Results 31 to 40 of about 1,105,657 (336)
RecFRCN: Few-Shot Object Detection With Recalibrated Faster R-CNN
IEEE Access, 2023 Currently, Faster R-CNN serves as the fundamental detection framework in the majority of few-shot object detection algorithms. However, due to limited samples per class, the Faster R-CNN’s classification branch faces limitations in capturing ...
Youyou Zhang, Tongwei Lu
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Seeing Objects as Faces Enhances Object Detection [PDF]
i-Perception, 2015 The face is a special visual stimulus. Both bottom-up processes for low-level facial features and top-down modulation by face expectations contribute to the advantages of face perception. However, it is hard to dissociate the top-down factors from the bottom-up processes, since facial stimuli mandatorily lead to face awareness.
Kohske Takahashi, Katsumi Watanabe
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Video Object Detection Guided by Object Blur Evaluation
IEEE Access, 2020 In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus ...
Yujie Wu +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 RGB–infrared object detection in remote-sensing images is crucial for achieving around-clock surveillance of unmanned aerial vehicles.
Jin Xie +4 more
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Contextualizing Object Detection and Classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate on co-occurrence relationship within classes and few of them focus on contextualization from a top-down ...
Chen, Qiang +5 more
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Localization Distillation for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling the localization information. In this paper, we investigate whether logit mimicking always lags behind feature imitation.
Zhaohui Zheng +6 more
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YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
Applied Sciences, 2023 Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects ...
Zixun Ye +3 more
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Detecting the unknown in Object Detection
, 2022 Object detection methods have witnessed impressive improvements in the last years thanks to the design of novel neural network architectures and the availability of large scale datasets. However, current methods have a significant limitation: they are able to detect only the classes observed during training time, that are only a subset of all the ...
Fontanel, Dario +3 more
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Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream
Sensors, 2023 Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan ...
Xiuhua Li +5 more
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Salient Object Detection: A Benchmark [PDF]
IEEE Transactions on Image Processing, 2015 We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods.
Huaizu Jiang +3 more
openaire +5 more sources