Search Results for author:
Found 10 papers, 4 papers with code
Self-Supervised Multi-Task Procedure Learning from Instructional Videos
We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs).
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval
Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification.
A recommender for the management of chronic pain in patients undergoing spinal cord stimulation
Spinal cord stimulation (SCS) is a therapeutic approach used for the management of chronic pain.
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images.
Compositional Fine-Grained Low-Shot Learning
In addition, instead of building holistic features for classes, we use our attribute features to form dense representations capable of capturing fine-grained attribute details of classes.
Ranked #2 on
Zero-Shot Learning
on CUB-200-2011
Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations
We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some interactions do not have training images.
Human-Object Interaction Detection
Multi-label zero-shot learning
+1
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
We propose a feature composition framework that learns to extract attribute-based features from training samples and combines them to construct fine-grained features for unseen classes.
Interactive Multi-Label CNN Learning With Partial Labels
Given that optimizing the new loss function over the CNN parameters requires learning similarities among labels and images, which itself depends on knowing the parameters of the CNN, we develop an efficient interactive learning framework in which the two steps of similarity learning and CNN training interact and improve the performance of each another.
A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning
Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of shared attentions which are trained to be label-agnostic and to focus on only relevant/foreground regions through our novel loss.
Ranked #4 on
Multi-label zero-shot learning
on Open Images V4
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention
We address the problem of fine-grained generalized zero-shot recognition of visually similar classes without training images for some classes.
