Contrastive Object Detection Using Knowledge Graph Embeddings
Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated. Each image region has to be assigned to one member of a set of objects, including a background class, disregarding any similarities in the object types. In this work, we compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs that are widely applied in open world object detection. Extensive experimental results on multiple knowledge-embeddings as well as distance metrics indicate that knowledge-based class representations result in more semantically grounded misclassifications while performing on par compared to one-hot methods on the challenging COCO and Cityscapes object detection benchmarks. We generalize our findings to multiple object detection architectures by proposing a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge Graph EmbeddingsKnowledge GraphsObjectobject-detectionObject DetectionObject RecognitionOpen World Object DetectionSimilar Papers 제목 키워드 기반
Adversarial Contrastive Estimation
Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in …
Contrastive LearningKnowledge Graph EmbeddingsKnowledge GraphsLearning Word Embeddings+1LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction r…
Self-Supervised LearningContrastive LearningGraph EmbeddingFraud DetectionAutomatic Biomedical Term Clustering by Learning Fine-grained Term Representations
Term clustering is important in biomedical knowledge graph construction. Using similarities between terms embedding is helpful for term clustering. State-of-the-art term embeddings leverage pretrained language models to …
ClusteringContrastive Learninggraph constructionKnowledge GraphsCombining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language
Word embeddings play a significant role in today's Natural Language Processing tasks and applications. While pre-trained models may be directly employed and integrated into existing pipelines, they are often fine-tuned t…
Contrastive LearningGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
Contrastive learning has demonstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings and then apply clustering algorithms, su…
Representation LearningContrastive Learning