SOTAVerified

Object Localization

Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.

Source: Fast On-Line Kernel Density Estimation for Active Object Localization

Papers

Showing 1–10 of 617 papers

TitleStatusHype
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval—0
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding—0
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base—0
CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion—0
UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
Multispectral Detection Transformer with Infrared-Centric Sensor FusionCode0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method—0
PointArena: Probing Multimodal Grounding Through Language-Guided Pointing—0
Show:102550
← PrevPage 1 of 62Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Frustrum-PointPillarsAP48.3—Unverified
2Frustum PointNetsAP47.2—Unverified
3Frustum PointNetsAP40.23—Unverified
4VoxelNetAP38.11—Unverified
5VoxelNetAP31.51—Unverified