Object Detection

ZiRa achieves zero-shot generalizable incremental learning for vision-language object detection by using a memory-efficient dual-branch architecture and zero-interference loss, significantly boosting …

YOLA: A novel framework for object detection in low-light conditions, achieving significant improvements by learning illumination-invariant features through a novel module.

YOLOv10: Real-time object detection achieves state-of-the-art speed and accuracy by eliminating NMS post-processing and holistically optimizing model architecture for efficiency and accuracy.

First unsupervised object detection method with theoretical guarantees to recover true object positions, up to quantifiable small shifts!

UNION: Unsupervised 3D object detection method doubles average precision, leveraging LiDAR, camera, and temporal data for efficient training without manual labels.

UMB: A novel model enhances open-world object detection by understanding model behavior, surpassing state-of-the-art with a 5.3 mAP gain for unknown classes.

Unsupervised model selection for domain adaptive object detection is achieved via a new Detection Adaptation Score (DAS), effectively selecting optimal models without target labels by leveraging the f…

SpikeSlicer: An adaptive event stream slicer using a spiking neural network (SNN) to efficiently split events for improved downstream processing in object tracking and recognition.

SARDet-100K: A new benchmark dataset and open-source toolkit revolutionizes large-scale SAR object detection.

RGB-Event tracker CSAM leverages MOT philosophy for enhanced robustness by integrating appearance and motion information from RGB and event streams, achieving state-of-the-art performance.

Researchers developed Camera-Agnostic Patch (CAP) attacks, improving adversarial patch reliability by simulating camera image processing in attacks against person detectors.

RETR: Multi-view radar detection transformer significantly improves indoor object detection and segmentation.

StreamDSGN: a real-time stereo 3D object detection framework significantly boosts streaming perception accuracy by leveraging historical information, a feature-flow fusion method, and a motion consist…

Progressive Exploration-Conformal Learning (PECL) revolutionizes sparsely annotated object detection in aerial images by adaptively selecting high-quality pseudo-labels, overcoming limitations of exis…

Parameter-Inverted Image Pyramid Networks (PIIP) boost image pyramid efficiency by using smaller models for higher-resolution images and larger models for lower-resolution ones, achieving superior per…

Language Hierarchical Self-training (LHST) enhances weakly-supervised object detection by integrating language hierarchy, mitigating label mismatch, and improving generalization across diverse dataset…

ODGEN: Boosting object detection accuracy by generating high-quality synthetic images using diffusion models conditioned on bounding boxes and text descriptions.

MonoMAE enhances monocular 3D object detection by using depth-aware masked autoencoders to effectively handle object occlusions, achieving superior performance on both occluded and non-occluded object…

AdaptOD: a novel approach for robust OOD detection in long-tailed recognition, dynamically adapting outlier distributions to true OOD distributions using a dual-normalized energy loss for improved acc…

Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL) pre-training significantly boosts object detection accuracy, especially for underrepresented classes.