Explicit Motion Handling and Interactive Prompting for Video Camouflaged
  Object Detection
Xin Zhang Tao Xiao Gepeng Ji Xuan Wu Keren Fu Qijun Zhao
Camouflage poses challenges in distinguishing a static target, whereas any
movement of the target can break this disguise. Existing video camouflaged
object detection (VCOD) approaches take noisy motion estimation as input or
model motion implicitly, restricting detection performance in complex dynamic
scenes. In this paper, we propose a novel Explicit Motion handling and
Interactive Prompting framework for VCOD, dubbed EMIP, which handles motion
cues explicitly using a frozen pre-trained optical flow fundamental model. EMIP
is characterized by a two-stream architecture for simultaneously conducting
camouflaged segmentation and optical flow estimation. Interactions across the
dual streams are realized in an interactive prompting way that is inspired by
emerging visual prompt learning. Two learnable modules, i.e. the camouflaged
feeder and motion collector, are designed to incorporate segmentation-to-motion
and motion-to-segmentation prompts, respectively, and enhance outputs of the
both streams. The prompt fed to the motion stream is learned by supervising
optical flow in a self-supervised manner. Furthermore, we show that long-term
historical information can also be incorporated as a prompt into EMIP and
achieve more robust results with temporal consistency. Experimental results
demonstrate that our EMIP achieves new state-of-the-art records on popular VCOD
benchmarks. The code will be publicly available.