Papers Exemplar-Free Counting
“Exemplar-Free Counting” 태그가 달린 논문 10편 · 필터 해제
CountFormer: A Transformer Framework for Learning Visual Repetition and Structure in Class-Agnostic Object Counting
Humans can often count unfamiliar objects by observing visual repetition and composition, rather than relying only on object categories. However, many exemplar-free counting models struggle in such situations and may ove…
Exemplar-Free CountingObject CountingSAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting
Zero-shot counting is a subcategory of Generic Visual Object Counting, which aims to count objects from an arbitrary class in a given image. While few-shot counting relies on delivering exemplars to the model to count si…
Exemplar-Free CountingObjectObject CountingZero-Shot CountingA Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appea…
Exemplar-Free CountingFew-shot Object Counting and DetectionObjectObject Counting+3GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting
Exemplar-Free Counting aims to count objects of interest without intensive annotations of objects or exemplars. To achieve this, we propose a Gated Context-Aware Swin-UNet (GCA-SUNet) to directly map an input image to th…
DecoderExemplar-FreeExemplar-Free CountingObject Counting+1DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting
Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the…
Exemplar-Free CountingFew-shot Object Counting and DetectionObjectObject Counting+1A Low-Shot Object Counting Network With Iterative Prototype Adaptation
We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries…
Exemplar-Free CountingObjectObject CountingObject LocalizationCounTR: Transformer-based Generalised Visual Counting
In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary semantic categories, using arbitrary numbe…
Exemplar-Free CountingObject CountingSelf-Supervised LearningLearning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision
Current class-agnostic counting methods can generalise to unseen classes but usually require reference images to define the type of object to be counted, as well as instance annotations during training. Reference-less cl…
Exemplar-Free CountingObject CountingExemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework based on parallel one-class classifiers (P…
class-incremental learningClass Incremental LearningExemplar-FreeExemplar-Free Counting+2Learning To Count Everything
Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any categor…
Exemplar-Free CountingObject Counting