1727 papers with code • 54 benchmarks • 165 datasets
Object detection is the task of detecting instances of objects of a certain class within an image. The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods. One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet. Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.
The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.
( Image credit: Detectron )
- 3D Object Detection
- RGB Salient Object Detection
- Real-Time Object Detection
- 2D Object Detection
- 2D Object Detection
- Video Object Detection
- RGB-D Salient Object Detection
- Few-Shot Object Detection
- Weakly Supervised Object Detection
- Small Object Detection
- Object Detection In Aerial Images
- Object Proposal Generation
- Robust Object Detection
- Video Salient Object Detection
- Dense Object Detection
- Head Detection
- Camouflaged Object Segmentation
- Co-Salient Object Detection
- Zero-Shot Object Detection
- Medical Object Detection
- One-Shot Object Detection
- Moving Object Detection
- Surgical tool detection
- Multiview Detection
- Semantic Part Detection
- Object Skeleton Detection
- Open World Object Detection
- Multiple Affordance Detection
- 3D Object Detection From Monocular Images
- Fish Detection
- Class-agnostic Object Detection
- Object Detection In Indoor Scenes
Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts
The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as root causes (RC) of the object shape defects.
Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.
To alleviate the impact of few samples, enhancing the generalization and discrimination abilities of detectors on new objects plays an important role.
Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures.
Deformable DETR uses the multiscale feature to ameliorate performance, however, the number of encoder tokens increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck.
Object detection is a very important basic research direction in the field of computer vision and a basic method for other advanced tasks in the field of computer vision.