ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories. In this paper, we propose ConceptLearner, which is a scalable approach to discover visual concepts from weakly labeled image collections. Thousands of visual concept detectors are learned automatically, without human in the loop for additional annotation. We show that these learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately. Under domain-specific supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. ConceptLearner shows promising performance compared to fully supervised and weakly supervised methods.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionScene RecognitionSimilar Papers 제목 키워드 기반
Weakly supervised training of universal visual concepts for multi-domain semantic segmentation
Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on multiple datasets becomes a method of choice towards strong generalization in usual scenes and gracefu…
Semantic SegmentationOntological Learning from Weak Labels
Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts. In this work, we seek to investigate whether using thi…
Discovering containment: from infants to machines
Current artificial learning systems can recognize thousands of visual categories, or play Go at a champion"s level, but cannot explain infants learning, in particular the ability to learn complex concepts without guidanc…
Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery
We consider the task of visual net surgery, in which a CNN can be reconfigured without extra data to recognize novel concepts that may be omitted from the training set. While most prior work make use of linguistic cues f…
Novel ConceptsZero-Shot LearningLearning from Weakly-labeled Web Videos via Exploring Sub-Concepts
Learning visual knowledge from massive weakly-labeled web videos has attracted growing research interests thanks to the large corpus of easily accessible video data on the Internet. However, for video action recognition,…
Action RecognitionPseudo LabelTemporal Action Localization