Papers Object Counting
“Object Counting” 태그가 달린 논문 214편 · 필터 해제
VideoSSR: Video Self-Supervised Reinforcement Learning
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially advanced the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, the rapid progress of MLLMs is outpacing the com…
Reinforcement LearningObject CountingDecoupling What to Count and Where to See for Referring Expression Counting
Referring Expression Counting (REC) extends class-level object counting to the fine-grained subclass-level, aiming to enumerate objects matching a textual expression that specifies both the class and distinguishing attri…
Referring ExpressionObject CountingCountFormer: 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 CountingD2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation
Text-to-image (T2I) diffusion models have achieved strong performance in semantic alignment, yet they still struggle with generating the correct number of objects specified in prompts. Existing approaches typically incor…
Text-to-Image GenerationObject CountingPrompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation Reasoning
Spatial reasoning in large-scale 3D environments such as warehouses remains a significant challenge for vision-language systems due to scene clutter, occlusions, and the need for precise spatial understanding. Existing m…
Spatial ReasoningObject CountingHead-wise Adaptive Rotary Positional Encoding for Fine-Grained Image Generation
Transformers rely on explicit positional encoding to model structure in data. While Rotary Position Embedding (RoPE) excels in 1D domains, its application to image generation reveals significant limitations such as fine-…
Text-to-Image GenerationObject CountingCausality $\neq$ Decodability, and Vice Versa: Lessons from Interpreting Counting ViTs
Mechanistic interpretability seeks to uncover how internal components of neural networks give rise to predictions. A persistent challenge, however, is disentangling two often conflated notions: decodability--the recovera…
Object CountingGetting the Numbers Right$\unicode{x2014}$Modelling Multi-Class Object Counting in Dense and Varied Scenes
Density map estimation enables accurate object counting in heavily occluded, and densely packed scenes where detection-based counting fails. In multi-class density estimation, class awareness can be introduced by modelli…
Density EstimationObject CountingMITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance
General-domain large multimodal models (LMMs) have achieved significant advances in various image-text tasks. However, their performance in the Intelligent Traffic Surveillance (ITS) domain remains limited due to the abs…
Object LocalizationObject CountingVisual Representation Alignment for Multimodal Large Language Models
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tasks such as object counting or spatial re…
Spatial ReasoningObject CountingUAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning
Recent advances in vision-language models (VLMs) have demonstrated strong generalization in natural image tasks. However, their performance often degrades on unmanned aerial vehicle (UAV)-based aerial imagery, which feat…
Visual Question AnsweringReinforcement LearningVisual ReasoningObject CountingRSVLM-QA: A Benchmark Dataset for Remote Sensing Vision Language Model-based Question Answering
Visual Question Answering (VQA) in remote sensing (RS) is pivotal for interpreting Earth observation data. However, existing RS VQA datasets are constrained by limitations in annotation richness, question diversity, and …
Visual Question AnsweringObject CountingCountQA: How Well Do MLLMs Count in the Wild?
Multimodal Large Language Models (MLLMs) demonstrate remarkable fluency in understanding visual scenes, yet they exhibit a critical lack in a fundamental cognitive skill: object counting. This blind spot severely limits …
Object CountingYOLO-Count: Differentiable Object Counting for Text-to-Image Generation
We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is …
Text-to-Image GenerationObject CountingQuerying Autonomous Vehicle Point Clouds: Enhanced by 3D Object Counting with CounterNet
Autonomous vehicles generate massive volumes of point cloud data, yet only a subset is relevant for specific tasks such as collision detection, traffic analysis, or congestion monitoring. Effectively querying this data i…
Object LocalizationAutonomous VehiclesObject CountingPoint CloudsCar Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
In this paper, we investigate the applicability of the CLIP-EBC framework, originally designed for crowd counting, to car object counting using the CARPK dataset. Experimental results show that our model achieves second-…
ClusteringCrowd CountingObjectObject Counting+1Text-promptable Object Counting via Quantity Awareness Enhancement
Recent advances in large vision-language models (VLMs) have shown remarkable progress in solving the text-promptable object counting problem. Representative methods typically specify text prompts with object category inf…
Object CountingOmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
Spatial reasoning is a key aspect of cognitive psychology and remains a major bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or improve VLMs' understanding of basic s…
Object CountingSpatial ReasoningImproving Contrastive Learning for Referring Expression Counting
Object counting has progressed from class-specific models, which count only known categories, to class-agnostic models that generalize to unseen categories. The next challenge is Referring Expression Counting (REC), wher…
Contrastive LearningObject CountingReferring ExpressionInstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
Language-Guided object recognition in remote sensing imagery is crucial for large-scale mapping and automated data annotation. However, existing open-vocabulary and visual grounding methods rely on explicit category cues…
Earth ObservationObjectObject CountingObject Recognition+3