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Found 19 papers, 1 papers with code
Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts
Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-by-batch manner during inference.
Progressive Random Convolutions for Single Domain Generalization
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains.
Few-Shot Common Action Localization via Cross-Attentional Fusion of Context and Temporal Dynamics
In this task, it is crucial to mine reliable temporal cues representing a common action from handful support videos.
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the performance degradation due to the distribution shift between the source and target domains.
Domain Agnostic Few-shot Learning for Speaker Verification
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features.
Distribution Estimation to Automate Transformation Policies for Self-Supervision
However, it is observed that image transformations already present in the dataset might be less effective in learning such self-supervised representations.
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning
The first step of our framework trains a feature extracting backbone with the contrastive loss on the base category data.
Federated Learning of User Verification Models Without Sharing Embeddings
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
Prototype-based Personalized Pruning
We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
SubSpectral Normalization for Neural Audio Data Processing
In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.
Ranked #1 on
Keyword Spotting
on TAU Urban Acoustic Scenes 2019
Secure Federated Learning of User Verification Models
We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization
Temporally localizing actions in videos is one of the key components for video understanding.
Efficient Action Recognition via Dynamic Knowledge Propagation
Efficient action recognition has become crucial to extend the success of action recognition to many real-world applications.
Federated Learning of User Authentication Models
In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.
End-to-End Lane Marker Detection via Row-wise Classification
In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task.
Ranked #19 on
Lane Detection
on TuSimple
Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning.
Acoustic Scene Classification Based on a Large-margin Factorized CNN
With this loss function, the samples from the same audio scene are clustered independently of the environment, and thus we can get the classifier with better generalization ability in an unseen environment.
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network
In training our speaker verification framework, we consider both the triplet loss minimization and adversarial gradient of the ASR network to obtain more discriminative and text-independent speaker embedding vectors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Phoneme Classification using Constrained Variational Gaussian Process Dynamical System
This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification.
