A deep historical LSTM model improves tennis shot recognition from RGB videos.
problem Improving action recognition from RGB video input for sports analysis.
method Convolutional Neural Network (CNN) and Weighted Long Short-Term Memory (LSTM) for 3D tennis shot recognition.
result The method achieves better performance than state-of-the-art baselines for tennis shot recognition.
Memory-enhanced model predicts tennis shots based on player history.
problem Predicting shot location and type in tennis.
method Semi-supervised Generative Adversarial Network with neural memory modules.
result The model learns player-specific behavioral patterns from match data.
Wearables like smartwatches which are embedded with sensors and powerful processors, provide a strong platform for development of analytics solutions in sports domain. To analyze players' games, while motion sensor based shot detection has been extensively studied in sports like Tennis, Golf, Baseball; Table Tennis and…
With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samp…
Paper tackles zero-shot activity recognition using video features and text embeddings.
problem Zero-shot activity recognition with videos.
method Auto-encoder model for multimodal joint embedding, 3D convolutional action recognition for visual features, GloVe word embeddings for textual features.
result Improved zero-shot recognition results with top-n accuracy and mean Nearest Neighbor Overlap.
Paper proposes redundancy-free features for zero-shot object recognition.
problem Redundant visual features degrade zero-shot object recognition.
method Project original features into a new, statistically independent space.
result RFF-GZSL achieves competitive results on benchmark datasets.
Paper introduces privacy-preserving few-shot learning for images.
problem Privacy risk in few-shot learning systems.
method Discrete embedding vectors and one-way hash functions.
result Achieves computational pan privacy without storing embeddings.
This research tackles few-shot video action recognition, improving accuracy with a two-stream setup.
problem Few-shot video action recognition with limited labeled examples.
method Two-stream models combining convolutional and recurrent neural network video encoders with metric-based few-shot algorithms.
result The setup achieves 84.2% accuracy on a 5-shot 5-way task, outperforming other methods.
Paper proposes a few-shot learning method for human activity recognition.
problem Efficiently recognize human activities with limited labeled data.
method Uses deep learning for feature extraction and knowledge transfer from existing models.
result Promising results show the proposed method's advantages over traditional approaches.
Random forest model predicts tennis match outcomes with 80% accuracy.
problem Predicting tennis match outcomes before the game starts.
method Used a large database of tennis match information and a random forest model.
result Identified serve strength as a key predictor of match outcome.
Few-shot NER classification with limited data.
problem Limited annotated data for Named Entity Recognition.
method Prototypical Network + Transfer Learning
result Well-performing classifiers trained on only 20 instances.
Improves few-shot learning for real-world recognition with novel methods.
problem Challenges in real-world recognition with heavy-tailed class distributions and cluttered scenes.
method Parameter-free improvements including better training procedures, object localization, and feature space expansion.
result Doubles accuracy of state-of-the-art models on meta-iNat while generalizing to diverse settings.
Meta-learning improves few-shot acoustic event detection.
problem Detecting new audio events with limited labeled data.
method Formulated few-shot AED problem; explored supervised and meta-learning approaches.
result Meta-learning achieves superior performance in few-shot AED.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
problem Zero-shot recognition of environments from object distributions.
method Bayesian framework using metric and self-supervised learning models to estimate object distributions in latent space.
result OBSER framework reliably performs inference in open-world and photorealistic environments, outperforming scene-based methods.
Survey of open set recognition techniques and their limitations.
problem Recognition tasks with unknown classes during testing.
method Comprehensive review of techniques, datasets, and evaluation criteria.
result Highlighting the limitations and future directions in open set recognition.
Two-stage neural network for few-shot image recognition.
problem Few-shot image recognition for novel categories.
method Multi-layer neural network with feature extraction and classification stages.
result Competitive performance on four standard datasets.
Improved speaker recognition with deep metric learning.
problem Performance gap between training and unseen speakers.
method Optimized speaker embedding model with prototypical network loss (PNL).
result Outperforms state-of-the-art models in speaker verification and identification.
Object recognition systems usually require fully complete manually labeled training data to train the classifier. In this paper, we study the problem of object recognition where the training samples are missing during the classifier learning stage, a task also known as zero-shot learning. We propose a novel zero-shot l…
Robotic table tennis learns efficient policies to return balls at 100Hz.
problem Developing efficient robotic table tennis strategies.
method Model-free reinforcement learning using evolutionary search on CNN-based policies.
result Robots can develop multi-modal styles (forehand and backhand) with 80% return rate.
Adaptive adjustment of semantic feature space improves zero-shot recognition.
problem Domain shift and hubness problems in zero-shot recognition.
method Adaptive adjustment of semantic feature space.
result Remarkable performance improvement compared to existing methods.
A new ranking model uses nonnegative matrix factorization for tennis players.
problem Modeling latent variables influencing tennis player performance.
method Combines Bradley-Terry-Luce model with nonnegative matrix factorization.
result Model identifies surface type as key determinant of male player performance.
Paper proposes a new process for GZSL improving recognition performance on unseen classes.
problem Maximizing recognition performance on unseen classes in ZSL.
method Proposes a new training and evaluation process that penalizes samples from seen classes to improve performance on unseen classes.
result Significant performance boost on CUB and AwA2 datasets.
Improved deep learning for one-shot and open-set classification using alignment-based matching.
problem Limited data for one-shot classification and open-set recognition.
method Aligns images to reference images for classification, learns alignment mechanism.
result Significantly improved classification accuracy (e.g., 1.4% error rate in Omniglot, 46.5% in MiniImageNet).
Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to r…
Survey on DNNs for speech processing, focusing on limited data challenges.
problem Challenges in training DNNs for speech tasks with limited data.
method Overview of techniques for few-shot speech processing.
result Promising few-shot techniques for speech processing.
GANs improve VSR accuracy by generating unseen classes.
problem Scarcity of training data for unseen classes in VSR.
method Generative Adversarial Networks (GANs) to generate new class samples.
result Accuracy increased by 27% for unseen classes.
Context-aware ZSL improves object recognition by considering object context.
problem Previous ZSL approaches ignore object context, limiting their effectiveness.
method Proposes a new approach that models the conditional likelihood of objects appearing in specific contexts.
result Contextual information significantly improves ZSL performance and is robust to class imbalance.
Deep Triplet Networks improve brain imaging modality recognition with limited data.
problem Efficiently recognizing new imaging modalities with scarce training data.
method Few-shot learning model based on Deep Triplet Networks.
result The model outperforms traditional CNN classifiers in modality recognition with limited data.
In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it posses…
Two approaches use TDA and graph theory for tennis match prediction.
problem Predicting tennis match outcomes using network features.
method Lower-star filtration on player competitive networks, Random Forest model, modified Katz similarity index.
result TDA features alone can achieve above-chance prediction in tennis match outcomes.
Meta Omnium benchmarks few-shot learning across diverse vision tasks.
problem Evaluating generalization of few-shot learning across multiple vision tasks.
method Introduction of Meta Omnium dataset and evaluation of meta-learning algorithms.
result Meta-learning algorithms can generalize across diverse vision tasks.
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it …
Gen1S learns novel classes with 1-shot data using residual space and generative models.
problem Learning new classes with limited data in a growing dataset.
method Mapping embeddings to a residual space, using generative models to learn multi-modal distribution, and applying it as a structural prior.
result Consistent improvement over state-of-the-art methods in recognizing novel classes.
IZF uses flow-based models to overcome ZSL limitations.
problem Hardness of training ZSL models and limited generation quality.
method IZF incorporates flow-based models to learn factorized data embeddings and generates samples.
result IZF significantly outperforms existing methods on ZSL benchmarks.
Generative classifiers show surprising human-like performance.
problem Comparing generative and discriminative models for object recognition.
method Built on recent advances in generative modeling to create classifiers and compared them to discriminative models.
result Generative classifiers outperform discriminative models in several key areas, including shape bias and out-of-distribution accuracy.
Theoretical analysis improves few-shot learning performance.
problem Optimizing the number of labeled examples per category in few-shot learning.
method Theoretical analysis of Prototypical Networks, proposing a robust method to the shot number.
result Model trained for arbitrary meta-training shot number performs well across different meta-testing shot numbers.
This paper proposes a unified framework for recognizing seen and unseen classes using visual and semantic prototypes.
problem Class overfitting and misclassification of unseen classes in zero-shot learning.
method Decomposes G-ZSL into OSR and ZSL, introduces semantic side-information for OSR, and uses a VSG-CNN framework.
result Improves recognition performance and cognitive ability for unknown classes.
The paper shows how continuous attention can lead to better object representations.
problem Learning good representations from data without labeled examples.
method Integrating unsupervised learning and reinforcement learning with intrinsic motivation.
result The proposed algorithm improves object recognition in few-shot settings.
Paper uses SSD to detect miners' activities in a mining environment.
problem Tracking miners' activities in a mining environment with little obstruction.
method Used SSD trained on COCO dataset to detect miners' activities. Implemented machine learning algorithms using Tensorflow and C++.
result Improved accuracy of detecting miners' activities through data fusion.
Wearable devices have the potential to enhance sports performance, yet they are not fulfilling this promise. Our previous studies with 6 professional tennis coaches and 20 players indicate that this could be due the lack of psychological or mental state feedback, which the coaches claim to provide. Towards this end, we…
New online few-shot learning model for context-aware recognition.
problem Few-shot learning in online, continuous settings with spatiotemporal context.
method Proposed new dataset and online versions of existing few-shot learning approaches.
result Contextual prototypical memory model improves performance.
Improved NER in medical text with few examples.
problem Limited annotated examples for NER in medical texts.
method Layer-wise initialization, hyperparameter tuning, pre-training data, custom word embeddings, optimizing OOV words.
result F1 score improved from 69.3% to 78.87%.
A new dataset for few-shot relation classification challenges current models.
problem Few-shot relation classification is an open problem requiring further research.
method Adapted state-of-the-art few-shot learning methods for relation classification.
result Current models struggle with relation classification, especially compared to humans.
Unified BERT model improves NER across multiple languages.
problem Language-specific NER models limit data extraction.
method Jointly trained multilingual BERT with regularization.
result Unified model outperforms monolingual models on various datasets.
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
problem Learning new concepts without examples, especially in medical imaging.
method Uses existing knowledge and auxiliary information to predict unknown concepts.
result Effective in diagnosing COVID-19 from chest X-rays.
Paper proposes LMM-PQS for cross-domain few-shot learning.
problem Cross-domain few-shot learning problem.
method Generates pseudo query images and fine-tunes feature extraction modules with a large margin mechanism.
result LMM-PQS outperforms baseline models in cross-domain few-shot learning.
Optimal transport framework for zero-shot learning.
problem Generalized zero-shot learning of unseen classes.
method Conditional generative model and optimal transport between generated and real features.
result Optimal transport-based method outperforms state-of-the-art methods.
Plex improves model reliability across vision and language tasks.
problem Improving model reliability in diverse decision-making tasks involving uncertainty and adaptation.
method Developed ViT-Plex and T5-Plex pretrained model extensions to evaluate and improve reliability across 40 datasets.
result Plex greatly improves state-of-the-art across reliability tasks, simplifying evaluation and performance.