Li Fei-Fei

TL;DR: It is shown that grouplets are more effective in classifying and detecting human-object interactions than other state-of-the-art methods and can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing.

TL;DR: Spatial-LTM represents an image containing objects in a hierarchical way by over-segmented image regions of homogeneous appearances and the salient image patches within the regions, enforcing the spatial coherency of the model.

TL;DR: Li et al. as discussed by the authors proposed to search the network level structure in addition to the cell level structure, which formed a hierarchical architecture search space and achieved state-of-the-art performance without any ImageNet pretraining.

TL;DR: In this article, a novel variant of LSTM deep networks is proposed for modeling temporal relations via multiple input and output connections, which improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.

TL;DR: In this paper, the authors used functional magnetic resonance imaging (fMRI) and distributed pattern analysis to ask what regions of the brain can differentiate natural scene categories (such as forests vs mountains vs beaches) and found that area V1, the parahippocampal place area (PPA), retrosplenial cortex (RSC), and lateral occipital complex (LOC) all contain information that distinguishes among natural scene classes.