Computer vision

Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks. The resulting algorithm requires less parameter tuning, trains well with an initial learning rate 1.0, and easily generalizes to different tasks. We enforce scale invariance with local statistics in the data to align similar samples

Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These

Due to the variance of optical properties across different people, the performance of a person-agnostic gaze estimation model may not generalize well on a specific person. Though one may achieve better performance by training a person-specific model, it typically requires a large number of samples which is not available in real-life scenarios. Hence, few-shot gaze estimation method is preferred for the

We approach instantaneous mapping, converting images to a top-down view of the world, as a translation problem. We show how a novel form of transformer network can be used to map from images and video directly to an overhead map or bird’s-eye-view (BEV) of the world, in a single end-to-end network. We assume a 1-1 correspondence between a vertical scanline in the image, and rays passing through the camera

Audio-visual data allows us to leverage different modalities for downstream tasks. The idea being individual streams can complement each other in the given task, thereby resulting in a model with improved performance. In this work, we present our experimental results on action recognition and video summarization tasks. The proposed modeling approach builds upon the recent advances in contrastive loss based