In this project, we worked on speech recognition, specifically predicting individual words based on both the video frames and audio. Empowered by convolutional neural networks, the recent speech recognition and lip reading models are comparable to human level performance. We re-implemented and made derivations of the state-of-the-art model. Then, we conducted rich experiments including the effectiveness of attention mechanism, more accurate residual network as the backbone with pre-trained weights and the sensitivity of our model with respect to audio input with without noise.
Deep architectures that use both audio and video data also tend to use LSTM or GRU units for their predictions. This is seen in the encoder-decoder architecture employed in @cite_20 which uses unidirectional LSTMs to encode both the image and audio data and generates attention vectors to predict the word being said. While @cite_32 used only video frames as input, it can be easily extended to incorporate both audio and visual information as seen in @cite_15 . This uses two separate ResNets and BGRUs to extract features and model temporal dependencies from the visual and audio inputs and two additional BGRU's to combine the extracted audio and visual features. @cite_13 uses another approach which uses temporal multimodal networks to learn a joint distribution over a mouth and lip movements along with the audio at every frame. These joint distributions are then combined to get a time-dependent sequence of frames and audio.
@cite_15 @cite_13 @cite_32 @cite_20
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Recent progress in using long short-term memory (LSTM) for image captioning has motivated the exploration of their applications for video captioning. By taking a video as a sequence of features, an LSTM model is trained on video-sentence pairs and learns to associate a video to a sentence. However, most existing methods compress an entire video shot or frame into a static representation, without considering attention mechanism which allows for selecting salient features. Furthermore, existing approaches usually model the translating error, but ignore the correlations between sentence semantics and visual content. To tackle these issues, we propose a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences. This framework integrates attention mechanism with LSTM to capture salient structures of video, and explores the correlation between multimodal representations (i.e., words and visual content) for generating sentences with rich semantic content. Specifically, we first propose an attention mechanism that uses the dynamic weighted sum of local two-dimensional convolutional neural network representations. Then, an LSTM decoder takes these visual features at time @math and the word-embedding feature at time @math @math 1 to generate important words. Finally, we use multimodal embedding to map the visual and sentence features into a joint space to guarantee the semantic consistence of the sentence description and the video visual content. Experiments on the benchmark datasets demonstrate that our method using single feature can achieve competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR. As characterizing videos simultaneously from spatial and temporal cues has been shown crucial for the video analysis, the combination of convolutional neural networks and recurrent neural networks, i.e., recurrent convolution networks (RCNs), should be a native framework for learning the spatio-temporal video features. In this paper, we develop a novel sequential vector of locally aggregated descriptor (VLAD) layer, named SeqVLAD, to combine a trainable VLAD encoding process and the RCNs architecture into a whole framework. In particular, sequential convolutional feature maps extracted from successive video frames are fed into the RCNs to learn soft spatio-temporal assignment parameters, so as to aggregate not only detailed spatial information in separate video frames but also fine motion information in successive video frames. Moreover, we improve the gated recurrent unit (GRU) of RCNs by sharing the input-to-hidden parameters and propose an improved GRU-RCN architecture named shared GRU-RCN (SGRU-RCN). Thus, our SGRU-RCN has a fewer parameters and a less possibility of overfitting. In experiments, we evaluate SeqVLAD with the tasks of video captioning and video action recognition. Experimental results on Microsoft Research Video Description Corpus, Montreal Video Annotation Dataset, UCF101, and HMDB51 demonstrate the effectiveness and good performance of our method. Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW). The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms. The temporal dynamics in each stream modality are modeled by a 2-layer BGRU and the fusion of multiple streams modalities takes place via another 2-layer BGRU. A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise. In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models. We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder. The temporal encoder is represented by a differentiable visual memory composed of convolutional long short-term memory (LSTM) cells that integrate changes over time. Here we target motion changes and use as temporal decoder a robust optical flow prediction module together with an image sampler serving as built-in feedback loop. The architecture is end-to-end differentiable. At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame. By minimising the reconstruction error between the predicted next frame and the corresponding ground truth next frame, we train the whole system to extract features useful for motion estimation without any supervision effort. We present one direct application of the proposed framework in weakly-supervised semantic segmentation of videos through label propagation using optical flow.