1 citations · 1 across the 3 of their papers we have counts for
3 papers
Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
Dror Aiger, Bingyi Cao, Kaifeng Chen +1
The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching…
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, Bingyi Cao +7
Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractica…
Global Features are All You Need for Image Retrieval and Reranking
Shihao Shao, Kaifeng Chen, Arjun Karpur +3
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking. However, the scalability of this…