Attention alignment by linear space projection for video features extraction Video feature extraction is the basis of research on video data. At present, many models based on convolution neural networks for features extracting need to stack deeper network layers to improve the robustness, which means that the amount of parameters and computation is greatly increased while the practicability of such models is constrained. In this paper, a global projection selfattention mechanism is proposed to empower shallow models generate robust video representations. Compared to Nonlocal 17 and Squeeze Excitation 18 networks, we adopt more succinct but sufficient context modeling strategy to establish complete global interaction space, introduce a lighter transform block for attention coefficients predicting, which improves the running efficiency of attention models greatly. We further embed our attention module to several modern networks including ResNet3,  mathbfR(mathbf21)mathbfDmathbf9  and I3D7, in that ResNet18 and  mathbfR(mathbf21)mathbfDmathbf18  meet considerably promotion upon its original version by more than 18.9 percent on UCF101 dataset, this result implies huge potential of proposed attention blocks.

See More, Know More: Unsupervised Video Object Segmentation with CoAttention Siamese Networks. We introduce a novel network, called COattention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global coattention mechanism to improve further the stateoftheart deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in shortterm temporal segments. The coattention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending coattention responses into a joint feature space. We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the coattention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. We propose a unified and endtoend trainable framework where different coattention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin.