On the Importance of Attention and Augmentations for Hypothesis Transfer in Domain Adaptation and Generalization.

Unsupervised domain adaptation (UDA) aims to mitigate the performance drop due to the distribution shift between the training and testing datasets. UDA methods have achieved performance gains for models trained on a source domain with labeled data to a target domain with only unlabeled data. The standard feature extraction method in domain adaptation has been convolutional neural networks (CNNs).

Video Captioning Using Global-Local Representation.

Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local vision representation for sentence generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GLR framework, namely a global-local representation granularity.

Full-BAPose: Bottom Up Framework for Full Body Pose Estimation.

We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning architecture is end-to-end trainable based on an encoder-decoder configuration with HRNet backbone and multi-scale representations using a disentangled waterfall atrous spatial pooling module.

Perceptual optimization of language: Evidence from American Sign Language.

If language has evolved for communication, languages should be structured such that they maximize the efficiency of processing. What is efficient for communication in the visual-gestural modality is different from the auditory-oral modality, and we ask here whether sign languages have adapted to the affordances and constraints of the signed modality. During sign perception, perceivers look almost exclusively at the lower face, rarely looking down at the hands.

Benchmarking Domain Adaptation Methods on Aerial Datasets.

Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same underlying data distribution. This may not always be the case, and in such cases, the performance of the model is degraded.