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Found 14 papers, 2 papers with code
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels.
A Constructive Prediction of the Generalization Error Across Scales
In this work, we present a functional form which approximates well the generalization error in practice.
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Predicting human perceptual similarity is a challenging subject of ongoing research.
The Elephant in the Room
We showcase a family of common failures of state-of-the art object detectors.
Totally Looks Like - How Humans Compare, Compared to Machines
Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations.
Bridging Cognitive Programs and Machine Learning
While great advances are made in pattern recognition and machine learning, the successes of such fields remain restricted to narrow applications and seem to break down when training data is scarce, a shift in domain occurs, or when intelligent reasoning is required for rapid adaptation to new environments.
Challenging Images For Minds and Machines
There is no denying the tremendous leap in the performance of machine learning methods in the past half-decade.
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing
The implications of this intriguing property of deep neural networks are discussed and we suggest ways to harness it to create more robust representations.
Priming Neural Networks
This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue.
Incremental Learning Through Deep Adaptation
Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned.
Action Classification via Concepts and Attributes
Classes in natural images tend to follow long tail distributions.
Visual Concept Recognition and Localization via Iterative Introspection
Convolutional neural networks have been shown to develop internal representations, which correspond closely to semantically meaningful objects and parts, although trained solely on class labels.
Face-space Action Recognition by Face-Object Interactions
Action recognition in still images has seen major improvement in recent years due to advances in human pose estimation, object recognition and stronger feature representations.
Hand-Object Interaction and Precise Localization in Transitive Action Recognition
In this paper we demonstrate how recognition is improved by obtaining precise localization of the action-object and consequently extracting details of the object shape together with the actor-object interaction.
