Learn Image Object Cosegmentation with Multiscale Feature Fusion Image object cosegmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multiscale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intraimage and the interimage features. Then these two kinds of features are further fused at each scale as the multiscale cofeatures of common objects, and finally the multiscale cofeatures are summed up and upsampled to obtain the cosegmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the stateoftheart cosegmentation methods while the computation cost has been effectively reduced.

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.