34 citations · 72 across the 12 of their papers we have counts for
12 papers
TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment
Bingyi Cao, Koert Chen, Kevis-Kokitsi Maninis +16
Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation…
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…
Learning Visual Composition through Improved Semantic Guidance
Austin Stone, Hagen Soltau, Robert Geirhos +6
Visual imagery does not consist of solitary objects, but instead reflects the composition of a multitude of fluid concepts. While there have been great advances in visual represent…
TIPS: Text-Image Pretraining with Spatial awareness
Kevis-Kokitsi Maninis, Kaifeng Chen, Soham Ghosh +11
While image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense und…
OmniGlue: Generalizable Feature Matching with Foundation Model Guidance
Hanwen Jiang, Arjun Karpur, Bingyi Cao +2
The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. Howe…
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…
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
Zu Kim, André Araujo, Bingyi Cao +5
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been f…
Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis
Zhengyao Lv, Xiaoming Li, Zhenxing Niu +2
Recent years have witnessed substantial progress in semantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previous method…
Towards A Fairer Landmark Recognition Dataset
Zu Kim, André Araujo, Bingyi Cao +5
We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible…
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao +1
While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing…
Unifying Deep Local and Global Features for Image Search
Bingyi Cao, Andre Araujo, Jack Sim
Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been s…