Search Results for author:
Found 25 papers, 7 papers with code
ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization
Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images.
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Self-Supervised Object Detection from Egocentric Videos
Understanding the visual world from the perspective of humans (egocentric) has been a long-standing challenge in computer vision.
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction
Many online action prediction models observe complete frames to locate and attend to informative subregions in the frames called glimpses and recognize an ongoing action based on global and local information.
Task Grouping for Multilingual Text Recognition
Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets.
Few-shot Learning with Noisy Labels
Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise.
Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
We study the challenging incremental few-shot object detection (iFSD) setting.
Extending One-Stage Detection with Open-World Proposals
In many applications, such as autonomous driving, hand manipulation, or robot navigation, object detection methods must be able to detect objects unseen in the training set.
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks
Few-shot learning (FSL) methods aim to generalize a model to new unseen classes using only a small number of support examples.
Towards Fair Federated Learning with Zero-Shot Data Augmentation
Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data.
A Multiplexed Network for End-to-End, Multilingual OCR
Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results.
Efficient Feature Transformations for Discriminative and Generative Continual Learning
However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so.
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
During the online phase of the attack, we then leverage representations of highly related proxy classes from the whitebox distribution to fool the blackbox model into predicting the desired target class.
MixKD: Towards Efficient Distillation of Large-scale Language Models
Large-scale language models have recently demonstrated impressive empirical performance.
Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images
The data sources described earlier make two "domains": a hand-collected data domain of images with threats, and a real-world domain of images assumed without threats.
WAFFLe: Weight Anonymized Factorization for Federated Learning
Experiments on MNIST, FashionMNIST, and CIFAR-10 demonstrate WAFFLe's significant improvement to local test performance and fairness while simultaneously providing an extra layer of security.
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers.
Transferable Perturbations of Deep Feature Distributions
Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network.
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable.
Object Detection as a Positive-Unlabeled Problem
These missing annotations can be problematic, as the standard cross-entropy loss employed to train object detection models treats classification as a positive-negative (PN) problem: unlabeled regions are implicitly assumed to be background.
Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection
We show performance of our models on held-out evaluation sets, analyze several design parameters, and demonstrate the potential of such systems for automated detection of threats that can be found in airports.
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.
Generative Adversarial Network Training is a Continual Learning Problem
We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator.
