Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most suitable backbone for our method. Second, we propose an outlier-aware tokenizer that uses a limited vocabulary to cover frequent tokens while allowing outliers to bypass the prediction and encoding. Third, we propose a novel high-rank reparameterization strategy that enhances the learning capability during training without increasing complexity during inference. Experimental results validate that our method achieves 48% bit saving compared to gzip compressor. Besides, L3TC offers compression performance comparable to other learned compressors, with a 50x reduction in model parameters. More importantly, L3TC is the fastest among all learned compressors, providing real-time decoding speeds up to megabytes per second.

For a long time, learning spatiotemporal features with deep neural networks has been a difficult task in the field of computer vision. In this paper, we present a novel deep architecture, termed as Bifurcated Convolutional Neural Network (BifurcatedNet) to learn the discriminative video representation in an end-to-end manner. In our work, the BifurcatedNet is built upon the stacking bifurcated blocks that aim at simultaneously capturing the static appearance information and the temporal dynamic from input data. Specifically, the bifurcated block is composed of two separated branch, i.e., an appearance branch and a dynamic branch. The appearance branch employs 2D convolutional operation to obtain the spatial responses of image pixels or filters of each input frame, while the design of the dynamic branch is based on the spatio-temporal convolutional operation to exploit the temporal dynamic between pixels and filter response across multiple frames. Multiple experiments are conducted on two popular action recognition benchmarks: UCF101 and HMDB51. With only RGB input, the BifurcatedNet obtains the superior performance over the existing state-of-the-art models under the same experimental setting. The proposed BifurcatedNet is also implemented in a two-stream fashion by using both RGB and optical flow input, and still achieves the state-of-the-art performance, demonstrating the effectiveness of the network design. Furthermore, in order to evaluate the generalization ability, we conduct experiments on the Chalearn LAP IsoGD dataset and find that our model works well in gesture recognition tasks.

Recently, many deep learning approaches have shown remarkable progress on human action recognition. However, it remains unclear how to extract the useful information in videos since only video-level labels are available in the training phase. To address this limitation, many efforts have been made to improve the performance of action recognition by applying the visual attention mechanism in the deep learning model. In this article, we propose a novel deep model called Moving Foreground Attention (MFA) that enhances the performance of action recognition by guiding the model to focus on the discriminative foreground targets. In our work, MFA detects the moving foreground through a proposed variance-based algorithm. Meanwhile, an unsupervised proposal is utilized to mine the action-related key volumes and generate corresponding correlation scores. Based on these scores, a newly proposed stochastic-out scheme is exploited to train the MFA. Experiment results show that action recognition performance can be significantly improved by using our proposed techniques, and our model achieves state-of-the-art performance on UCF101 and HMDB51.