Describing Videos by Exploiting Temporal Structure

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.Comment: Accepted to ICCV15. This version comes with code release and supplementary materia

MagicVideo: Efficient Video Generation With Latent Diffusion Models

We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. Given a text description, MagicVideo can generate photo-realistic video clips with high relevance to the text content. With the proposed efficient latent 3D U-Net design, MagicVideo can generate video clips with 256x256 spatial resolution on a single GPU card, which is 64x faster than the recent video diffusion model (VDM). Unlike previous works that train video generation from scratch in the RGB space, we propose to generate video clips in a low-dimensional latent space. We further utilize all the convolution operator weights of pre-trained text-to-image generative U-Net models for faster training. To achieve this, we introduce two new designs to adapt the U-Net decoder to video data: a framewise lightweight adaptor for the image-to-video distribution adjustment and a directed temporal attention module to capture frame temporal dependencies. The whole generation process is within the low-dimension latent space of a pre-trained variation auto-encoder. We demonstrate that MagicVideo can generate both realistic video content and imaginary content in a photo-realistic style with a trade-off in terms of quality and computational cost. Refer to https://magicvideo.github.io/# for more examples

Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions

Given a video and a description sentence with one missing word (we call it the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the missing word automatically. The contextual information of the sentence, as well as visual cues from the video, are important to infer the missing word accurately. Since the source sentence is broken into two fragments: the sentence's left fragment (before the blank) and the sentence's right fragment (after the blank), traditional Recurrent Neural Networks cannot encode this structure accurately because of many possible variations of the missing word in terms of the location and type of the word in the source sentence. For example, a missing word can be the first word or be in the middle of the sentence and it can be a verb or an adjective. In this paper, we propose a framework to tackle the textual encoding: Two separate LSTMs (the LR and RL LSTMs) are employed to encode the left and right sentence fragments and a novel structure is introduced to combine each fragment with an "external memory" corresponding the opposite fragments. For the visual encoding, end-to-end spatial and temporal attention models are employed to select discriminative visual representations to find the missing word. In the experiments, we demonstrate the superior performance of the proposed method on challenging VFIB problem. Furthermore, we introduce an extended and more generalized version of VFIB, which is not limited to a single blank. Our experiments indicate the generalization capability of our method in dealing with such more realistic scenarios

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning. To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets