ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation

Recent sparse detectors with multiple, e.g. six, decoder layers achieve promising performance but much inference time due to complex heads. Previous works have explored using dense priors as initialization and built one-decoder-layer detectors. Although they gain remarkable acceleration, their performance still lags behind their six-decoder-layer counterparts by a large margin. In this work, we aim to bridge this performance gap while retaining fast speed. We find that the architecture discrepancy between dense and sparse detectors leads to feature conflict, hampering the performance of one-decoder-layer detectors. Thus we propose Adaptive Sparse Anchor Generator (ASAG) which predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. For each image, ASAG dynamically selects which feature maps and which locations to predict, forming a fully adaptive way to generate image-specific anchors. Further, a simple and effective Query Weighting method eases the training instability from adaptiveness. Extensive experiments show that our method outperforms dense-initialized ones and achieves a better speed-accuracy trade-off. The code is available at \url{https://github.com/iSEE-Laboratory/ASAG}.Comment: Accepted to ICCV 202

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factorial components. A commonly used model for dynamic patterns is the state space model, where the state evolves over time according to a transition model and the state generates the observed image frames according to an emission model. To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels. Thus in the emission model, we let the hidden state generate the displacement field, which warps the trackable component in the previous image frame to generate the next frame while adding a simultaneously emitted residual image to account for the change that cannot be explained by the deformation. The warping of the previous image is about the trackable part of the change of image frame, while the residual image is about the intrackable part of the image. We use a maximum likelihood algorithm to learn the model that iterates between inferring latent noise vectors that drive the transition model and updating the parameters given the inferred latent vectors. Meanwhile we adopt a regularization term to penalize the norms of the residual images to encourage the model to explain the change of image frames by trackable motion. Unlike existing methods on dynamic patterns, we learn our model in unsupervised setting without ground truth displacement fields. In addition, our model defines a notion of intrackability by the separation of warped component and residual component in each image frame. We show that our method can synthesize realistic dynamic pattern, and disentangling appearance, trackable and intrackable motions. The learned models are useful for motion transfer, and it is natural to adopt it to define and measure intrackability of a dynamic pattern

Mesenchymal stem cells differentiate into hepatocyte-like cells under different induction systems in hepatitis B patients with liver failure

This study aimed to investigate and compare induced differentiation of mesenchymal stem cells from hepatitis B (HB) patients with liver failure into hepatocytes-like cells with different induction systems in vitro. The differentiation of MSCs from HB patients with liver failure was induced in vitro into hepatocytes-like cells by three cell culture media (serum-free medium (group 1), auto serum-containing medium (group 2) and medium supplemented with fetal bovine serum (FBS) (group 3)). Cell morphology,cell growth curve, amount of urea and glycogen and mRNA expressions of ALB, CK18 and AFP were detected and compared. Morphological changes in group 1 and 2 were more evident than that in group 3 and cell growth in group 3 was faster than the other two groups. The amount of urea and glycogen in group 1 and 2 was significantly elevated when compared with that in group 3 after 6 days of culture. RT-PCR analysis indicated the mRNA expressions of ALB, CK18 and AFP in group 1 and 2 were markedly increased as compared to that in group 3. The differentiation of MSCs from HB patients with liver failure into hepatocytes-like cells can be induced by three different cell culture media and the inductive effects were more profound in cells grown in the serum-free medium and auto serum-containing medium.Key words: Liver failure, mesenchymal stem cells, hepatogenic differentiation, in vitro