Chaoyue Niu

Yucheng Ding, Chaoyue Niu, Fan Wu et al.

Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage the device-side samples to improve the cloud-side large model becomes a practical requirement, but falls into the dilemma of no raw sample up-link and no large model down-link. Specifically, the user may opt out of sharing raw samples with the cloud due to the concern of privacy or communication overhead, while the size of some large vision models far exceeds the mobile device's runtime capacity. In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples. In particular, DC-CCL vertically splits the base model into two submodels, one large submodel for learning from the cloud-side samples and the other small submodel for learning from the device-side samples and performing device-cloud knowledge fusion. Nevertheless, on-device training of the small submodel requires the output of the cloud-side large submodel to compute the desired gradients. DC-CCL thus introduces a light-weight model to mimic the large cloud-side submodel with knowledge distillation, which can be offloaded to the mobile device to control its small submodel's optimization direction. Given the decoupling nature of two submodels in collaborative learning, DC-CCL also allows the cloud to take a pre-trained model and the mobile device to take another model with a different backbone architecture.

Yikai Yan, Chaoyue Niu, Fan Wu et al.

The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized. From the cloud-client discrepancies on the range of image classes, the recognition model is desired to have strong adaptiveness, intuitively by concentrating the focus on each individual client's local dynamic class subset, while incurring negligible overhead. In this work, we propose to plug a new intra-client and inter-image attention (ICIIA) module into existing backbone recognition models, requiring only one-time cloud-based training to be client-adaptive. In particular, given a target image from a certain client, ICIIA introduces multi-head self-attention to retrieve relevant images from the client's historical unlabeled images, thereby calibrating the focus and the recognition result. Further considering that ICIIA's overhead is dominated by linear projection, we propose partitioned linear projection with feature shuffling for replacement and allow increasing the number of partitions to dramatically improve efficiency without scarifying too much accuracy. We finally evaluate ICIIA using 3 different recognition tasks with 9 backbone models over 5 representative datasets. Extensive evaluation results demonstrate the effectiveness and efficiency of ICIIA. Specifically, for ImageNet-1K with the backbone models of MobileNetV3-L and Swin-B, ICIIA can improve the testing accuracy to 83.37% (+8.11%) and 88.86% (+5.28%), while adding only 1.62% and 0.02% of FLOPs, respectively.

Chaoyue Niu, Danesh Tarapore, Klaus-Peter Zauner

Robot swarms to date are not prepared for autonomous navigation such as path planning and obstacle detection in forest floor, unable to achieve low-cost. The development of depth sensing and embedded computing hardware paves the way for swarm of terrestrial robots. The goal of this research is to improve this situation by developing low cost vision system for small ground robots to rapidly perceive terrain. We develop two depth estimation models and evaluate their performance on Raspberry Pi 4 and Jetson Nano in terms of accuracy, runtime and model size of depth estimation models, as well as memory consumption, power draw, temperature, and cost of above two embedded on-board computers. Our research demonstrated that auto-encoder network deployed on Raspberry Pi 4 runs at a power consumption of 3.4 W, memory consumption of about 200 MB, and mean runtime of 13 ms. This can be to meet our requirement for low-cost swarm of robots. Moreover, our analysis also indicated multi-scale deep network performs better for predicting depth map from blurred RGB images caused by camera motion. This paper mainly describes depth estimation models trained on our own dataset recorded in forest, and their performance on embedded on-board computers.

Chaoyue Niu, Danesh Tarapore, Klaus-Peter Zauner

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100k images were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leafs, and dirt. In addition GPS, IMU, and wheel encoder data was recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.