SOTAVerified

Open Vocabulary Object Detection

Open-vocabulary detection (OVD) aims to generalize beyond the limited number of base classes labeled during the training phase. The goal is to detect novel classes defined by an unbounded (open) vocabulary at inference.

Papers

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Showing 1–10 of 145 papers

TitleStatusHype
ATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction—0
Gen-n-Val: Agentic Image Data Generation and Validation—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
FG-CLIP: Fine-Grained Visual and Textual AlignmentCode4
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language ModelCode9
Superpowering Open-Vocabulary Object Detectors for X-ray VisionCode1
An Iterative Feedback Mechanism for Improving Natural Language Class Descriptions in Open-Vocabulary Object Detection—0
Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark—0
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data GenerationCode0
Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object DetectionCode0
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All datasetsMSCOCOLVIS v1.0Objects365OpenImages-v4

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Object-Centric-OVDmask AP5022.3—Unverified
2ViLDmask AP5018.2—Unverified