Visual Representation Learning from Unlabeled Video using Contrastive Masked Autoencoders

Masked Autoencoders (MAEs) learn self-supervised representations by randomly masking input image patches and a reconstruction loss. Alternatively, contrastive learning self-supervised methods encourage two versions of the same input to have a similar representation, while pulling apart the representations for different inputs. We propose ViC-MAE, a general method that combines both MAE and contrastive learning by pooling the local feature representations learned under the MAE reconstruction objective and leveraging this global representation under a contrastive objective across video frames. We show that visual representations learned under ViC-MAE generalize well to both video classification and image classification tasks. Using a backbone ViT-B/16 network pre-trained on the Moments in Time (MiT) dataset, we obtain state-of-the-art transfer learning from video to images on Imagenet-1k by improving 1.58% in absolute top-1 accuracy from a recent previous work. Moreover, our method maintains a competitive transfer-learning performance of 81.50% top-1 accuracy on the Kinetics-400 video classification benchmark. In addition, we show that despite its simplicity, ViC-MAE yields improved results compared to combining MAE pre-training with previously proposed contrastive objectives such as VicReg and SiamSiam.

A fast multi-object tracking system using an object detector ensemble

Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.

Learning from multivariate discrete sequential data using a restricted Boltzmann machine model

A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by proposing the p-RBM model, a generalization of the regular RBM model, capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of predicting the stock market direction considering 100 stocks of the NASDAQ-100 index. Obtained results show that the p-RBM offer promising prediction potential.