Search Results for author:
Found 15 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.
Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection
We first pretrain these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality to avoid over-fitting, while encouraging them to capture complementary cues from those trained only on the RGB modality.
Are Deep Neural Networks SMARTer than Second Graders?
To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6--8 age group.
Cross-Domain Video Anomaly Detection without Target Domain Adaptation
Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain.
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data
In such cases, transferring knowledge from a neural network trained on a well-labeled large dataset in the source modality (RGB) to a neural network that works on a target modality (depth, infrared, etc.)
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition
Graph Convolutional Networks (GCNs) have been widely used to model the high-order dynamic dependencies for skeleton-based action recognition.
Iterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition
Pothole classification has become an important task for road inspection vehicles to save drivers from potential car accidents and repair bills.
ViewSynth: Learning Local Features from Depth using View Synthesis
By learning view synthesis, we explicitly encourage the feature extractor to encode information about not only the visible, but also the occluded parts of the scene.
Attention Guided Anomaly Localization in Images
Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information.
Ranked #74 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
Learning without Memorizing
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model.
Sharpen Focus: Learning with Attention Separability and Consistency
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.
Tell Me Where to Look: Guided Attention Inference Network
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients.
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data.
Zero-Shot Deep Domain Adaptation
Therefore, the source-domain task of interest solution (e. g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations.
A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image.
