Papers Object Counting
“Object Counting” 태그가 달린 논문 214편 · 필터 해제
Counting to Four is still a Chore for VLMs
Vision--language models (VLMs) have achieved impressive performance on complex multimodal reasoning tasks, yet they still fail on simple grounding skills such as object counting. Existing evaluations mostly assess only f…
Multimodal ReasoningAnswer GenerationObject CountingBoosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
Zero-shot object counting (ZSOC) aims to enumerate objects of arbitrary categories specified by text descriptions without requiring visual exemplars. However, existing methods often treat counting as a coarse retrieval t…
Object CountingAutomated Counting of Stacked Objects in Industrial Inspection
Visual object counting is a fundamental computer vision task in industrial inspection, where accurate, high-throughput inventory tracking and quality assurance are critical. Moreover, manufactured parts are often too lig…
Object CountingSVCBench: A Streaming Video Counting Benchmark for Spatial-Temporal State Maintenance
Video understanding requires models to continuously track and update world state during playback. Although existing benchmarks have advanced video understanding evaluation across multiple dimensions, they provide limited…
Object CountingModality Collapse as Mismatched Decoding: Information-Theoretic Limits of Multimodal LLMs
Numerous studies have shown that multimodal LLMs process speech and images well but fail in non-intuitive ways rendering trivial tasks such as object counting unreliable. We investigate this behavior from an information-…
Object CountingBootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting
Object counting is a fundamental task in computer vision, with broad applicability in many real-world scenarios. Fully-supervised counting methods require costly point-level annotations per object. Few weakly-supervised …
Object CountingVideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
The growing capability of video generation poses escalating security risks, making reliable detection increasingly essential. In this paper, we introduce VideoVeritas, a framework that integrates fine-grained perception …
Reinforcement LearningVideo GenerationObject CountingSITUATE -- Synthetic Object Counting Dataset for VLM training
We present SITUATE, a novel dataset designed for training and evaluating Vision Language Models on counting tasks with spatial constraints. The dataset bridges the gap between simple 2D datasets like VLMCountBench and of…
Object CountingForest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis
The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central challenges in this domain are pixel-level …
Change DetectionObject CountingDisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage assessment and response. While Visual Question Answering (VQA) has shown str…
Visual Question AnsweringObject CountingOCCAM: Class-Agnostic, Training-Free, Prior-Free and Multi-Class Object Counting
Class-Agnostic object Counting (CAC) involves counting instances of objects from arbitrary classes within an image. Due to its practical importance, CAC has received increasing attention in recent years. Most existing me…
Object CountingAECV-Bench: Benchmarking Multimodal Models on Architectural and Engineering Drawings Understanding
AEC drawings encode geometry and semantics through symbols, layout conventions, and dense annotation, yet it remains unclear whether modern multimodal and vision-language models can reliably interpret this graphical lang…
Spatial ReasoningObject CountingCoDi -- an exemplar-conditioned diffusion model for low-shot counting
Low-shot object counting addresses estimating the number of previously unobserved objects in an image using only few or no annotated test-time exemplars. A considerable challenge for modern low-shot counters are dense re…
Object CountingCountZES: Counting via Zero-Shot Exemplar Selection
Object counting in complex scenes is particularly challenging in the zero-shot (ZS) setting, where instances of unseen categories are counted using only a class name. Existing ZS counting methods that infer exemplars fro…
Object CountingAssessing the Visual Enumeration Abilities of Specialized Counting Architectures and Vision-Language Models
Counting the number of items in a visual scene remains a fundamental yet challenging task in computer vision. Traditional approaches to solving this problem rely on domain-specific counting architectures, which are train…
Object CountingObject Counting with GPT-4o and GPT-5: A Comparative Study
Zero-shot object counting attempts to estimate the number of object instances belonging to novel categories that the vision model performing the counting has never encountered during training. Existing methods typically …
Object CountingUnderstanding Task Transfer in Vision-Language Models
Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like depth estimation or object counting. Finetuning on one task can unpredictab…
Depth EstimationObject CountingCounting Through Occlusion: Framework for Open World Amodal Counting
Object counting has achieved remarkable success on visible instances, yet state-of-the-art (SOTA) methods fail under occlusion. This failure stems from a fundamental architectural limitation where backbone networks encod…
Object CountingCountSteer: Steering Attention for Object Counting in Diffusion Models
Text-to-image diffusion models generate realistic and coherent images but often fail to follow numerical instructions in text, revealing a gap between language and visual representation. Interestingly, we found that thes…
Text-to-Image GenerationObject CountingGeneralized-Scale Object Counting with Gradual Query Aggregation
Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple sizes is to merge backbone features of di…
Small Object DetectionObject Counting