Machine perception

We adapt large-scale image-text pretrained models such as CLIP for temporal localization tasks in untrimmed videos, which is still a relatively unexplored task. We do so by designing a new approach called UnLoc, which uses a pretrained image and text tower, and feeds tokens to a video-text fusion model. The output of the fusion module are then used to construct a feature pyramid in which each...

We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views.The core of our method is a multilayer perceptron (MLP)that generates RGBA at each 5D coordinate from multi-view image features. Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that naturally...

Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a (student) model may have less representational capacity than the corresponding teacher model. Often, knowledge of specific problem features is used to reweight instances & rebalance the learning process. We propose a novel early...

Given a pair of images—target person and garment on another person—we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data...