Xiangpeng Li

TL;DR: This work proposes a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering and significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task.

TL;DR: This paper proposes a novel unsupervised video hashing framework dubbed SSVH, which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion, and designs a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities.

TL;DR: A hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning that utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information.

TL;DR: Self-supervised video hashing (SSVH) as discussed by the authors proposes a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture.

TL;DR: A novel architecture, namely Learnable Aggregating Net with Diversity learning (LAD-Net), for V-VQA, which automatically aggregates adaptively-weighted frame-level features to extract rich video (or question) context semantic information by imitating Bags-of-Words (BoW) quantization.