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Found 14 papers, 5 papers with code
Long-Tailed Anomaly Detection with Learnable Class Names
Phase 2 then learns the parameters of the reconstruction and classification modules of LTAD.
ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
A new Prompt Tuning for Hierarchical Consistency (ProTeCt) technique is then proposed to calibrate classification across label set granularities.
Toward Unsupervised Realistic Visual Question Answering
To resolve the first drawback, we propose a new testing dataset, RGQA, which combines AQs from an existing VQA dataset with around 29K human-annotated UQs.
DISCO: Adversarial Defense with Local Implicit Functions
The problem of adversarial defenses for image classification, where the goal is to robustify a classifier against adversarial examples, is considered.
YORO -- Lightweight End to End Visual Grounding
We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task.
Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets
We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects.
OOWL500: Overcoming Dataset Collection Bias in the Wild
Second, it is used to show that the augmentation of in the wild datasets, such as ImageNet, with in the lab data, such as OOWL500, can significantly decrease these biases, leading to object recognizers of improved generalization.
Black-Box Test-Time Shape REFINEment for Single View 3D Reconstruction
Much recent progress has been made in reconstructing the 3D shape of an object from an image of it, i. e. single view 3D reconstruction.
Contrastive Learning with Adversarial Examples
This paper addresses the problem, by introducing a new family of adversarial examples for constrastive learning and using these examples to define a new adversarial training algorithm for SSL, denoted as CLAE.
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition
Multiview recognition has been well studied in the literature and achieves decent performance in object recognition and retrieval task.
A New Technique of Camera Calibration: A Geometric Approach Based on Principal Lines
Camera calibration is a crucial prerequisite in many applications of computer vision.
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks
Better results are achieved with real world data augmentation, but even this is not foolproof.
PIEs: Pose Invariant Embeddings
The new pose-invariant models are shown to have interesting properties, both theoretically and through experiments, where they outperform existing multiview approaches.
