New generation deep learning for video object detection: A survey
Video object detection, a basic task in the computer vision field, is rapidly evolving and
widely used. In recent years, deep learning methods have rapidly become widespread in the …
widely used. In recent years, deep learning methods have rapidly become widespread in the …
[HTML][HTML] A review of video object detection: Datasets, metrics and methods
H Zhu, H Wei, B Li, X Yuan, N Kehtarnavaz - Applied Sciences, 2020 - mdpi.com
Although there are well established object detection methods based on static images, their
application to video data on a frame by frame basis faces two shortcomings:(i) lack of …
application to video data on a frame by frame basis faces two shortcomings:(i) lack of …
Bevdet4d: Exploit temporal cues in multi-camera 3d object detection
J Huang, G Huang - arXiv preprint arXiv:2203.17054, 2022 - arxiv.org
Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …
[HTML][HTML] Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM
PN Srinivasu, JG SivaSai, MF Ijaz, AK Bhoi, W Kim… - Sensors, 2021 - mdpi.com
Deep learning models are efficient in learning the features that assist in understanding
complex patterns precisely. This study proposed a computerized process of classifying skin …
complex patterns precisely. This study proposed a computerized process of classifying skin …
Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition
While today's video recognition systems parse snapshots or short clips accurately, they
cannot connect the dots and reason across a longer range of time yet. Most existing video …
cannot connect the dots and reason across a longer range of time yet. Most existing video …
[HTML][HTML] A comparative analysis of object detection metrics with a companion open-source toolkit
Recent outstanding results of supervised object detection in competitions and challenges
are often associated with specific metrics and datasets. The evaluation of such methods …
are often associated with specific metrics and datasets. The evaluation of such methods …
Deep learning for unmanned aerial vehicle-based object detection and tracking: A survey
Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …
recently become a hotspot across the fields of computer vision (CV) and remote sensing …
[HTML][HTML] Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-
169 model. However, the current system for identifying metastases in a lymph node is …
169 model. However, the current system for identifying metastases in a lymph node is …
Learning to detect objects with a 1 megapixel event camera
E Perot, P De Tournemire, D Nitti… - Advances in Neural …, 2020 - proceedings.neurips.cc
Event cameras encode visual information with high temporal precision, low data-rate, and
high-dynamic range. Thanks to these characteristics, event cameras are particularly suited …
high-dynamic range. Thanks to these characteristics, event cameras are particularly suited …
Mots: Multi-object tracking and segmentation
This paper extends the popular task of multi-object tracking to multi-object tracking and
segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two …
segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two …