Glance and Focus Networks for Dynamic Visual Recognition

Nancy J. Delong

Deep learning algorithms can realize super-human-amount overall performance on visible recognition duties, the two in illustrations or photos and online video. Nevertheless, it is complicated in apply due to the superior computational value and superior memory footprint.

Deep learning-based vVisual recognition is important in processing video and still images.

Deep learning-centered visible recognition is critical in processing online video and still illustrations or photos. Picture credit history: honeycombhc through Pixabay, free licence

A latest paper published on aims to lower the computational value of superior-resolution visible recognition from the standpoint of spatial redundancy.

Deep models can identify objects precisely with only a few course-discriminative patches, these types of as the head of a pet dog. Relying on this notion, researchers current glance and focus, a two-phase framework. At the glance stage, the design produces a fast prediction with world functions. The most discriminative area is picked out for the focus phase. It proceeds progressively with iteratively localizing and processing the course-discriminative locations.

The proposed method reveals a significant enhancement of the total efficiency by allocating computation unevenly throughout various illustrations or photos.

Spatial redundancy greatly exists in visible recognition duties, i.e., discriminative functions in an image or online video frame generally correspond to only a subset of pixels, whilst the remaining locations are irrelevant to the undertaking at hand. Therefore, static models which course of action all the pixels with an equivalent volume of computation consequence in significant redundancy in conditions of time and area consumption. In this paper, we formulate the image recognition issue as a sequential coarse-to-wonderful characteristic learning course of action, mimicking the human visible technique. Specially, the proposed Glance and Emphasis Community (GFNet) initial extracts a fast world representation of the input image at a small resolution scale, and then strategically attends to a series of salient (little) locations to find out finer functions. The sequential course of action normally facilitates adaptive inference at check time, as it can be terminated after the design is sufficiently self-assured about its prediction, steering clear of further more redundant computation. It is worthy of noting that the issue of locating discriminant locations in our design is formulated as a reinforcement learning undertaking, hence demanding no more manual annotations other than classification labels. GFNet is normal and flexible as it is appropriate with any off-the-shelf spine models (these types of as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the characteristic extractor. Intensive experiments on a wide range of image classification and online video recognition duties and with various spine models reveal the exceptional efficiency of our method. For case in point, it decreases the average latency of the very economical MobileNet-V3 on an Apple iphone XS Max by one.3x without having sacrificing precision. Code and pre-trained models are obtainable at this https URL.

Research paper: Huang, G., “Glance and Emphasis Networks for Dynamic Visible Recognition”, 2021. Website link: muscles/2201.03014

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