General-purpose model learns visual reasoning without strong priors.
problem Achieving visual reasoning in image-related questions.
method Conditional Batch Normalization approach.
result 2.4% error rate on CLEVR Visual Reasoning benchmark.
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169,181 papers · 148 categories
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48 results for “image-related questions”
FiLM layers improve visual reasoning tasks by modulating features.
problem Visual reasoning tasks that require multi-step, high-level processes.
method General-purpose FiLM layers that apply feature-wise linear transformations based on conditioning information.
result FiLM layers reduce error by half on the CLEVR benchmark and improve feature coherence.
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signaturescs.CV
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
In this paper, we introduce a new image representation based on a multilayer kernel machine. Unlike traditional kernel methods where data representation is decoupled from the prediction task, we learn how to shape the kernel with supervision. We proceed by first proposing improvements of the recently-introduced convolu…
Deep learning solves jigsaw puzzles by classifying fragment positions.
problem Automated reconstruction of archaeological fragments from jigsaw puzzles.
method Classifies relative positions of fragments using deep neural networks and local feature co-occurrences.
result Our method outperforms state-of-the-art by 25%.
POUF fine-tunes large models without labeled data.
problem Lack of labeled data for fine-tuning large pre-trained models.
method Prompt-oriented unsupervised fine-tuning.
result Consistent improvements across various tasks.
Revisited Lmser for image recognition with convolutional layers.
problem Lmser's limitations in one-hidden-layer implementation and lack of computing resources.
method Developed Lmser network using multiple convolutional layers for image-related tasks.
result Lmser functions are confirmed with preliminary demonstrations on image recognition and reconstruction.
Predicting MRI coil failures using image features and ensemble learning.
problem Ensuring trouble-free operation of MRI systems by predicting hardware failures.
method Two-level data analysis with neural networks and Random Forest ensemble learning.
result Improved prediction results with an F-score of 94.14% and an accuracy of 99.09%.
A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. However, analytical estimates can be obtained only for particular combinations of analytical models of signal and noise, thus precluding its straightforward extension to deal with other arbitrary noise sour…
Crowdsourced science questions improve exam accuracy.
problem Generating high-quality, domain-specific multiple choice questions is challenging.
method Leveraging a corpus and existing questions, the method suggests document selection and answer distractors.
result Crowdsourced questions are indistinguishable from original questions and improve exam accuracy.
Hybrid QC system for Bengali questions using smart data balancing.
problem Classifying factoid questions in Bengali.
method Two-stage approach with 1D CNN for coarse classification and SGD for fine classification.
result Effectiveness of smart data balancing technique in improving classification accuracy.
Paper tackles medical question similarity using domain-relevant embeddings.
problem Identifying same-question pairs in medical contexts.
method Semi-supervised pre-training of a neural network on medical question-answer pairs.
result Our model achieves 82.6% accuracy on medical question similarity task.
Paper analyzes biases in video QA datasets, showing models can answer 37-48% questions correctly without multimodal context.
problem Question answering biases in video QA datasets can lead to model overfitting and poor generalization.
method Analyzed popular video question answering datasets, conducted ablation studies on biases from annotators and question types.
result Pretrained language models can answer 37-48% questions correctly without multimodal context, far exceeding random guess baseline.
Paper evaluates Arabic question similarity, 9 teams participated.
problem Predicting semantic similarity between Arabic questions.
method 9 teams participated in a shared task to predict semantic similarity.
result 9 teams participated in the task, results made publicly available.
Paper proposes a neural network for generating better questions from text.
problem Automatic generation of relevant questions from sentences and paragraphs.
method Adaptive copying recurrent neural network model with a copying mechanism added to a bidirectional LSTM architecture.
result The model outperforms state-of-the-art methods in question generation metrics.
Paper tackles Arabic question similarity, outperforming state-of-the-art.
problem Detecting semantically similar questions in Arabic is challenging.
method Utilizes contextualized word representations (ELMo embeddings) trained on MSA and dialectic sentences, combined with a pairwise similarity layer.
result Achieves 93% F1-score on Modern Standard Arabic benchmark and 82% on dialectical benchmark.
Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order …
MAC Net improves natural language question answering with data-driven reasoning.
problem Natural Language Question Answering requires complex reasoning.
method MAC Net architecture separates memory and control for iterative reasoning.
result MAC Net achieves high efficiency and interpretability in NLP tasks.
R2DE assesses new exam questions quickly and accurately.
problem Assessing newly generated exam questions efficiently and accurately.
method R2DE uses NLP to estimate difficulty and discrimination of questions based on their text.
result R2DE can estimate question difficulty and discrimination quickly and accurately.
Unified QuesNet learns comprehensive representations for diverse test questions.
problem Lack of labeled data for test questions in online learning systems.
method Unified framework and two-level hierarchical pre-training algorithm for unsupervised learning of heterogeneous question representations.
result QuesNet effectively learns comprehensive question representations and outperforms existing methods.
On two questions of Jamesmath.DG
James's octonionic Stiefel spaces questions answered partially.
problem Two fundamental questions about octonionic Stiefel spaces.
method Partial answers to James's questions about octonionic Stiefel spaces.
result Partial answers to James's questions about octonionic Stiefel spaces.
First Proofcs.AI
AI tested on 10 math questions from research.
problem Assessing AI's ability to solve research-level math problems.
method Shared 10 math questions not previously publicly available.
result Answers to questions are known to authors but encrypted.
Predicts student performance in interactive online question pools using GNNs.
problem Predicting student performance in interactive online question pools with evolving knowledge.
method Proposes R^2GCN, a GNN model for heterogeneous networks to predict student performance.
result Achieves higher accuracy in student performance prediction than traditional methods.
DiffQue estimates relative difficulty of questions in CQA services.
problem Estimating relative difficulty of questions in community Q&A services.
method Network-aided edge directionality prediction.
result DiffQue outperforms state-of-the-art methods by significant margins.
Algorithm transfers visual concepts to answer out-of-vocabulary questions.
problem Leveraging off-the-shelf visual and linguistic data for out-of-vocabulary answers in visual question answering.
method Unsupervised task discovery for learning task conditional visual classifier, then transferring to visual question answering models.
result Algorithm generalizes to out-of-vocabulary answers successfully.
EduQG generates better educational questions by pre-training on scientific text.
problem Improving the quality of educational questions for scalable self-assessment.
method Adapting a large language model for educational question generation, pre-trained on scientific text.
result EduQG produces superior educational questions compared to baseline models.
Solved a specific case of Salter's question on Burau representation.
problem Under what conditions are matrices in the image of the Burau representation of .
method Algorithmically constructed a counterexample to Salter's specific question.
result The central quotient of the Burau image group is not the central quotient of a certain subgroup of the unitary group.
This study analyzes financial equity research reports to identify frequently asked questions and automates 80% of them.
problem Insufficient empirical analysis of questions answered in financial equity research reports.
method Analyzed 72 financial equity research reports, classifying sentences into 169 unique question archetypes. Used public corporate reports to classify questions' potential for automation.
result Approximately 80% of financial equity research reports can be automated, with 78.7% of questions automatable.
We are raising questions on discrete and dense subgroups of Diff(I). Most of the questions are around the problems discussed in [A1]-[A4].
Predicts programming languages from Stack Overflow questions and snippets.
problem Incorrectly tagging programming languages in Stack Overflow questions.
method Combines NLP features from titles, bodies, and code snippets for prediction.
result Combined features classifier achieves 91.1% accuracy.
Abstract reviews known and open questions on spaces with lower Ricci bounds.
problem Understanding the structure and regularity of spaces with lower Ricci curvature bounds.
method Review of known results and presentation of open questions.
result Presentation of new open questions in the field.
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for mo…
Paper proposes a fair grading method for randomized exams.
problem Ensuring fairness in grading for randomized exams.
method Maximum-likelihood estimator for Bradley-Terry-Luce model on student-question graph.
result Maximum-likelihood estimator is consistent and outperforms simple averaging in fairness and accuracy.
New task AQA tackles acoustic reasoning from sound scenes.
problem Promote research in acoustic reasoning.
method Generate acoustic scenes from elementary sounds and formulate questions.
result Preliminary results with models FiLM and MAC show promise.
Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we deve…
Paper answers Gromov's compactness question on noncompact manifolds.
problem Gromov's compactness question on positive scalar curvature metrics.
method Construct examples and prove vanishing of certain index theoretic invariants.
result Positive answer for a class of 1-tame manifolds.
Paper tackles natural science exam questions, improving over previous systems.
problem Hard natural science exam questions requiring advanced logic reasoning.
method Constructs contextual knowledge graphs for questions and supporting sentences, learns to reason with neural embeddings.
result Model outperforms previous state-of-the-art QA systems on the ARC Challenge Set.
Paper proposes a reinforcement learning framework for distant supervision of question parsing.
problem Lack of gold annotations for parsing in QA datasets.
method Reinforcement learning framework based on formal queries to learn mentions of entities and relations.
result Significant improvement in entity and relation linking compared to state of the art.
qDKT improves KT models by considering individual question outcomes.
problem Ignoring question-specific outcomes leads to inaccurate learner skill tracking.
method qDKT incorporates graph Laplacian regularization and uses a fastText-inspired initialization scheme.
result qDKT achieves state-of-the-art performance in predicting learner outcomes.
The paper tackles adaptive questioning to classify candidate ability.
problem Designing an optimal sequence of adaptive questions to classify candidate ability.
method Develops lower bounds and geometrical insights for sequential questioning strategies, and arrives at algorithms that match these bounds.
result Asymptotically, any candidate needs to be asked questions at most at two levels, facilitating endogenous exploration.
QAInfomax improves reading comprehension by maximizing mutual information, achieving state-of-the-art performance.
problem Distractor sentences in question answering datasets are hard to distinguish from relevant ones.
method QAInfomax regularizes reading comprehension models to learn mutual information among passages, questions, and answers.
result QAInfomax achieves state-of-the-art performance on Adversarial-SQuAD dataset.
New model improves diversity in question generation over text.
problem Inadequate evaluation metrics for diverse questions.
method Proposed a variational encoder-decoder model and evaluation scheme.
result Improved diversity in generated questions without loss of quality.
A new model answers questions about medical images.
problem Lack of transparency in deep learning models for medical imaging.
method A question-centric model that queries image models directly.
result The model achieves equal or higher accuracy than existing methods.
Enhanced ontology learning from text improves question-answering systems.
problem Improving ontology learning from unstructured text for better question-answering systems.
method Heuristically modified FP-Tree with DFA for concept extraction and frequent pattern mining for ontology learning.
result Our approach significantly improves question-answering system performance, answering 80% of questions compared to 28.4% with Text2Onto.
Enhanced QANet tackles unanswerable questions in MRC.
problem Improving performance on unanswerable questions in MRC.
method Extends QANet architecture to handle unanswerable questions through multi-task learning.
result EQuANt achieves close to 2x better performance on SQuAD 2 compared to baseline.
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learningcs.LG
Deep RL agent predicts malaria likelihood from household surveys.
problem Predicting malaria likelihood from household surveys.
method Deep Reinforcement Learning (DQN) agent learns to ask adaptive questions.
result 80% accuracy with 2.5 questions, vs. 6 questions for supervised learning.
Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queriescs.CL
Quriosity analyzes curiosity-driven questions from diverse sources.
problem Understanding and analyzing human curiosity-driven questions.
method Collection of 13.5K questions from various sources, development of an iterative prompt improvement framework.
result 42% of questions are causal, revealing unique linguistic properties.
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effectscs.LG
AdvReg improves VQA models but introduces instability and bias issues.
problem VQA models over-rely on linguistic biases, ignoring visual context.
method Adversarial regularization to encourage bias-free question representations.
result AdvReg yields side-effects like unstable gradients and reduced performance on in-domain examples.