Papers Zero-Shot Counting
“Zero-Shot Counting” 태그가 달린 논문 18편 · 필터 해제
Expanding Zero-Shot Object Counting with Rich Prompts
Expanding pre-trained zero-shot counting models to handle unseen categories requires more than simply adding new prompts, as this approach does not achieve the necessary alignment between text and visual features for acc…
ObjectObject CountingZero-Shot CountingLearning What NOT to Count
Few/zero-shot object counting methods reduce the need for extensive annotations but often struggle to distinguish between fine-grained categories, especially when multiple similar objects appear in the same scene. To add…
Object CountingZero-Shot 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 CountingT2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting
Zero-shot object counting aims to count instances of arbitrary object categories specified by text descriptions. Existing methods typically rely on vision-language models like CLIP, but often exhibit limited sensitiv…
DenoisingObject CountingSensitivityZero-Shot CountingGCA-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+1Zero-shot Object Counting with Good Exemplars
Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lie…
Contrastive LearningObjectObject CountingZero-Shot CountingCountGD: Multi-Modal Open-World Counting
The goal of this paper is to improve the generality and accuracy of open-vocabulary object counting in images. To improve the generality, we repurpose an open-vocabulary detection foundation model (GroundingDINO) for the…
Object CountingOpen-vocabulary object countingZero-Shot CountingCountCLIP -- [Re] Teaching CLIP to Count to Ten
Large vision-language models (VLMs) are shown to learn rich joint image-text representations enabling high performances in relevant downstream tasks. However, they fail to showcase their quantitative understanding of obj…
zero-shot-classificationZero-Shot CountingZero-Shot LearningZero-shot counting with a dual-stream neural network model
Deep neural networks have provided a computational framework for understanding object recognition, grounded in the neurophysiology of the primate ventral stream, but fail to account for how we process relational aspects …
Object RecognitionZero-Shot CountingDAVE -- 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+1Enhancing Zero-shot Counting via Language-guided Exemplar Learning
Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC). This paper proposes a novel Expre…
Object CountingZero-Shot CountingZero-Shot LearningPoint Segment and Count: A Generalized Framework for Object Counting
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names a.k.a few-shot and zero-shot counting. In this paper we propose a generalized framework for both few-s…
Few-shot Object Counting and DetectionKnowledge DistillationObjectObject Counting+2VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies proposed a two-stage pipeline: discovering e…
DecoderObject CountingZero-Shot CountingT-Rex: Counting by Visual Prompting
We introduce T-Rex, an interactive object counting model designed to first detect and then count any objects. We formulate object counting as an open-set object detection task with the integration of visual prompts. User…
ObjectObject Countingobject-detectionObject Detection+2Point, Segment and Count: A Generalized Framework for Object Counting
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. In this paper, we propose a generalized framework for both …
Knowledge DistillationObjectObject CountingObject Localization+1Open-world Text-specified Object Counting
Our objective is open-world object counting in images, where the target object class is specified by a text description. To this end, we propose CounTX, a class-agnostic, single-stage model using a transformer decoder co…
DecoderObjectObject CountingZero-Shot CountingCLIP-Count: Towards Text-Guided Zero-Shot Object Counting
Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to downstream tasks such as object detection and segmentation. Adapting these models for object c…
Cross-Part Crowd CountingCross-Part EvaluationCrowd CountingObject+4Zero-shot Object Counting
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods require human-annotated exemplars as input…
ObjectObject CountingZero-Shot Counting