We present a new, simple yet effective approach to uplift video object detection. We observe
that prior works operate on instance-level feature aggregation that imminently neglects the …

Abstract Objects in videos are typically characterized by continuous smooth motion. We
exploit continuous smooth motion in three ways. 1) Improved accuracy by using object …

Video object detection is an important yet challenging task in the computer vision field. One
limitation of off-the-shelf video object detection methods is that they only explore information …

State-of-the-art video object detection methods maintain a memory structure, either a sliding
window or a memory queue, to enhance the current frame using attention mechanisms …

Single-frame object detectors perform well on videos sometimes, even without temporal
context. However, challenges such as occlusion, motion blur, and rare poses of objects are …

Compared with still image object detection, video object detection (VOD) needs to
particularly concern the high across-frame variation in object appearance, and the diverse …

Recently, one-stage detectors have achieved competitive accuracy and faster speed
compared with traditional two-stage detectors on image data. However, in the field of video …

Recent studies have shown that, context aggregating information from proposals in different
frames can clearly enhance the performance of video object detection. However, these …

For efficient video object detection, our detector consists of a spatial module and a temporal
module. The spatial module aims to detect objects in static frames using convolutional …

Extending state-of-the-art object detectors from image to video is challenging. The accuracy
of detection suffers from degenerated object appearances in videos, eg, motion blur, video …