Papers Object Counting
“Object Counting” 태그가 달린 논문 214편 · 필터 해제
Counting Beyond Instances: A Benchmark for Group-Individual Object Counting
Visual counting is commonly formulated at the instance level, aiming to estimate how many objects of a queried category appear in an image. However, real-world counting often involves higher-level semantic units formed b…
Object CountingDepth-Guided Video Object Counting in Crowded Scenes
Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts. Existing methods rely on RGB information, li…
Object CountingSpatially-Aware Class-Agnostic Object Counting
Generalised object counting aims to estimate the number of instances of an arbitrary object category from a single image, but many recent methods can struggle on structurally complex objects due to limited spatial modell…
Object CountingThe Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs
Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting. We investigate whether this reflects missing internal knowledge or a gap between internal repr…
Object CountingAdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting
Zero-shot object counting (ZOC) aims to count instances of arbitrary object categories specified only through textual prompts. Recent training-free approaches leverage foundation models such as SAM to reformulate countin…
Object CountingCan AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models
Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts. Yet existing …
Object CountingTriViewBench: Controlled Complexity Scaling for Multi-View Structural Reasoning in MLLMs
Multimodal Large Language Models (MLLMs) demonstrate strong performance on standard visual question answering benchmarks, yet their scalability under controlled structural complexity remains poorly understood. We introdu…
Visual Question AnsweringVisual ReasoningObject CountingABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is bu…
Object LocalizationImage GenerationObject CountingCrowd CountingVTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers
Vision foundation tools such as open-vocabulary detectors, segmentation models, and post-processing operators are powerful building blocks for computer vision, but their effectiveness depends heavily on how they are orch…
Domain GeneralizationObject CountingRT-Counter: Real-Time Text-Guided Open-Vocabulary Object Counting
Text-guided open-vocabulary object counting (TOOC) aims to count objects belonging to the categories specified by natural language descriptions. Although vision-language pre-trained models have been successful applied to…
Computational EfficiencyObject CountingTest-Time Training for Robust Text-Guided Open-Vocabulary Object Counting
Text-guided Open-vocabulary Object Counting (TOOC) enables counting arbitrary object categories specified by text prompts, offering substantially greater flexibility than conventional closed-set counting. However, existi…
Object CountingMambaCount: Efficient Text-guided Open-vocabulary Object Counting with Spatial Sparse State Space Duality Block
Text-guided Open-vocabulary Object Counting (TOOC) aims to estimate the number of objects described by text prompts, which is particularly challenging in dense scenes with large scale variations. Existing TOOC approaches…
Object CountingCount Anything
Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing counting models are often tailored to scenarios such as crowds, vehic…
Domain GeneralizationObject CountingArchSIBench: Benchmarking the Architectural Spatial Intelligence of Vision-Language Models
Architectural spatial intelligence, the ability to recognize and infer architectural space, is fundamental to tasks such as robot navigation, embodied interaction, and 3D scene understanding and generation. Although exte…
Scene UnderstandingRobot NavigationObject CountingClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison
Long-form image captioning exposes a reward granularity problem in RL: captions are judged as whole sequences, while the important errors occur at the level of individual visual claims. A good dense caption should be bot…
Reinforcement LearningScene RecognitionImage CaptioningObject CountingThe MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting
Object counting is a foundational vision task with over a decade of dedicated research, yet state-of-the-art models still fail systematically in the mixed-object setting that dominates real-world applications such as ind…
Object CountingBeyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We study this challenge as Perspective-Conditioned Spatial Reasoning (PCSR) i…
Spatial ReasoningObject CountingCount Anything at Any Granularity
Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far from solved. We argue that a central reason is that counting granul…
Object CountingImage EditingSketchVLM: Vision language models can annotate images to explain thoughts and guide users
When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro and GPT-5 only respond with text, which ca…
Trajectory PredictionVisual ReasoningObject CountingLearning to count small and clustered objects with application to bacterial colonies
Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. However, bacterial colonies present unique machine vision challenges that…
Feature EngineeringObject Counting