Papers Open World Object Detection
“Open World Object Detection” 태그가 달린 논문 55편 · 필터 해제
CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection
Open World Object Detection (OWOD) built on multimodal foundation models often suffers from semantic ambiguity caused by unidirectional text-to-vision matching, while rigid outlier penalties may over-suppress unknown obj…
Open World Object DetectionREAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling
Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories, while preserving previously acquired knowledge. …
Open World Object DetectionDetecting Unknown Objects via Energy-based Separation for Open World Object Detection
In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without forgetting while identifying unknown obj…
Open World Object DetectionImplicit Non-Causal Factors are Out via Dataset Splitting for Domain Generalization Object Detection
Open world object detection faces a significant challenge in domain-invariant representation, i.e., implicit non-causal factors. Most domain generalization (DG) methods based on domain adversarial learning (DAL) pay much…
Open World Object DetectionDomain GeneralizationData AugmentationTowards Open World Detection: A Survey
For decades, Computer Vision has aimed at enabling machines to perceive the external world. Initial limitations led to the development of highly specialized niches. As success in each task accrued and research progressed…
Open World Object DetectionSaliency DetectionDecoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection
Open World Object Detection (OWOD) is a challenging computer vision task that extends standard object detection by (1) detecting and classifying unknown objects without supervision, and (2) incrementally learning new obj…
object-detectionObject DetectionOpen World Object DetectionSAM2Auto: Auto Annotation Using FLASH
Vision-Language Models (VLMs) lag behind Large Language Models due to the scarcity of annotated datasets, as creating paired visual-textual annotations is labor-intensive and expensive. To address this bottleneck, we int…
Instance SegmentationObjectobject-detectionObject Detection+5VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion
Current perception models have achieved remarkable success by leveraging large-scale labeled datasets, but still face challenges in open-world environments with novel objects. To address this limitation, researchers intr…
Denoisingobject-detectionObject DetectionOpen World Object DetectionOpen-World Objectness Modeling Unifies Novel Object Detection
The challenge in open-world object detection, similarly to few- and zero-shot learning, is to generalize beyond the class distribution of the training data. In this paper, we propose a general class-agnostic objectne…
Novel Object Detectionobject-detectionObject DetectionOpen-vocabulary object detection+3Detecting Open World Objects via Partial Attribute Assignment
Despite being trained on massive data, today's vision foundation models still fall short in detecting open world objects. Apart from recognizing known objects from training, a successful Open World Object Detection (…
Attributeobject-detectionObject DetectionOpen World Object DetectionOW-OVD: Unified Open World and Open Vocabulary Object Detection
Open world perception expands traditional closed-set frameworks, which assume a predefined set of known categories, to encompass dynamic real-world environments. Open World Object Detection (OWOD) and Open Vocabulary…
AttributeIncremental Learningobject-detectionObject Detection+4YOLO-UniOW: Efficient Universal Open-World Object Detection
Traditional object detection models are constrained by the limitations of closed-set datasets, detecting only categories encountered during training. While multimodal models have extended category recognition by aligning…
Incremental LearningObjectobject-detectionObject Detection+1UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework
We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknow…
Objectobject-detectionObject DetectionOpen World Object DetectionFrom Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on open vocabulary object detection (OVD) …
Autonomous DrivingObjectobject-detectionObject Detection+3OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
Open-world autonomous driving encompasses domain generalization and open-vocabulary. Domain generalization refers to the capabilities of autonomous driving systems across different scenarios and sensor parameter configur…
3D Object DetectionAutonomous DrivingDomain GeneralizationLanguage Modeling+6DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encod…
Long-tailed Object DetectionObjectobject-detectionObject Detection+3Open World Object Detection: A Survey
Exploring new knowledge is a fundamental human ability that can be mirrored in the development of deep neural networks, especially in the field of object detection. Open world object detection (OWOD) is an emerging area …
Incremental LearningObjectobject-detectionObject Detection+3SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary Detection
Open-vocabulary detection (OVD) aims to detect novel objects without instance-level annotations to achieve open-world object detection at a lower cost. Existing OVD methods mainly rely on the powerful open-vocabulary ima…
object-detectionObject DetectionOpen Vocabulary Object DetectionOpen World Object DetectionOW-Rep: Open World Object Detection with Instance Representation Learning
Open World Object Detection(OWOD) addresses realistic scenarios where unseen object classes emerge, enabling detectors trained on known classes to detect unknown objects and incrementally incorporate the knowledge they p…
Novel Class DiscoveryObjectobject-detectionObject Detection+3Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes
Detecting and localising unknown or Out-of-distribution (OOD) objects in any scene can be a challenging task in vision. Particularly, in safety-critical cases involving autonomous systems like automated vehicles or train…
Anomaly Segmentationobject-detectionObject DetectionOpen World Object Detection