Papers Object Localization
“Object Localization” 태그가 달린 논문 724편 · 필터 해제
Tail-Likelihood Reinforcement Learning
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward while having very different chances of prod…
Reinforcement LearningObject LocalizationWALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes
Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is absent, large vision-language models usually address this task. We ask …
Object LocalizationObject DetectionGrounding Isn't Knowing: Do VLMs Need Object Localization for Spatial Reasoning?
Vision-language models (VLMs) can answer spatial questions, yet the mechanisms connecting object grounding to spatial reasoning remain poorly understood. It is underexplored whether spatial reasoning internally requires …
Object LocalizationSpatial ReasoningDoomed to Re-Annotate, Forever: The ImageNet Story
Top-1 accuracy on ImageNet-1k remains the most commonly reported metric in visual recognition. Quality issues with the dataset have been repeatedly reported, yet the original 2012 noisy labels are still predominantly use…
Object LocalizationDRPFNet: Dual-domain Residual Progressive Fusion Network for RGB-Thermal Object Detection
RGB-thermal (RGB-T) object detection aims to fuse complementary information from visible and thermal modalities to achieve robust detection under varying illumination and weather conditions. Current methods typically emp…
Object LocalizationObject DetectionRadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models carry a high computational cost, and only few aim to be broadly applica…
3D Object DetectionObject LocalizationGeometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks
Bounding boxes are fundamental for object localization in visual detection tasks. Among them, oriented bounding boxes are widely used in visual detection tasks, which provide a more precise directional representation. Ge…
Semantic SimilarityObject LocalizationVocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval
Open-vocabulary object detection and segmentation aim to recognize arbitrary objects beyond predefined categories. Although recent vision-language and reference-based approaches have significantly advanced this field, th…
Object LocalizationObject DetectionExploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity
Unmanned aerial vehicle (UAV) target segmentation remains challenging due to the small size of objects, appearance variations, cluttered backgrounds, and the scarcity of densely annotated data. These factors hinder the p…
Computational EfficiencyObject LocalizationVision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics
State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal sequence modeling. While effective for sequential data, directional sca…
Instance SegmentationSemantic SegmentationObject LocalizationObject DetectionPersonalized Object Identification and Localization via In-Context Inference with Vision-Language Models
Personalized object localization (POL) localizes an object instance in a query image based on a few reference images with bounding-box annotations and a target object label. The pioneering method, IPLoc, solves this task…
Few-Shot Object DetectionObject LocalizationGeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision
Recent multimodal large language models (MLLMs) have shown strong cross-modal understanding and coordinate generation abilities in visual grounding. However, transferring these abilities to remote sensing visual groundin…
Object LocalizationVisual GroundingDirect Action-Head Injection of A Grounded 3D Point Unlocks Spatial and Task Generalization
Vision-Language-Action (VLA) models leverage large-scale vision-language pretraining for flexible robot manipulation, yet at test time they remain brittle along two axes: spatial generalization, when object positions dif…
Object LocalizationRobot ManipulationTACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments
Humans effortlessly locate and identify objects by touch alone, even without vision. In contrast, robotic systems rely heavily on vision and struggle with autonomous tactile exploration and object identification. We pres…
Object LocalizationUniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial preci…
Zero-shot GeneralizationObject LocalizationScene UnderstandingAutonomous DrivingFew-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precision and automation speed, helping users concentrate on higher-expertise …
Object LocalizationABACUS: 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 CountingMotion-Aware Reinforcement Learning For Object Localization
We present MARLNet (Motion-Aware Reinforcement Learning Network), a PPO-based bounding-box refinement agent that incorporates a constant-velocity motion prior into the observation state and an action smoothness penalty i…
Reinforcement LearningObject LocalizationFEMOT: Multi-Object Tracking using Frame and Event Cameras
Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information. However, their performance is often degraded under complex real-world chal…
Multi-Object TrackingObject LocalizationMaking Foresight Actionable: Repurposing Representation Alignment in World Action Models
World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a ph…
Object LocalizationRobot ManipulationVideo Generation