9 papers with code • 0 benchmarks • 1 datasets
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
These leaderboards are used to track progress in Instance Search
Most implemented papers
Faster R-CNN Features for Instance Search
This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN.
Bags of Local Convolutional Features for Scalable Instance Search
This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW).
Class-Weighted Convolutional Features for Visual Instance Search
In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image.
Saliency Weighted Convolutional Features for Instance Search
This work explores attention models to weight the contribution of local convolutional representations for the instance search task.
Instance Search via Instance Level Segmentation and Feature Representation
In addition, the proposed enhancement on the network structure also shows superior performance on the instance segmentation task.
GlobalTrack: A Simple and Strong Baseline for Long-term Tracking
Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales.
Confidence-Aware Active Feedback for Interactive Instance Search
To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks.
Data-efficient End-to-end Information Extraction for Statistical Legal Analysis
Lawyers, for instance, search for appropriate precedents favorable to their clients, while the number of legal precedents is ever-growing.
The Effect of Points Dispersion on the $k$-nn Search in Random Projection Forests
$k$-nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest.