | null | null | ['disguised-face-verification'] | ['computer-vision'] | [-1.08422125e-02 6.29936531e-02 9.90887210e-02 -6.49112880e-01
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1997f575-3485-46db-8264-20478d1eb033 | robust-automatic-whole-brain-extraction-on | 2006.02627 | null | https://arxiv.org/abs/2006.02627v1 | https://arxiv.org/pdf/2006.02627v1.pdf | Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet | Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skull striping is a preliminary step in presurgical planning, cortical reconstruction, and automatic tumor segmentation. Despite a plethora of skull stripping approaches in the literature, few are sufficiently accurate for processing pathology-presenting MRIs, especially MRIs with brain tumors. In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with gadolinium contrast (T1Gd) and T2-weighted fluid attenuated inversion recovery (FLAIR) in patients with brain tumors. We automatically created gray matter, white matter, and CSF probability masks using SPM12 software and merged the masks into one for a final whole-brain mask for model training. Dice agreement, sensitivity, and specificity of the model (referred herein as DeepBrain) was tested against manual brain masks. To assess data efficiency, we retrained our models using progressively fewer training data examples and calculated average dice scores on the test set for the models trained in each round. Further, we tested our model against MRI of healthy brains from the LBP40A dataset. Overall, DeepBrain yielded an average dice score of 94.5%, sensitivity of 96.4%, and specificity of 98.5% on brain tumor data. For healthy brains, model performance improved to a dice score of 96.2%, sensitivity of 96.6% and specificity of 99.2%. The data efficiency experiment showed that, for this specific task, comparable levels of accuracy could have been achieved with as few as 50 training samples. In conclusion, this study demonstrated that a deep learning model trained on minimally processed automatically-generated labels can generate more accurate brain masks on MRI of brain tumor patients within seconds. | ['Kristin R. Swanson', 'J. Ross Mitchell', 'Leland S. Hu', 'Kyle W. Singleton', 'Sara Ranjbar', 'Lisa E. Paulson', 'Lee Curtin', 'Cassandra R. Rickertsen'] | 2020-06-04 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 2.57497340e-01 4.72400993e-01 2.90065825e-01 -5.04497528e-01
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c4b16286-b0f3-49ff-9190-d9214cb5afda | seeknet-improved-human-instance-segmentation | 2011.08682 | null | https://arxiv.org/abs/2011.08682v2 | https://arxiv.org/pdf/2011.08682v2.pdf | SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation | Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic obstacles. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded pedestrians. We also demonstrate the benefits of our algorithm on occluded human detection and tracking over other baselines. Additionally, we set up a multi-robot environment with SeekNet to identify and track visual disease markers for airborne disease in crowded areas. We conduct our experiments in a simulated indoor environment and show that our method enhances the overall accuracy of the amodal recognition task and achieves the largest improvement in detection accuracy over time in comparison to the baseline approaches. | ['Aniket Bera', 'Phu Pham', 'Rama Prashanth RV', 'Bala Murali Manoghar', 'Venkatraman Narayanan'] | 2020-11-17 | null | null | null | null | ['human-instance-segmentation'] | ['computer-vision'] | [ 1.71323642e-01 -1.12729356e-01 1.07706457e-01 -1.67537704e-02
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ee9f36e6-3694-4c72-9c3e-c3328a1a8f2c | machine-learning-aided-anonymization-of | 1902.08934 | null | https://arxiv.org/abs/1902.08934v2 | https://arxiv.org/pdf/1902.08934v2.pdf | Privacy Preserving Location Data Publishing: A Machine Learning Approach | Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose. A variation of $k$-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework. | ['Sina Shaham', 'Zihuai Lin', 'Jun Li', 'Ming Ding', 'Bo Liu', 'Shuping Dang'] | 2019-02-24 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.70252664e-03 -8.16864595e-02 -1.94697991e-01 -4.34800208e-01
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819629ed-60cd-482c-a6fb-abeb97bfaf1d | sleepppg-net-a-deep-learning-algorithm-for | 2202.05735 | null | https://arxiv.org/abs/2202.05735v4 | https://arxiv.org/pdf/2202.05735v4.pdf | SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography | Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). Methods: We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG time series. SleepPPG-Net was trained end-to-end and consists of a residual convolutional network for automatic feature extraction and a temporal convolutional network to capture long-range contextual information. We benchmarked the performance of SleepPPG-Net against models based on the best-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked on a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ($\kappa$) score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good generalization performance to an external database, obtaining a $\kappa$ score of 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net provides new SOTA performance. In addition, performance is high enough to open the path to the development of wearables that meet the requirements for usage in clinical applications such as the diagnosis and monitoring of obstructive sleep apnea. | ['Joachim A. Behar', 'Lea Amar', 'Amir Landesberg', 'Sharon Salabi', 'Peter H. Charlton', 'Kevin Kotzen'] | 2022-02-11 | null | null | null | null | ['photoplethysmography-ppg', 'sleep-staging'] | ['medical', 'medical'] | [ 3.48539394e-03 -1.72441476e-03 -3.05434763e-01 -6.34455264e-01
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20e09c55-def2-48a8-accb-3e9968cee543 | morpheme-boundary-detection-grammatical | 2112.09860 | null | https://arxiv.org/abs/2112.09860v1 | https://arxiv.org/pdf/2112.09860v1.pdf | Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model | Developing Natural Language Processing resources for a low resource language is a challenging but essential task. In this paper, we present a Morphological Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to perform morpheme boundary detection and grammatical feature tagging. We have created a data set of Gujarati words with lemma and grammatical features. The Bi-LSTM based model of Morph Analyzer discussed in the paper handles the language morphology effectively without the knowledge of any hand-crafted suffix rules. To the best of our knowledge, this is the first dataset and morph analyzer model for the Gujarati language which performs both grammatical feature tagging and morpheme boundary detection tasks. | ['Dr. Brijesh Bhatt', 'Jatayu Baxi'] | 2021-12-18 | null | https://aclanthology.org/2021.icon-main.45 | https://aclanthology.org/2021.icon-main.45.pdf | icon-2021-12 | ['boundary-detection'] | ['computer-vision'] | [-2.56276806e-04 1.30604282e-01 3.20516616e-01 -3.08799088e-01
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5d284459-371f-4512-9472-cbd5de34c6d8 | deconfounded-and-explainable-interactive | null | null | https://dl.acm.org/doi/abs/10.1145/3474085.3475366?casa_token=vPQdimvIM_0AAAAA:v79z37V9qYI2Z8QpWndZfLCpcbC6Z2j4LRpmDU8OEdqSET23vzR1gbkr7TGMbgjMGsErTzJU7J-uqrA | https://dl.acm.org/doi/pdf/10.1145/3474085.3475366?casa_token=QelAAj4QhBoAAAAA:l4fJpTytXhwrUZjpHeVzPpk6425a5rs6Sl9Lwys0dw5u9WrMuV_gMd7stznvlP_Ow2FOE79VuXyCYxI | Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes | In vision-language retrieval systems, users provide natural language feedback to find target images. Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval. However, developing explainable vision-language retrieval systems can be challenging, due to limited labeled multimodal data. In the retrieval of complex scenes, the issue of limited labeled data can be more severe. With multiple objects in the complex scenes, each user query may not exhaustively describe all objects in the desired image and thus more labeled queries are needed. The issue of limited labeled data can cause data selection biases, and result in spurious correlations learned by the models. When learning spurious correlations, existing explainable models may not be able to accurately extract regions from images and keywords from user queries. In this paper, we discover that deconfounded learning is an important step to provide better vision-language explanations. Thus we propose a deconfounded explainable vision-language retrieval system. By introducing deconfounded learning to pretrain our vision-language model, the spurious correlations in the model can be reduced through interventions by potential confounders. This helps to train more accurate representations and further enable better explainability. Based on explainable retrieval results, we propose novel interactive mechanisms. In such interactions, users can better understand why the system returns particular results and give feedback effectively improving the results. This additional feedback is sample efficient and thus alleviates the data limitation problem. Through extensive experiments, our system achieves about 60% improvements, compared to the state-of-the-art. | ['Shuai Li', 'Tong Yu', 'Junda Wu'] | 2021-10-17 | null | null | null | the-29th-acm-international-conference-on | ['explainable-models'] | ['computer-vision'] | [-7.18697235e-02 7.79902115e-02 -5.81313431e-01 -4.87246364e-01
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3249d170-f2ec-424d-bd80-a3c7903f3376 | semeval-2021-task-5-toxic-spans-detection | null | null | https://aclanthology.org/2021.semeval-1.6 | https://aclanthology.org/2021.semeval-1.6.pdf | SemEval-2021 Task 5: Toxic Spans Detection | The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gold toxic spans provided by the organisers. It could also be treated as rationale extraction, using classifiers trained on potentially larger external datasets of posts manually annotated as toxic or not, without toxic span annotations. For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans. Participants submitted their predicted spans for a held-out test set and were scored using character-based F1. This overview summarises the work of the 36 teams that provided system descriptions. | ['Ion Androutsopoulos', "L{\\'e}o Laugier", 'Jeffrey Sorensen', 'John Pavlopoulos'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 6.48985863e-01 6.36841416e-01 -3.02907936e-02 -2.41013557e-01
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b0ecbd6d-b3d4-4e4e-96dc-4041666eb617 | m2pht-mixed-models-with-preferences-and | 2004.01646 | null | https://arxiv.org/abs/2004.01646v4 | https://arxiv.org/pdf/2004.01646v4.pdf | M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation | Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation. | ['Srinivasan Parthasarathy', 'Bo Peng', 'Zhiyun Ren', 'Xia Ning'] | 2020-04-03 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-9.81398299e-02 -5.33600748e-01 -8.75675976e-01 -3.43346626e-01
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c95bac33-88b6-4af7-968a-9fdf6eec5447 | nne-a-dataset-for-nested-named-entity | 1906.01359 | null | https://arxiv.org/abs/1906.01359v1 | https://arxiv.org/pdf/1906.01359v1.pdf | NNE: A Dataset for Nested Named Entity Recognition in English Newswire | Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE---a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER. | ['Ben Hachey', 'Nicky Ringland', 'James R. Curran', 'Xiang Dai', 'Sarvnaz Karimi', 'Cecile Paris'] | 2019-06-04 | null | https://aclanthology.org/P19-1510 | https://aclanthology.org/P19-1510.pdf | acl-2019-7 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.91490328e-01 4.82307196e-01 -4.24796283e-01 -7.82576799e-01
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1216d0e3-447f-4bcd-944f-5d265664ea0b | holoface-augmenting-human-to-human | 1802.00278 | null | http://arxiv.org/abs/1802.00278v1 | http://arxiv.org/pdf/1802.00278v1.pdf | HoloFace: Augmenting Human-to-Human Interactions on HoloLens | We present HoloFace, an open-source framework for face alignment, head pose
estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace
implements two state-of-the-art face alignment methods which can be used
interchangeably: one running locally and one running on a remote backend. Head
pose estimation is accomplished by fitting a deformable 3D model to the
landmarks localized using face alignment. The head pose provides both the
rotation of the head and a position in the world space. The parameters of the
fitted 3D face model provide estimates of facial attributes such as mouth
opening or smile. Together the above information can be used to augment the
faces of people seen by the HoloLens user, and thus their interaction.
Potential usage scenarios include facial recognition, emotion recognition, eye
gaze tracking and many others. We demonstrate the capabilities of our framework
by augmenting the faces of people seen through the HoloLens with various
objects and animations. | ['Zbigniew Nasarzewski', 'Piotr Garbat', 'Marek Kowalski', 'Grzegorz Galinski'] | 2018-02-01 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.65664750e-01 1.91380620e-01 1.66074932e-01 -7.27514744e-01
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112fcb0b-5956-42bb-9a83-c0e361841699 | word-embedding-based-antonym-detection-using | null | null | https://aclanthology.info/papers/N15-1100/n15-1100 | https://www.aclweb.org/anthology/N15-1100 | Word Embedding-based Antonym Detection using Thesauri and Distributional Information | null | ['Makoto Miwa', 'Yutaka Sasaki', 'Masataka Ono'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['learning-word-embeddings'] | ['methodology'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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c8212dfb-9a15-40d7-97a0-9ba0e855535d | frozen-clip-models-are-efficient-video | 2208.03550 | null | https://arxiv.org/abs/2208.03550v1 | https://arxiv.org/pdf/2208.03550v1.pdf | Frozen CLIP Models are Efficient Video Learners | Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos. This enables the video network to benefit from the pretrained image model. However, this requires substantial computation and memory resources for finetuning on videos and the alternative of directly using pretrained image features without finetuning the image backbone leads to subpar results. Fortunately, recent advances in Contrastive Vision-Language Pre-training (CLIP) pave the way for a new route for visual recognition tasks. Pretrained on large open-vocabulary image-text pair data, these models learn powerful visual representations with rich semantics. In this paper, we present Efficient Video Learning (EVL) -- an efficient framework for directly training high-quality video recognition models with frozen CLIP features. Specifically, we employ a lightweight Transformer decoder and learn a query token to dynamically collect frame-level spatial features from the CLIP image encoder. Furthermore, we adopt a local temporal module in each decoder layer to discover temporal clues from adjacent frames and their attention maps. We show that despite being efficient to train with a frozen backbone, our models learn high quality video representations on a variety of video recognition datasets. Code is available at https://github.com/OpenGVLab/efficient-video-recognition. | ['Hongsheng Li', 'Yu Qiao', 'Jifeng Dai', 'Xiaogang Wang', 'Gerard de Melo', 'Peng Gao', 'Renrui Zhang', 'Shijie Geng', 'Ziyi Lin'] | 2022-08-06 | null | null | null | null | ['video-recognition', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 3.18588108e-01 -1.86344266e-01 -4.33640838e-01 -4.82670963e-01
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d545c706-4ec5-4509-ae27-abc57cdcfed6 | multi-band-wi-fi-sensing-with-matched-feature | 2112.14006 | null | https://arxiv.org/abs/2112.14006v2 | https://arxiv.org/pdf/2112.14006v2.pdf | Multi-Band Wi-Fi Sensing with Matched Feature Granularity | Complementary to the fine-grained channel state information (CSI) from the physical layer and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes (e.g., beam SNR) that are available at millimeter-wave (mmWave) bands during the mandatory beam training phase can be repurposed for Wi-Fi sensing applications. In this paper, we propose a multi-band Wi-Fi fusion method for Wi-Fi sensing that hierarchically fuses the features from both the fine-grained CSI at sub-6 GHz and the mid-grained beam SNR at 60 GHz in a granularity matching framework. The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights. To further address the issue of limited labeled training data, we propose an autoencoder-based multi-band Wi-Fi fusion network that can be pre-trained in an unsupervised fashion. Once the autoencoder-based fusion network is pre-trained, we detach the decoders and append multi-task sensing heads to the fused feature map by fine-tuning the fusion block and re-training the multi-task heads from the scratch. The multi-band Wi-Fi fusion framework is thoroughly validated by in-house experimental Wi-Fi sensing datasets spanning three tasks: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to four baseline methods (i.e., CSI-only, beam SNR-only, input fusion, and feature fusion) demonstrates the granularity matching improves the multi-task sensing performance. Quantitative performance is evaluated as a function of the number of labeled training data, latent space dimension, and fine-tuning learning rates. | ['R. Michael Buehrer', 'Philip V. Orlik', 'Ye Wang', 'Toshiaki Koike-Akino', 'Wang', 'Pu', 'Jianyuan Yu'] | 2021-12-28 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 4.94534165e-01 -4.47531268e-02 -9.24065933e-02 -5.10311365e-01
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44e74733-a37c-4c88-97e2-d72e555710b0 | fully-non-homogeneous-atmospheric-scattering | 2108.11292 | null | https://arxiv.org/abs/2108.11292v1 | https://arxiv.org/pdf/2108.11292v1.pdf | Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing | In recent years, single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results. However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and the angular scattering coefficient (ASC) are assumed as constants for one image. Obviously, the hazy images taken in real world cannot always satisfy this assumption. Such generating modelling mismatch between the real-world images and ASM sets up the upper bound of trained ASM-based SIDM for dehazing. Bearing this in mind, in this study, a new fully non-homogeneous atmospheric scattering model (FNH-ASM) is proposed for well modeling the hazy images under complex conditions where ALF and ASC are pixel dependent. However, FNH-ASM brings difficulty in practical application. In FNH-ASM based SIDM, the estimation bias of parameters at different positions lead to different distortion of dehazing result. Hence, in order to reduce the influence of parameter estimation bias on dehazing results, two new cost sensitive loss functions, beta-Loss and D-Loss, are innovatively developed for limiting the parameter bias of sensitive positions that have a greater impact on the dehazing result. In the end, based on FNH-ASM, an end-to-end CNN-based dehazing network, FNHD-Net, is developed, which applies beta-Loss and D-Loss. Experimental results demonstrate the effectiveness and superiority of our proposed FNHD-Net for dehazing on both synthetic and real-world images. And the performance improvement of our method increases more obviously in dense and heterogeneous haze scenes. | ['Yong Xu', 'Yuexian Zou', 'Yan Huang', 'Cong Wang'] | 2021-08-25 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.35844880e-01 -3.82548183e-01 6.91272855e-01 -3.20167571e-01
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f0110b13-2d31-4824-a3e0-e84ccfe4db5c | utilizing-relative-event-time-to-enhance | null | null | https://aclanthology.org/2021.emnlp-main.815 | https://aclanthology.org/2021.emnlp-main.815.pdf | Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction | Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt the Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance. | ['Heng Ji', 'Haoyang Wen'] | null | null | null | null | emnlp-2021-11 | ['temporal-relation-extraction', 'temporal-relation-classification', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.02797258e-01 2.44374350e-01 -7.28035271e-01 -6.05995059e-01
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ed54d3d9-9da7-4924-b4f7-91967da2b032 | a-deep-generative-model-for-graph-layout | 1904.12225 | null | https://arxiv.org/abs/1904.12225v7 | https://arxiv.org/pdf/1904.12225v7.pdf | A Deep Generative Model for Graph Layout | Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics. | ['Kwan-Liu Ma', 'Oh-Hyun Kwon'] | 2019-04-27 | null | null | null | null | ['layout-design', '3d-depth-estimation'] | ['computer-vision', 'computer-vision'] | [-1.47761386e-02 1.56331748e-01 2.10635886e-01 -4.85121906e-01
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17abbd07-6217-43fb-af9a-36660636eac6 | exploring-the-limits-of-out-of-distribution | 2106.03004 | null | https://arxiv.org/abs/2106.03004v3 | https://arxiv.org/pdf/2106.03004v3.pdf | Exploring the Limits of Out-of-Distribution Detection | Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision Transformers pre-trained on ImageNet-21k. On a challenging genomics OOD detection benchmark, we improve the AUROC from 66% to 77% using transformers and unsupervised pre-training. To further improve performance, we explore the few-shot outlier exposure setting where a few examples from outlier classes may be available; we show that pre-trained transformers are particularly well-suited for outlier exposure, and that the AUROC of OOD detection on CIFAR-100 vs CIFAR-10 can be improved to 98.7% with just 1 image per OOD class, and 99.46% with 10 images per OOD class. For multi-modal image-text pre-trained transformers such as CLIP, we explore a new way of using just the names of outlier classes as a sole source of information without any accompanying images, and show that this outperforms previous SOTA on standard vision OOD benchmark tasks. | ['Balaji Lakshminarayanan', 'Jie Ren', 'Stanislav Fort'] | 2021-06-06 | null | http://proceedings.neurips.cc/paper/2021/hash/3941c4358616274ac2436eacf67fae05-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3941c4358616274ac2436eacf67fae05-Paper.pdf | neurips-2021-12 | ['unsupervised-pre-training'] | ['methodology'] | [ 1.25285566e-01 4.71216887e-02 3.31042707e-01 -2.16643035e-01
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e053ca3a-1177-40f8-88c6-1c393d4b7ff8 | a-tiered-move-making-algorithm-for-general | 1403.6275 | null | http://arxiv.org/abs/1403.6275v1 | http://arxiv.org/pdf/1403.6275v1.pdf | A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies | A large number of problems in computer vision can be modelled as energy
minimization problems in a Markov Random Field (MRF) or Conditional Random
Field (CRF) framework. Graph-cuts based $\alpha$-expansion is a standard
move-making method to minimize the energy functions with sub-modular pairwise
terms. However, certain problems require more complex pairwise terms where the
$\alpha$-expansion method is generally not applicable.
In this paper, we propose an iterative {\em tiered move making algorithm}
which is able to handle general pairwise terms. Each move to the next
configuration is based on the current labeling and an optimal tiered move,
where each tiered move requires one application of the dynamic programming
based tiered labeling method introduced in Felzenszwalb et. al.
\cite{tiered_cvpr_felzenszwalbV10}. The algorithm converges to a local minimum
for any general pairwise potential, and we give a theoretical analysis of the
properties of the algorithm, characterizing the situations in which we can
expect good performance. We first evaluate our method on an object-class
segmentation problem using the Pascal VOC-11 segmentation dataset where we
learn general pairwise terms. Further we evaluate the algorithm on many other
benchmark labeling problems such as stereo, image segmentation, image stitching
and image denoising. Our method consistently gets better accuracy and energy
values than alpha-expansion, loopy belief propagation (LBP), quadratic
pseudo-boolean optimization (QPBO), and is competitive with TRWS. | ['Philip H. S. Torr', 'Vibhav Vineet', 'Jonathan Warrell'] | 2014-03-25 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.49392879e-01 1.44950554e-01 -1.76501200e-01 -6.00031912e-01
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a284aa3c-e10c-4052-8eb4-5a93bb10facb | the-role-of-relevance-in-fair-ranking | 2305.05608 | null | https://arxiv.org/abs/2305.05608v2 | https://arxiv.org/pdf/2305.05608v2.pdf | The Role of Relevance in Fair Ranking | Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace. In order to do so responsibly, these socially consequential systems employ various fairness measures and interventions, many of which seek to allocate exposure based on worthiness. Because these constructs are typically not directly observable, platforms must instead resort to using proxy scores such as relevance and infer them from behavioral signals such as searcher clicks. Yet, it remains an open question whether relevance fulfills its role as such a worthiness score in high-stakes fair rankings. In this paper, we combine perspectives and tools from the social sciences, information retrieval, and fairness in machine learning to derive a set of desired criteria that relevance scores should satisfy in order to meaningfully guide fairness interventions. We then empirically show that not all of these criteria are met in a case study of relevance inferred from biased user click data. We assess the impact of these violations on the estimated system fairness and analyze whether existing fairness interventions may mitigate the identified issues. Our analyses and results surface the pressing need for new approaches to relevance collection and generation that are suitable for use in fair ranking. | ['Asia Biega', 'Abigail Z. Jacobs', 'Aparna Balagopalan'] | 2023-05-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.36479455e-01 3.98021191e-01 -7.22762525e-01 -5.49488783e-01
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962ece63-5acd-4be6-8639-4cb7381a44d8 | icdaelst-intensity-controllable-detail | 2306.16846 | null | https://arxiv.org/abs/2306.16846v1 | https://arxiv.org/pdf/2306.16846v1.pdf | ICDaeLST: Intensity-Controllable Detail Attention-enhanced for Lightweight Fast Style Transfer | The mainstream style transfer methods usually use pre-trained deep convolutional neural network (VGG) models as encoders, or use more complex model structures to achieve better style transfer effects. This leads to extremely slow processing speeds for practical tasks due to limited resources or higher resolution image processing, such as 4K images, severely hindering the practical application value of style transfer models. We introduce a lightweight and fast styletransfer model with controllable detail attention enhancement, named ICDaeLST. The model adopts a minimal, shallow, and small architecture, forming a very compact lightweight model for efficient forward inference. Although its structure is simple and has limited parameters, we achieve better overall color and texture structure matching by introducing a style discriminator, design additional global semantic invariance loss to preserve the semantic and structural information of the content image from a high-level global perspective, and design a shallow detail attention enhancement module to preserve the detail information of the content image from a low-level detail perspective. We also achieve controllable intensity during inference for the first time (adjusting the degree of detail retention and texture structure transfer based on subjective judgment) to meet different users' subjective evaluation of stylization effects. Compared with the current best-performing and most lightweight models, our model achieves better style transfer quality and better content structure and detail retention, while having a smaller model size (17-250 times smaller) and faster speed (0.26-6.5 times faster), and achieves the fastest processing speed of 0.38s on 4K high-resolution images. | ['Jiang Shi Qi'] | 2023-06-29 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 1.58321053e-01 -1.87489077e-01 -1.94919109e-01 -3.51095110e-01
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1fb96dd2-71f3-49cc-a7ca-aa0ed855c78a | vector-of-locally-aggregated-word-embeddings | 1902.08850 | null | https://arxiv.org/abs/1902.08850v3 | https://arxiv.org/pdf/1902.08850v3.pdf | Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation | In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings. Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. First, the word embeddings gathered from a collection of documents are clustered by k-means in order to learn a codebook of semnatically-related word embeddings. Each word embedding is then associated to its nearest cluster centroid (codeword). The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword. We plug the VLAWE representation, which is learned in an unsupervised manner, into a classifier and show that it is useful for a diverse set of text classification tasks. We compare our approach with a broad range of recent state-of-the-art methods, demonstrating the effectiveness of our approach. Furthermore, we obtain a considerable improvement on the Movie Review data set, reporting an accuracy of 93.3%, which represents an absolute gain of 10% over the state-of-the-art approach. Our code is available at https://github.com/raduionescu/vlawe-boswe/. | ['Radu Tudor Ionescu', 'Andrei M. Butnaru'] | 2019-02-23 | vector-of-locally-aggregated-word-embeddings-1 | https://aclanthology.org/N19-1033 | https://aclanthology.org/N19-1033.pdf | naacl-2019-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-1.01542647e-03 -3.61925453e-01 -3.78904819e-01 -6.72930852e-02
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a201ea8c-0113-40a1-be76-bdfde5d2e018 | video-based-surgical-skills-assessment-using | 2207.02247 | null | https://arxiv.org/abs/2207.02247v1 | https://arxiv.org/pdf/2207.02247v1.pdf | Video-based Surgical Skills Assessment using Long term Tool Tracking | Mastering the technical skills required to perform surgery is an extremely challenging task. Video-based assessment allows surgeons to receive feedback on their technical skills to facilitate learning and development. Currently, this feedback comes primarily from manual video review, which is time-intensive and limits the feasibility of tracking a surgeon's progress over many cases. In this work, we introduce a motion-based approach to automatically assess surgical skills from surgical case video feed. The proposed pipeline first tracks surgical tools reliably to create motion trajectories and then uses those trajectories to predict surgeon technical skill levels. The tracking algorithm employs a simple yet effective re-identification module that improves ID-switch compared to other state-of-the-art methods. This is critical for creating reliable tool trajectories when instruments regularly move on- and off-screen or are periodically obscured. The motion-based classification model employs a state-of-the-art self-attention transformer network to capture short- and long-term motion patterns that are essential for skill evaluation. The proposed method is evaluated on an in-vivo (Cholec80) dataset where an expert-rated GOALS skill assessment of the Calot Triangle Dissection is used as a quantitative skill measure. We compare transformer-based skill assessment with traditional machine learning approaches using the proposed and state-of-the-art tracking. Our result suggests that using motion trajectories from reliable tracking methods is beneficial for assessing surgeon skills based solely on video streams. | ['Jocelyn Barker', 'Aishani Ataliwala', 'Anshu Gupta', 'Lela DiMonte', 'Ramon Pena', 'Mohammad Hasan Sarhan', 'Mona Fathollahi'] | 2022-07-05 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-3.14107048e-03 -2.42979318e-01 -5.94233274e-01 3.23787808e-01
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02300535-3234-4c18-8c9e-363362398107 | diva-domain-invariant-variational-autoencoder | null | null | https://openreview.net/forum?id=SkgkEL8FdV | https://openreview.net/pdf?id=SkgkEL8FdV | DIVA: Domain Invariant Variational Autoencoder | We consider the problem of domain generalization, namely, how to learn representations
given data from a set of domains that generalize to data from a previously
unseen domain. We propose the domain invariant VAE (DIVA), a generative
model that tackles this problem by learning three independent latent subspaces,
one for the class, one for the domain and one for the object itself. In addition,
we highlight that due to the generative nature of our model we can also incorporate
unlabeled data from known or previously unseen domains. This property is
highly desirable in fields like medical imaging where labeled data is scarce. We
experimentally evaluate our model on the rotated MNIST benchmark where we
show that (i) the learned subspaces are indeed complementary to each other, (ii)
we improve upon recent works on this task and (iii) incorporating unlabelled data
can boost the performance even further. | ['Max Welling', 'Christos Louizos', 'Jakub M. Tomczak', 'Maximilian Ilse'] | 2019-03-27 | null | https://openreview.net/forum?id=rJxotpNYPS | https://openreview.net/pdf?id=rJxotpNYPS | iclr-workshop-deepgenstruct-2019 | ['rotated-mnist'] | ['computer-vision'] | [ 3.80567789e-01 2.06652507e-01 -1.83914945e-01 -4.33811486e-01
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b2d8e096-6be2-4dc2-9c3b-ebf1a788c6ed | punktuator-a-multilingual-punctuation | null | null | https://aclanthology.org/2021.eacl-demos.37 | https://aclanthology.org/2021.eacl-demos.37.pdf | PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text | Text transcripts without punctuation or sentence boundaries are hard to comprehend for both humans and machines. Punctuation marks play a vital role by providing meaning to the sentence and incorrect use or placement of punctuation marks can often alter it. This can impact downstream tasks such as language translation and understanding, pronoun resolution, text summarization, etc. for humans and machines. An automated punctuation restoration (APR) system with minimal human intervention can improve comprehension of text and help users write better. In this paper we describe a multitask modeling approach as a system to restore punctuation in multiple high resource {--} Germanic (English and German), Romanic (French){--} and low resource languages {--} Indo-Aryan (Hindi) Dravidian (Tamil) {--} that does not require extensive knowledge of grammar or syntax of a given language for both spoken and written form of text. For German language and the given Indic based languages this is the first towards restoring punctuation and can serve as a baseline for future work. | ['Varnith Chordia'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.71375233e-01 8.75092894e-02 1.08001895e-01 -4.79218811e-01
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b00b1d14-ad1d-4778-949e-4f3f9ce06b8f | off-by-one-implementation-error-in-j-uniward | 2305.19776 | null | https://arxiv.org/abs/2305.19776v1 | https://arxiv.org/pdf/2305.19776v1.pdf | Off-By-One Implementation Error in J-UNIWARD | J-UNIWARD is a popular steganography method for hiding secret messages in JPEG cover images. As a content-adaptive method, J-UNIWARD aims to embed into textured image regions where changes are difficult to detect. To this end, J-UNIWARD first assigns to each DCT coefficient an embedding cost calculated based on the image's Wavelet residual, and then uses a coding method that minimizes the cost while embedding the desired payload. Changing one DCT coefficient affects a 23x23 window of Wavelet coefficients. To speed up the costmap computation, the original implementation pre-computes the Wavelet residual and then considers per changed DCT coefficient a 23x23 window of the Wavelet residual. However, the implementation accesses a window accidentally shifted by one pixel to the bottom right. In this report, we evaluate the effect of this off-by-one error on the resulting costmaps. Some image blocks are over-priced while other image blocks are under-priced, but the difference is relatively small. The off-by-one error seems to make little difference for learning-based steganalysis. | ['Benedikt Lorch'] | 2023-05-31 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 7.42098331e-01 2.47576565e-01 -9.60758105e-02 2.19531246e-02
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2d76f4fa-436f-4b36-895f-f2ad382a9568 | synthetic-data-based-detection-of-zebras-in | 2305.00432 | null | https://arxiv.org/abs/2305.00432v2 | https://arxiv.org/pdf/2305.00432v2.pdf | Synthetic Data-based Detection of Zebras in Drone Imagery | Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. On the other hand, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information, such as human shapes, are hardly available. To overcome this, synthetic data generation with realistic rendering technologies has recently gained traction and advanced research areas such as target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://eliabntt.github.io/grade-rr. | ['Aamir Ahmad', 'Elia Bonetto'] | 2023-04-30 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.42189115e-01 1.26794592e-01 2.68212944e-01 -2.14036897e-01
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6fd3a962-008a-448c-b12f-46c79b315111 | ai-ku-at-semeval-2016-task-11-word-embeddings | null | null | https://aclanthology.org/S16-1163 | https://aclanthology.org/S16-1163.pdf | AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification | null | ['Onur Kuru'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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32b22725-7215-49ea-bdfa-d5900ce77fd9 | learning-person-object-interactions-for | null | null | http://papers.nips.cc/paper/4224-learning-person-object-interactions-for-action-recognition-in-still-images | http://papers.nips.cc/paper/4224-learning-person-object-interactions-for-action-recognition-in-still-images.pdf | Learning person-object interactions for action recognition in still images | We investigate a discriminatively trained model of person-object interactions for recognizing common human actions in still images. We build on the locally order-less spatial pyramid bag-of-features model, which was shown to perform extremely well on a range of object, scene and human action recognition tasks. We introduce three principal contributions. First, we replace the standard quantized local HOG/SIFT features with stronger discriminatively trained body part and object detectors. Second, we introduce new person-object interaction features based on spatial co-occurrences of individual body parts and objects. Third, we address the combinatorial problem of a large number of possible interaction pairs and propose a discriminative selection procedure using a linear support vector machine (SVM) with a sparsity inducing regularizer. Learning of action-specific body part and object interactions bypasses the difficult problem of estimating the complete human body pose configuration. Benefits of the proposed model are shown on human action recognition in consumer photographs, outperforming the strong bag-of-features baseline. | ['Ivan Laptev', 'Vincent Delaitre', 'Josef Sivic'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 4.29279178e-01 -1.51857138e-01 -2.56898075e-01 -5.04041255e-01
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9584540c-2000-4ec5-ab7d-037f848126dc | salsa-lite-a-fast-and-effective-feature-for | 2111.08192 | null | https://arxiv.org/abs/2111.08192v2 | https://arxiv.org/pdf/2111.08192v2.pdf | SALSA-Lite: A Fast and Effective Feature for Polyphonic Sound Event Localization and Detection with Microphone Arrays | Polyphonic sound event localization and detection (SELD) has many practical applications in acoustic sensing and monitoring. However, the development of real-time SELD has been limited by the demanding computational requirement of most recent SELD systems. In this work, we introduce SALSA-Lite, a fast and effective feature for polyphonic SELD using microphone array inputs. SALSA-Lite is a lightweight variation of a previously proposed SALSA feature for polyphonic SELD. SALSA, which stands for Spatial Cue-Augmented Log-Spectrogram, consists of multichannel log-spectrograms stacked channelwise with the normalized principal eigenvectors of the spectrotemporally corresponding spatial covariance matrices. In contrast to SALSA, which uses eigenvector-based spatial features, SALSA-Lite uses normalized inter-channel phase differences as spatial features, allowing a 30-fold speedup compared to the original SALSA feature. Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset showed that the SALSA-Lite feature achieved competitive performance compared to the full SALSA feature, and significantly outperformed the traditional feature set of multichannel log-mel spectrograms with generalized cross-correlation spectra. Specifically, using SALSA-Lite features increased localization-dependent F1 score and class-dependent localization recall by 15% and 5%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra. | ['Woon-Seng Gan', 'Huy Phan', 'Karn N. Watcharasupat', 'Douglas L. Jones', 'Thi Ngoc Tho Nguyen'] | 2021-11-16 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.73044646e-01 -7.11855292e-01 7.72508740e-01 -1.51161170e-02
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7fce0e61-03ab-4c93-936a-e8745bc81ea7 | a-temporally-aware-interpolation-network-for | 1803.07218 | null | http://arxiv.org/abs/1803.07218v2 | http://arxiv.org/pdf/1803.07218v2.pdf | A Temporally-Aware Interpolation Network for Video Frame Inpainting | We propose the first deep learning solution to video frame inpainting, a
challenging instance of the general video inpainting problem with applications
in video editing, manipulation, and forensics. Our task is less ambiguous than
frame interpolation and video prediction because we have access to both the
temporal context and a partial glimpse of the future, allowing us to better
evaluate the quality of a model's predictions objectively. We devise a pipeline
composed of two modules: a bidirectional video prediction module, and a
temporally-aware frame interpolation module. The prediction module makes two
intermediate predictions of the missing frames, one conditioned on the
preceding frames and the other conditioned on the following frames, using a
shared convolutional LSTM-based encoder-decoder. The interpolation module
blends the intermediate predictions to form the final result. Specifically, it
utilizes time information and hidden activations from the video prediction
module to resolve disagreements between the predictions. Our experiments
demonstrate that our approach produces more accurate and qualitatively
satisfying results than a state-of-the-art video prediction method and many
strong frame inpainting baselines. | ['Ximeng Sun', 'Ryan Szeto', 'Jason J. Corso'] | 2018-03-20 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.03524762e-01 9.80293900e-02 -3.18601906e-01 -4.59014118e-01
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d567d0d9-cc1b-4e10-bae0-d9cf462643af | benchmarking-deepart-detection | 2302.14475 | null | https://arxiv.org/abs/2302.14475v1 | https://arxiv.org/pdf/2302.14475v1.pdf | Benchmarking Deepart Detection | Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfake researchers have been making a great upending to create deepfake artworks (deeparts), which are further closing the gap between reality and fantasy. To address potentially appeared ethics questions, this paper establishes a deepart detection database (DDDB) that consists of a set of high-quality conventional art images (conarts) and five sets of deepart images generated by five state-of-the-art deepfake models. This database enables us to explore once-for-all deepart detection and continual deepart detection. For the two new problems, we suggest four benchmark evaluations and four families of solutions on the constructed DDDB. The comprehensive study demonstrates the effectiveness of the proposed solutions on the established benchmark dataset, which is capable of paving a way to more interesting directions of deepart detection. The constructed benchmark dataset and the source code will be made publicly available. | ['Xiaopeng Hong', 'Zhiwu Huang', 'Yabin Wang'] | 2023-02-28 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-5.04763842e-01 -2.69038439e-01 -3.34746353e-02 2.21451186e-02
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6e153c5b-57d7-4d1b-84c6-8e3f29337489 | ev-nerf-event-based-neural-radiance-field | 2206.12455 | null | https://arxiv.org/abs/2206.12455v2 | https://arxiv.org/pdf/2206.12455v2.pdf | Ev-NeRF: Event Based Neural Radiance Field | We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting or extreme motion suffer from significant domain discrepancy with complex noise. As a result, the performance of event-based vision tasks does not transfer to challenging environments, where the event cameras are expected to thrive over normal cameras. We find that the multi-view consistency of NeRF provides a powerful self-supervision signal for eliminating the spurious measurements and extracting the consistent underlying structure despite highly noisy input. Instead of posed images of the original NeRF, the input to Ev-NeRF is the event measurements accompanied by the movements of the sensors. Using the loss function that reflects the measurement model of the sensor, Ev-NeRF creates an integrated neural volume that summarizes the unstructured and sparse data points captured for about 2-4 seconds. The generated neural volume can also produce intensity images from novel views with reasonable depth estimates, which can serve as a high-quality input to various vision-based tasks. Our results show that Ev-NeRF achieves competitive performance for intensity image reconstruction under extreme noise conditions and high-dynamic-range imaging. | ['Young Min Kim', 'Junho Kim', 'Inwoo Hwang'] | 2022-06-24 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.62595463e-01 -2.03165323e-01 3.40945840e-01 -5.54597616e-01
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529a1782-c88e-402c-9379-a552e5dad057 | the-inception-platform-machine-assisted-and | null | null | https://aclanthology.org/C18-2002 | https://aclanthology.org/C18-2002.pdf | The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation | We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation). These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used. We address these issues by developing an annotation platform that incorporates machine learning capabilities which actively assist and guide annotators. The platform is both generic and modular. It targets a range of research domains in need of semantic annotation, such as digital humanities, bioinformatics, or linguistics. INCEpTION is publicly available as open-source software. | ['Richard Eckart de Castilho', 'Jan-Christoph Klie', 'Iryna Gurevych', 'Beto Boullosa', 'Michael Bugert'] | 2018-08-01 | the-inception-platform-machine-assisted-and-1 | https://aclanthology.org/C18-2002 | https://aclanthology.org/C18-2002.pdf | coling-2018-8 | ['text-annotation'] | ['natural-language-processing'] | [-1.13020360e-01 9.37431872e-01 -3.36246967e-01 -5.60945496e-02
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97ca4cd7-dfb9-4c6e-afd4-b5fa4f4ad1bc | semantic-clustering-of-a-sequence-of | 2208.13504 | null | https://arxiv.org/abs/2208.13504v2 | https://arxiv.org/pdf/2208.13504v2.pdf | Unsupervised Semantic Analysis of a Region from Satellite Image Time Series | Temporal sequences of satellite images constitute a highly valuable and abundant resource to analyze a given region. However, the labeled data needed to train most machine learning models are scarce and difficult to obtain. In this context, the current work investigates a fully unsupervised methodology that, given a sequence of images, learns a semantic embedding and then, creates a partition of the ground according to its semantic properties and its evolution over time. We illustrate the methodology by conducting the semantic analysis of a sequence of satellite images of a region of Navarre (Spain). The proposed approach reveals a novel broad perspective of the land, where potentially large areas that share both a similar semantic and a similar temporal evolution are connected in a compact and well-structured manner. The results also show a close relationship between the allocation of the clusters in the geographic space and their allocation in the embedded spaces. The semantic analysis is completed by obtaining the representative sequence of tiles corresponding to each cluster, the linear interpolation between related areas, and a graph that shows the relationships between the clusters, providing a concise semantic summary of the whole region. | ['María Dolores Ugarte', 'Unai Pérez-Goya', 'Guzmán Santafé', 'Aritz Pérez', 'Carlos Echegoyen'] | 2022-08-29 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 2.21442714e-01 1.07367292e-01 7.65482560e-02 -2.85151184e-01
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4d4d5ae2-6793-4fa6-8a39-33eb549714ef | a-survey-on-recent-advances-and-challenges-in | 2202.13675 | null | https://arxiv.org/abs/2202.13675v2 | https://arxiv.org/pdf/2202.13675v2.pdf | A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning | Dialogue Policy Learning is a key component in a task-oriented dialogue system (TDS) that decides the next action of the system given the dialogue state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the dialogue policy, regarding the user as the environment and the system as the agent. Many benchmark datasets and algorithms have been created to facilitate the development and evaluation of dialogue policy based on RL. In this paper, we survey recent advances and challenges in dialogue policy from the prescriptive of RL. More specifically, we identify the major problems and summarize corresponding solutions for RL-based dialogue policy learning. Besides, we provide a comprehensive survey of applying RL to dialogue policy learning by categorizing recent methods into basic elements in RL. We believe this survey can shed a light on future research in dialogue management. | ['Kam-Fai Wong', 'Huimin Wang', 'Hongru Wang', 'Wai-Chung Kwan'] | 2022-02-28 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 5.53882215e-03 6.36379957e-01 -5.02154469e-01 -4.63691056e-01
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d707e826-f857-45bd-a615-7f057a0965c6 | distilled-non-semantic-speech-embeddings-with | 2207.05784 | null | https://arxiv.org/abs/2207.05784v3 | https://arxiv.org/pdf/2207.05784v3.pdf | Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices | This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks. We train the model with knowledge distillation from a large and real-valued TRILLsson model with only a fraction of the dataset used to train TRILLsson. The resulting BRILLsson models are only 2MB in size with a latency less than 8ms, making them suitable for deployment in low-resource devices such as wearables. We evaluate BRILLsson on eight benchmark tasks (including but not limited to spoken language identification, emotion recognition, health condition diagnosis, and keyword spotting), and demonstrate that our proposed ultra-light and low-latency models perform as well as large-scale models. | ['Aaqib Saeed', 'Harlin Lee'] | 2022-07-12 | null | null | null | null | ['keyword-spotting', 'spoken-language-identification'] | ['speech', 'speech'] | [ 2.01873824e-01 1.62899345e-01 -3.97741884e-01 -5.37634730e-01
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13605d9e-e6c4-4083-b1e2-e43103ccb222 | topic-analysis-for-text-with-side-data | 2203.00762 | null | https://arxiv.org/abs/2203.00762v1 | https://arxiv.org/pdf/2203.00762v1.pdf | Topic Analysis for Text with Side Data | Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendations. In this paper, we employ text with side data to tackle these limitations. We introduce a hybrid generative probabilistic model that combines a neural network with a latent topic model, which is a four-level hierarchical Bayesian model. In the model, each document is modeled as a finite mixture over an underlying set of topics and each topic is modeled as an infinite mixture over an underlying set of topic probabilities. Furthermore, each topic probability is modeled as a finite mixture over side data. In the context of text, the neural network provides an overview distribution about side data for the corresponding text, which is the prior distribution in LDA to help perform topic grouping. The approach is evaluated on several different datasets, where the model is shown to outperform standard LDA and Dirichlet-multinomial regression (DMR) in terms of topic grouping, model perplexity, classification and comment generation. | ['Diego Klabjan', 'Kripa Rajshekhar', 'Biyi Fang'] | 2022-03-01 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [-2.64755189e-01 4.67729837e-01 -5.76519966e-01 -5.04892528e-01
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02bb8d95-944e-4125-954a-cc79d6f4a02d | english-to-chinese-transliteration-with | null | null | https://aclanthology.org/2020.aacl-main.40 | https://aclanthology.org/2020.aacl-main.40.pdf | English-to-Chinese Transliteration with Phonetic Auxiliary Task | Approaching named entities transliteration as a Neural Machine Translation (NMT) problem is common practice. While many have applied various NMT techniques to enhance machine transliteration models, few focus on the linguistic features particular to the relevant languages. In this paper, we investigate the effect of incorporating phonetic features for English-to-Chinese transliteration under the multi-task learning (MTL) setting{---}where we define a phonetic auxiliary task aimed to improve the generalization performance of the main transliteration task. In addition to our system, we also release a new English-to-Chinese dataset and propose a novel evaluation metric which considers multiple possible transliterations given a source name. Our results show that the multi-task model achieves similar performance as the previous state of the art with a model of a much smaller size. | ['Shay B. Cohen', 'Yuan He'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['transliteration'] | ['natural-language-processing'] | [ 2.29618713e-01 -2.11512029e-01 -4.77632850e-01 -5.83358526e-01
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575b5f02-37ee-43b7-b261-667e456f52d4 | an-efficient-subpopulation-based-membership | 2203.02080 | null | https://arxiv.org/abs/2203.02080v1 | https://arxiv.org/pdf/2203.02080v1.pdf | An Efficient Subpopulation-based Membership Inference Attack | Membership inference attacks allow a malicious entity to predict whether a sample is used during training of a victim model or not. State-of-the-art membership inference attacks have shown to achieve good accuracy which poses a great privacy threat. However, majority of SOTA attacks require training dozens to hundreds of shadow models to accurately infer membership. This huge computation cost raises questions about practicality of these attacks on deep models. In this paper, we introduce a fundamentally different MI attack approach which obviates the need to train hundreds of shadow models. Simply put, we compare the victim model output on the target sample versus the samples from the same subpopulation (i.e., semantically similar samples), instead of comparing it with the output of hundreds of shadow models. The intuition is that the model response should not be significantly different between the target sample and its subpopulation if it was not a training sample. In cases where subpopulation samples are not available to the attacker, we show that training only a single generative model can fulfill the requirement. Hence, we achieve the state-of-the-art membership inference accuracy while significantly reducing the training computation cost. | ['Xin Liu', 'Shahbaz Rezaei'] | 2022-03-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-4.86534499e-02 6.83811605e-02 -5.15824370e-02 -3.07146460e-01
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e3676c44-5061-4fef-980b-5c5a420b7e96 | visual-language-pretrained-multiple-instance-1 | 2306.07831 | null | https://arxiv.org/abs/2306.07831v1 | https://arxiv.org/pdf/2306.07831v1.pdf | Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images | Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 x 100,000 pixels. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pre-train our text encoder. By effectively leveraging strong pre-trained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. Our code is available at: https://github.com/mahmoodlab/MI-Zero. | ['Faisal Mahmood', 'Yung-Sung Chuang', 'Long Phi Le', 'Tong Ding', 'Richard J. Chen', 'Drew F. K. Williamson', 'Andrew Zhang', 'Bowen Chen', 'Ming Y. Lu'] | 2023-06-13 | visual-language-pretrained-multiple-instance | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.pdf | cvpr-2023-1 | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.55942619e-01 3.46197724e-01 -2.23539740e-01 -2.36041561e-01
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139fe27f-ad50-4b17-af4f-45eb447fecdf | map-estimation-for-graphical-models-by | null | null | http://papers.nips.cc/paper/4165-map-estimation-for-graphical-models-by-likelihood-maximization | http://papers.nips.cc/paper/4165-map-estimation-for-graphical-models-by-likelihood-maximization.pdf | MAP Estimation for Graphical Models by Likelihood Maximization | Computing a {\em maximum a posteriori} (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which transforms the MAP problem into that of inference in a finite mixture of simple Bayes nets. We then derive the Expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm are remarkably simple, both conceptually and computationally, and can be implemented using a graph-based message passing paradigm similar to max-product computation. We experiment on the real-world protein design dataset and show that EM's convergence rate is significantly higher than the previous LP relaxation based approach MPLP. EM achieves a solution quality within $95$\% of optimal for most instances and is often an order-of-magnitude faster than MPLP. | ['Akshat Kumar', 'Shlomo Zilberstein'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['protein-design'] | ['medical'] | [ 4.51149464e-01 5.11796296e-01 -7.82736614e-02 -6.52242184e-01
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3598380c-3419-489a-9505-8269f4c2ac8a | learning-bias-invariant-representation-by | 2108.05449 | null | https://arxiv.org/abs/2108.05449v2 | https://arxiv.org/pdf/2108.05449v2.pdf | Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization | Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with a cross-sample adversarial debiasing (CSAD) method. CSAD explicitly extracts target and bias features disentangled from the latent representation generated by a feature extractor and then learns to discover and remove the correlation between the target and bias features. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. Moreover, we propose joint content and local structural representation learning to boost mutual information estimation for better performance. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches. | ['Jiebo Luo', 'Weijian Li', 'Haofu Liao', 'Haitian Zheng', 'Wei Zhu'] | 2021-08-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.pdf | iccv-2021-1 | ['mutual-information-estimation'] | ['methodology'] | [ 2.81647295e-01 -1.23222783e-01 -3.05719614e-01 -3.26561153e-01
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97c65a8a-d4d3-43e4-ac10-e70a0f157150 | cross-scale-multi-instance-learning-for | 2304.00216 | null | https://arxiv.org/abs/2304.00216v1 | https://arxiv.org/pdf/2304.00216v1.pdf | Cross-scale Multi-instance Learning for Pathological Image Diagnosis | Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL. | ['Yuankai Huo', 'Bennett A. Landman', 'Lori A. Coburn', 'Yaohong Wang', 'Keith T. Wilson', 'Qi Liu', 'Ken S. Lau', 'Joseph T. Roland', 'Jia Li', 'Sophie Chiron', 'R. Michael Womick', 'Shunxing Bao', 'Lucas W. Remedios', 'Can Cui', 'Ruining Deng'] | 2023-04-01 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 5.08983254e-01 2.10765731e-02 -9.47191790e-02 -1.95318550e-01
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bc4df425-557f-4d6b-b31c-ac3706d0c548 | exploiting-weakly-labeled-web-images-to | null | null | http://papers.nips.cc/paper/4064-exploiting-weakly-labeled-web-images-to-improve-object-classification-a-domain-adaptation-approach | http://papers.nips.cc/paper/4064-exploiting-weakly-labeled-web-images-to-improve-object-classification-a-domain-adaptation-approach.pdf | Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach | Most current image categorization methods require large collections of manually annotated training examples to learn accurate visual recognition models. The time-consuming human labeling effort effectively limits these approaches to recognition problems involving a small number of different object classes. In order to address this shortcoming, in recent years several authors have proposed to learn object classifiers from weakly-labeled Internet images, such as photos retrieved by keyword-based image search engines. While this strategy eliminates the need for human supervision, the recognition accuracies of these methods are considerably lower than those obtained with fully-supervised approaches, because of the noisy nature of the labels associated to Web data. In this paper we investigate and compare methods that learn image classifiers by combining very few manually annotated examples (e.g., 1-10 images per class) and a large number of weakly-labeled Web photos retrieved using keyword-based image search. We cast this as a domain adaptation problem: given a few strongly-labeled examples in a target domain (the manually annotated examples) and many source domain examples (the weakly-labeled Web photos), learn classifiers yielding small generalization error on the target domain. Our experiments demonstrate that, for the same number of strongly-labeled examples, our domain adaptation approach produces significant recognition rate improvements over the best published results (e.g., 65% better when using 5 labeled training examples per class) and that our classifiers are one order of magnitude faster to learn and to evaluate than the best competing method, despite our use of large weakly-labeled data sets. | ['Alessandro Bergamo', 'Lorenzo Torresani'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['image-categorization'] | ['computer-vision'] | [ 4.38628852e-01 -9.15017650e-02 -6.24325693e-01 -6.36020243e-01
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8e70c10a-3dbb-4f3a-b51f-9b014025e588 | adaptive-recursive-circle-framework-for-fine | 2107.11813 | null | https://arxiv.org/abs/2107.11813v1 | https://arxiv.org/pdf/2107.11813v1.pdf | Adaptive Recursive Circle Framework for Fine-grained Action Recognition | How to model fine-grained spatial-temporal dynamics in videos has been a challenging problem for action recognition. It requires learning deep and rich features with superior distinctiveness for the subtle and abstract motions. Most existing methods generate features of a layer in a pure feedforward manner, where the information moves in one direction from inputs to outputs. And they rely on stacking more layers to obtain more powerful features, bringing extra non-negligible overheads. In this paper, we propose an Adaptive Recursive Circle (ARC) framework, a fine-grained decorator for pure feedforward layers. It inherits the operators and parameters of the original layer but is slightly different in the use of those operators and parameters. Specifically, the input of the layer is treated as an evolving state, and its update is alternated with the feature generation. At each recursive step, the input state is enriched by the previously generated features and the feature generation is made with the newly updated input state. We hope the ARC framework can facilitate fine-grained action recognition by introducing deeply refined features and multi-scale receptive fields at a low cost. Significant improvements over feedforward baselines are observed on several benchmarks. For example, an ARC-equipped TSM-ResNet18 outperforms TSM-ResNet50 with 48% fewer FLOPs and 52% model parameters on Something-Something V1 and Diving48. | ['Jiebo Luo', 'Xinxiao wu', 'Hanxi Lin'] | 2021-07-25 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.07682723e-01 -4.60345626e-01 -3.34566504e-01 -2.74845630e-01
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c026a901-5ce2-483c-8781-3613505f579e | real-time-semantic-scene-completion-via | 2303.10967 | null | https://arxiv.org/abs/2303.10967v2 | https://arxiv.org/pdf/2303.10967v2.pdf | Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction | Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. In this paper, we propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module. Feature aggregation fuses feature with different receptive fields and gathers context to improve scene completion performance. And the conditioned prediction module adopts a two-step prediction scheme that takes volumetric occupancy as a condition to enhance semantic completion prediction. We conduct experiments on three recognized benchmarks NYU, NYUCAD, and SUNCG. Our method achieves competitive performance at a speed of 110 FPS on one GTX 1080 Ti GPU. | ['Gang Zeng', 'Yajie Xing', 'Xiaokang Chen'] | 2023-03-20 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 3.01339358e-01 -3.53268385e-01 1.03853196e-01 -6.13761008e-01
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a05b8d49-348a-439d-a4ad-6607f9a6ef74 | rethinking-prl-a-multiscale-progressively | 2305.17355 | null | https://arxiv.org/abs/2305.17355v1 | https://arxiv.org/pdf/2305.17355v1.pdf | Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning | Image inverse halftoning is a classic image restoration task, aiming to recover continuous-tone images from halftone images with only bilevel pixels. Because the halftone images lose much of the original image content, inverse halftoning is a classic ill-problem. Although existing inverse halftoning algorithms achieve good performance, their results lose image details and features. Therefore, it is still a challenge to recover high-quality continuous-tone images. In this paper, we propose an end-to-end multiscale progressively residual learning network (MSPRL), which has a UNet architecture and takes multiscale input images. To make full use of different input image information, we design a shallow feature extraction module to capture similar features between images of different scales. We systematically study the performance of different methods and compare them with our proposed method. In addition, we employ different training strategies to optimize the model, which is important for optimizing the training process and improving performance. Extensive experiments demonstrate that our MSPRL model obtains considerable performance gains in detail restoration. | ['Jun Yang', 'Feiyu Li'] | 2023-05-27 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 5.33532023e-01 -3.84470552e-01 -2.06099469e-02 -2.71248877e-01
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c84c63de-c138-43ed-8f55-023dddf26659 | visual-semantic-segmentation-based-on-few | 2211.08352 | null | https://arxiv.org/abs/2211.08352v1 | https://arxiv.org/pdf/2211.08352v1.pdf | Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview | Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning. The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen-category from a few labeled or zero-labeled samples, which advances the extension to practical applications. Therefore, this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances. Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed. Moreover, three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation, including image semantic segmentation, video object segmentation, and 3D segmentation. Finally, the future challenges of few/zero-shot visual semantic segmentation are discussed. | ['Qing-Long Han', 'Chaoqiang Zhao', 'Qiyu Sun', 'Yang Tang', 'Wenqi Ren'] | 2022-11-13 | null | null | null | null | ['video-object-segmentation'] | ['computer-vision'] | [ 3.90973687e-01 3.94569933e-02 -6.54801607e-01 -4.05314863e-01
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c4d21e7d-9bcd-428e-a7ba-2dbd096aacfc | mesograph-automatic-profiling-of-malignant | 2302.12653 | null | https://arxiv.org/abs/2302.12653v1 | https://arxiv.org/pdf/2302.12653v1.pdf | MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images | Malignant mesothelioma is classified into three histological subtypes, Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Biphasic tumors display significant populations of both cell types. This subtyping is subjective and limited by current diagnostic guidelines and can differ even between expert thoracic pathologists when characterising the continuum of relative proportions of epithelioid and sarcomatoid components using a three class system. In this work, we develop a novel dual-task Graph Neural Network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score of all the cells in the sample. The proposed approach uses only core-level labels and frames the prediction task as a dual multiple instance learning (MIL) problem. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multi-centric test set from Mesobank, on which we demonstrate the predictive performance of our model. We validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score, finding that some of the morphological differences identified by our model match known differences used by pathologists. We further show that the model score is predictive of patient survival with a hazard ratio of 2.30. The code for the proposed approach, along with the dataset, is available at: https://github.com/measty/MesoGraph. | ['Jan Lukas Robertus', 'Fayyaz Minhas', 'Sanjay Popat', 'Miriam Moffatt', 'William Cookson', 'Danny Jonigk', 'Angeles Montero Fernandez', 'Emmanouil Karteris', 'Judith Offman', 'Xiaohong Gao', 'Silviu Tudor', 'Heba Sailem', 'Mark Eastwood'] | 2023-02-23 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 3.02877724e-01 3.60390127e-01 -5.91295004e-01 1.15339831e-01
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4a0f7152-f59d-456f-8491-4717354482c7 | self-organizing-maps-for-the-visual-analysis | null | null | https://aclanthology.org/W15-1823 | https://aclanthology.org/W15-1823.pdf | Self Organizing Maps for the Visual Analysis of Pitch Contours | null | ['Miriam Butt', 'Dominik Sacha', 'Daniel Keim', 'Christian Rohrdantz', 'Yuki Asano', 'Felix Hamborg', 'Bettina Braun'] | 2015-05-01 | null | null | null | ws-2015-5 | ['art-analysis'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8a8a9f12-f943-4e9a-923c-71b85bb51e94 | faster-training-of-diffusion-models-and | 2306.02658 | null | https://arxiv.org/abs/2306.02658v1 | https://arxiv.org/pdf/2306.02658v1.pdf | Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching | In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potentially impede modeling flexibility and capacity. To counteract these challenges, we propose leveraging the independence of learning tasks at different time points inherent to DPMs. More specifically, we partition the learning task by utilizing independent networks, each dedicated to learning the evolution of scores within a specific time sub-interval. Further, inspired by residual flows, we extend this strategy to its logical conclusion by employing separate networks to independently model the score at each individual time point. As empirically demonstrated on synthetic and image datasets, our approach not only significantly accelerates the training process by introducing an additional layer of parallelization atop data parallelization, but it also enhances density estimation performance when compared to the conventional training methodology for DPMs. | ['Marco Lorenzi', 'Etrit Haxholli'] | 2023-06-05 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 4.46704961e-02 5.43905869e-02 -2.23950058e-01 -2.53488421e-01
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370219cc-37ac-480b-b54c-6775fe1b6cbc | unfolding-local-growth-rate-estimates-for | 2212.06776 | null | https://arxiv.org/abs/2212.06776v1 | https://arxiv.org/pdf/2212.06776v1.pdf | Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection | Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small "detector" is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID | ['Janis Keuper', 'Margret Keuper', 'Peter Lorenz'] | 2022-12-13 | null | null | null | null | ['adversarial-defense', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['adversarial', 'computer-vision', 'knowledge-base'] | [ 3.30480754e-01 3.49240869e-01 1.25991061e-01 -1.07436135e-01
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6.37134612e-01 -3.89062703e-01 -8.28541279e-01 2.76236199e-02] | [5.623636245727539, 7.861093997955322] |
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