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Found 45 papers, 18 papers with code
S4T: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training
Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints.
RobustNav: Towards Benchmarking Robustness in Embodied Navigation
As an attempt towards assessing the robustness of embodied navigation agents, we propose RobustNav, a framework to quantify the performance of embodied navigation agents when exposed to a wide variety of visual - affecting RGB inputs - and dynamics - affecting transition dynamics - corruptions.
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label distribution shift.
Temporal Action Detection with Multi-level Supervision
We extensively benchmark against the baselines for SSAD and OSAD on our created data splits in THUMOS14 and ActivityNet1. 2, and demonstrate the effectiveness of the proposed UFA and IB methods.
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
We study the problem of AL under a domain shift, called Active Domain Adaptation (Active DA).
Auxiliary Task Reweighting for Minimum-data Learning
By adjusting the auxiliary task weights to minimize the divergence between the surrogate prior and the true prior of the main task, we obtain a more accurate prior estimation, achieving the goal of minimizing the required amount of training data for the main task and avoiding a costly grid search.
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy.
Ranked #5 on
Robot Navigation
on Habitat 2020 Point Nav test-std
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain.
Ranked #2 on
Domain Generalization
on DomainNet
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally occurring and of low probability.
TIDE: A General Toolbox for Identifying Object Detection Errors
We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms.
Analyzing Visual Representations in Embodied Navigation Tasks
Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task.
Representation Learning Through Latent Canonicalizations
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision.
Insights on Visual Representations for Embodied Navigation Tasks
Surprisingly, we find that slight differences in task have no measurable effect on the visual representation for both SqueezeNet and ResNet architectures.
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.
Robust Learning with Jacobian Regularization
Design of reliable systems must guarantee stability against input perturbations.
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation
We propose SplitNet, a method for decoupling visual perception and policy learning.
Predictive Inequity in Object Detection
In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones.
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
In this paper, we present a new large-scale benchmark called Syn2Real, which consists of a synthetic domain rendered from 3D object models and two real-image domains containing the same object categories.
Algorithms and Theory for Multiple-Source Adaptation
This work includes a number of novel contributions for the multiple-source adaptation problem.
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a data efficient manner.
Label Efficient Learning of Transferable Representations across Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner.
Multiple-Source Adaptation for Regression Problems
We present a detailed theoretical analysis of the problem of multiple-source adaptation in the general stochastic scenario, extending known results that assume a single target labeling function.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Domain adaptation is critical for success in new, unseen environments.
VisDA: The Visual Domain Adaptation Challenge
We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach
While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild.
Inferring and Executing Programs for Visual Reasoning
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
Ranked #5 on
Visual Question Answering
on CLEVR-Humans
Adversarial Discriminative Domain Adaptation
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
Ranked #2 on
Image-to-Image Translation
on SYNTHIA Fall-to-Winter
Clockwork Convnets for Video Semantic Segmentation
Recent years have seen tremendous progress in still-image segmentation; however the na\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video.
Learning With Side Information Through Modality Hallucination
Thus, our method transfers information commonly extracted from depth training data to a network which can extract that information from the RGB counterpart.
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification
We address the difficult problem of distinguishing fine-grained object categories in low resolution images.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.
Quantification in-the-wild: data-sets and baselines
Quantification is the task of estimating the class-distribution of a data-set.
Spatial Semantic Regularisation for Large Scale Object Detection
Our approach proves to be especially useful in large scale settings with thousands of classes, where spatial and semantic interactions are very frequent and only weakly supervised detectors can be built due to a lack of bounding box annotations.
Simultaneous Deep Transfer Across Domains and Tasks
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias.
Cross Modal Distillation for Supervision Transfer
In this work we propose a technique that transfers supervision between images from different modalities.
Deep Domain Confusion: Maximizing for Domain Invariance
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark.
Ranked #6 on
Domain Adaptation
on Office-Caltech
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
We develop methods for detector learning which exploit joint training over both weak and strong labels and which transfer learned perceptual representations from strongly-labeled auxiliary tasks.
LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories.
Continuous Manifold Based Adaptation for Evolving Visual Domains
The classic domain adaptation paradigm considers the world to be separated into stationary domains with clear boundaries between them.
One-Shot Adaptation of Supervised Deep Convolutional Models
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Images seen during test time are often not from the same distribution as images used for learning.
Semi-supervised Domain Adaptation with Instance Constraints
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets.
Efficient Learning of Domain-invariant Image Representations
We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.