SOTAVerified

Instance Search

Visual Instance Search is the task of retrieving from a database of images the ones that contain an instance of a visual query. It is typically much more challenging than finding images from the database that contain objects belonging to the same category as the object in the query. If the visual query is an image of a shoe, visual Instance Search does not try to find images of shoes, which might differ from the query in shape, color or size, but tries to find images of the exact same shoe as the one in the query image. Visual Instance Search challenges image representations as the features extracted from the images must enable such fine-grained recognition despite variations in viewpoints, scale, position, illumination, etc. Whereas holistic image representations, where each image is mapped to a single high-dimensional vector, are sufficient for coarse-grained similarity retrieval, local features are needed for instance retrieval.

Source: Dynamicity and Durability in Scalable Visual Instance Search

Papers

Showing 110 of 29 papers

TitleStatusHype
TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domainsCode1
Data-efficient End-to-end Information Extraction for Statistical Legal AnalysisCode1
Class-Weighted Convolutional Features for Visual Instance SearchCode0
GlobalTrack: A Simple and Strong Baseline for Long-term TrackingCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
The Effect of Points Dispersion on the k-nn Search in Random Projection ForestsCode0
Faster R-CNN Features for Instance SearchCode0
Bags of Local Convolutional Features for Scalable Instance SearchCode0
Saliency Weighted Convolutional Features for Instance SearchCode0
Compressive Quantization for Fast Object Instance Search in Videos0
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