A survey and performance evaluation of deep learning methods for small object detection
In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …
development of deep convolutional neural networks (CNN). This paper provides a …
Recent advances in deep learning for object detection
Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …
been widely studied in the past decades. Visual object detection aims to find objects of …
Toward fast, flexible, and robust low-light image enhancement
Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …
both visual quality and computational efficiency but also commonly invalid in unknown …
Towards large-scale small object detection: Survey and benchmarks
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …
prominent advances in past years. However, such prosperity could not camouflage the …
Attentional feature fusion
Feature fusion, the combination of features from different layers or branches, is an
omnipresent part of modern network architectures. It is often implemented via simple …
omnipresent part of modern network architectures. It is often implemented via simple …
Expressive talking head generation with granular audio-visual control
Generating expressive talking heads is essential for creating virtual humans. However,
existing one-or few-shot methods focus on lip-sync and head motion, ignoring the emotional …
existing one-or few-shot methods focus on lip-sync and head motion, ignoring the emotional …
RFLA: Gaussian receptive field based label assignment for tiny object detection
Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …
Attentional local contrast networks for infrared small target detection
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this article,
we propose a novel model-driven deep network for infrared small target detection, which …
we propose a novel model-driven deep network for infrared small target detection, which …
Memory-efficient patch-based inference for tiny deep learning
Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …
Dynamic anchor learning for arbitrary-oriented object detection
Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote
sensing images, etc., and thus arbitrary-oriented object detection has received considerable …
sensing images, etc., and thus arbitrary-oriented object detection has received considerable …