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Found 12 papers, 4 papers with code
Interactive Class-Agnostic Object Counting
The user can provide feedback by selecting a region with obvious counting errors and specifying the range for the estimated number of objects within it.
Zero-shot Object Counting
By applying this model to all the candidate patches, we can select the most suitable patches as exemplars for counting.
Ranked #4 on
Zero-Shot Counting
on FSC147
Exemplar Free Class Agnostic Counting
We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category.
Vicinal Counting Networks
Given an image containing multiple objects of a novel visual category and few exemplar bounding boxes depicting the visual category of interest, we want to count all of the instances of the desired visual category in the image.
Learning To Count Everything
We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.
Ranked #14 on
Object Counting
on FSC147
Uncertainty Estimation and Sample Selection for Crowd Counting
We present sample selection strategies which make use of the density and uncertainty of predictions from the networks trained on one domain to select the informative images from a target domain of interest to acquire human annotation.
A Study of Human Gaze Behavior During Visual Crowd Counting
Analyzing the collected gaze behavior of ten human participants on thirty crowd images, we observe some common approaches for visual counting.
Crowd Transformer Network
Most of the existing crowd counting approaches rely on local features for estimating the crowd density map.
Fake Sentence Detection as a Training Task for Sentence Encoding
We automatically generate fake sentences by corrupting original sentences from a source collection and train the encoders to produce representations that are effective at detecting fake sentences.
Iterative Crowd Counting
In this work, we tackle the problem of crowd counting in images.
Ranked #10 on
Crowd Counting
on UCF CC 50
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions.
Multi-Label Cross-Modal Retrieval
In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations.
