Refining Image Categorization by Exploiting Web Images and General Corpus
Studies show that refining real-world categories into semantic subcategories contributes to better image modeling and classification. Previous image sub-categorization work relying on labeled images and WordNet's hierarchy is not only labor-intensive, but also restricted to classify images into NOUN subcategories. To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories. The following two major challenges are well studied: 1) noise in the labels of subcategories derived from the general corpus; 2) noise in the labels of images retrieved from the web. Specifically, we first obtain the semantic refinement subcategories from the text perspective and remove the noise by the relevance-based approach. To suppress the search error induced noisy images, we then formulate image selection and classifier learning as a multi-class multi-instance learning problem and propose to solve the employed problem by the cutting-plane algorithm. The experiments show significant performance gains by using the generated data of our way on both image categorization and sub-categorization tasks. The proposed approach also consistently outperforms existing weakly supervised and web-supervised approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Image CategorizationSimilar Papers 제목 키워드 기반
Exploiting Temporal Coherence for Multi-modal Video Categorization
Multimodal ML models can process data in multiple modalities (e.g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e.g., object detection, scene understanding). In this pa…
object-detectionObject DetectionScene UnderstandingChannel Interaction Networks for Fine-Grained Image Categorization
Fine-grained image categorization is challenging due to the subtle inter-class differences.We posit that exploiting the rich relationships between channels can help capture such differences since different channels corre…
Image CategorizationMetric LearningA Rational Model of Dimension-reduced Human Categorization
Humans can categorize with only a few samples despite the numerous features. To mimic this ability, we propose a novel dimension-reduced category representation using a mixture of probabilistic principal component analyz…
Few-Shot LearningmodelZero-Shot LearningCapturing human categorization of natural images at scale by combining deep networks and cognitive models
Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have been contingent on small sets of simple, ar…
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To…
Contrastive LearningImage InpaintingMetric Learning