Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bounding box annotations as supervision. To this end, we propose affinity attention graph neural network ( A2GNN). Following previous practices, we first generate pseudo semantic-aware seeds, which are then formed into semantic graphs based on our newly proposed affinity Convolutional Neural Network (CNN). Then the built graphs are input to our A2GNN, in which an affinity attention layer is designed to acquire the short- and long- distance information from soft graph edges to accurately propagate semantic labels from the confident seeds to the unlabeled pixels. However, to guarantee the precision of the seeds, we only adopt a limited number of confident pixel seed labels for A2GNN, which may lead to insufficient supervision for training. To alleviate this issue, we further introduce a new loss function and a consistency-checking mechanism to leverage the bounding box constraint, so that more reliable guidance can be included for the model optimization. Experiments show that our approach achieves new state-of-the-art performances on Pascal VOC 2012 datasets (val: 76.5 percent, test: 75.2 percent). More importantly, our approach can be readily applied to bounding box supervised instance segmentation task or other weakly supervised semantic segmentation tasks, with state-of-the-art or comparable performance among almot all weakly supervised tasks on PASCAL VOC or COCO dataset. Our source code will be available at https://github.com/zbf1991/A2GNN.

Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow.

The Coarse-To-Fine (CTF) matching scheme has been widely applied to reduce computational complexity and matching ambiguity in stereo matching and optical flow tasks by converting image pairs into multi-scale representations and performing matching from coarse to fine levels. Despite its efficiency, it suffers from several weaknesses, such as tending to blur the edges and miss small structures like thin bars and holes. We find that the pixels of small structures and edges are often assigned with wrong disparity/flow in the upsampling process of the CTF framework, introducing errors to the fine levels and leading to such weaknesses. We observe that these wrong disparity/flow values can be avoided if we select the best-matched value among their neighborhood, which inspires us to propose a novel differentiable Neighbor-Search Upsampling (NSU) module. The NSU module first estimates the matching scores and then selects the best-matched disparity/flow for each pixel from its neighbors. It effectively preserves finer structure details by exploiting the information from the finer level while upsampling the disparity/flow. The proposed module can be a drop-in replacement of the naive upsampling in the CTF matching framework and allows the neural networks to be trained end-to-end. By integrating the proposed NSU module into a baseline CTF matching network, we design our Detail Preserving Coarse-To-Fine (DPCTF) matching network. Comprehensive experiments demonstrate that our DPCTF can boost performances for both stereo matching and optical flow tasks. Notably, our DPCTF achieves new state-of-the-art performances for both tasks - it outperforms the competitive baseline (Bi3D) by 28.8% (from 0.73 to 0.52) on EPE of the FlyingThings3D stereo dataset, and ranks first in KITTI flow 2012 benchmark. The code is available at https://github.com/Deng-Y/DPCTF.

Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding.

In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage. In traditional methods, an object region that best matches the referring expression is picked out, and then the query sentence is reconstructed from the selected region, where the reconstruction difference serves as the loss for back-propagation. The existing methods, however, conduct both the matching and the reconstruction approximately as they ignore the fact that the matching correctness is unknown. To overcome this limitation, a discriminative triad is designed here as the basis to the solution, through which a query can be converted into one or multiple discriminative triads in a very scalable way. Based on the discriminative triad, we further propose the triad-level matching and reconstruction modules which are lightweight yet effective for the weakly-supervised training, making it three times lighter and faster than the previous state-of-the-art methods. One important merit of our work is its superior performance despite the simple and neat design. Specifically, the proposed method achieves a new state-of-the-art accuracy when evaluated on RefCOCO (39.21 percent), RefCOCO+ (39.18 percent) and RefCOCOg (43.24 percent) datasets, that is 4.17, 4.08 and 7.8 percent higher than the previous one, respectively. The code is available at https://github.com/insomnia94/DTWREG.

Multiview video plus depth transmission via virtual-view-assisted complementary down/upsampling.

Multiview video plus depth is a popular 3D video format which can provide viewers a vivid 3D feeling. However, its requirements in terms of computational complexity and transmission bandwidth are more than that of conventional 2D video. To mitigate these limitations, some works have proposed to reduce the amount of transmitted data by adopting different resolutions for different views, and consequently, the transmitted video is called mixed resolution video. In order to further reduce the transmitted data and maintain good quality at the decoder side; in this paper, we propose a down/upsampling algorithm for 3D multiview video which systematically takes into account the video encoder and decoder. At the encoder side, the rows of the two adjacent views are downsampled following an interlacing and complementary fashion, whereas, at the decoder side, the discarded pixels are recovered by fusing the virtual view pixels with the directional interpolated pixels from the complementary downsampled views. Moreover, the patterns of the texture surrounding the discarded pixels are used to aid the data fusion, so as to enhance edges recovery. Meanwhile, with the assistance of virtual views, at the decoder side, the proposed approach can effectively recover the discarded high-frequency details. The experimental results demonstrate the superior performance of the proposed framework.

[PROGRESS OF ALGINATE DERIVATIVES BASED ON BIOMEDICAL MATERIALS].

OBJECTIVE: To summarize the current research status of alginate derivatives based on biomedical materials, and analyze several key points as novel clinical products. METHODS: The general preparation and application methods of alginate derivatives based on biomedical materials at home and abroad were reviewed. The present status and problems were analyzed. RESULTS: The derivation methods to prepare alginate derivatives include crosslink, sulfation, biological factors derivatization, hydrophobic modification, and graft copolymerization. With excellent bionic performance of structure and properties, many alginate derivatives are available for tissue engineering scaffolds, artificial organs, and drug delivery systems etc. However, more systematic applied basic research data should be collected and statistically analyzed for risk managements. CONCLUSION: Alginate derivatives have good feasibility as novel medical products, meanwhile, systematic evaluation and verification should be executed for their safety, effectiveness, and suitability.