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Found 8 papers, 6 papers with code
HAMMR: HierArchical MultiModal React agents for generic VQA
We start from a multimodal ReAct-based system and make it hierarchical by enabling our HAMMR agents to call upon other specialized agents.
Global Features are All You Need for Image Retrieval and Reranking
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking.
Ranked #1 on
Image Retrieval
on RParis (Hard)
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval.
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
Ranked #1 on
Landmark Recognition
on Google Landmarks Dataset v2 (recognition, validation)
(using extra training data)
Unifying Deep Local and Global Features for Image Search
Image retrieval is the problem of searching an image database for items that are similar to a query image.
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.
Recurrent Neural Networks for Person Re-identification Revisited
The task of person re-identification has recently received rising attention due to the high performance achieved by new methods based on deep learning.
Large-Scale Image Retrieval with Attentive Deep Local Features
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).
Ranked #2 on
Image Retrieval
on Oxf105k
