Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HR-Net), our method brings consistent performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our community to rethink the current de facto training paradigm in fully supervised semantic segmentation.Comment: Our code will be available at https://github.com/tfzhou/ContrastiveSe

Real-time superpixel segmentation by DBSCAN clustering algorithm

In this paper, we propose a real-time image superpixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency

Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Current semantic segmentation methods focus only on mining “local” context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure aware optimization criteria (e.g., IoU-like loss). However, they ignore “global” context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by recent advance in unsupervised contrastive rep resentation learning, we propose a pixel-wise contrastive algorithm for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings be longing to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HRNet), our method brings performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our commu nity to rethink the current de facto training paradigm in se mantic segmentation

Behavior characteristics of hydrogen and its isotope in molten salt of LiF-NaF-KF (FLiNaK)

Experimental studies to investigate the behaviors of hydrogen in molten FLiNaK (LiF-NaF-KF) have been conducted at 500–700 °C. On the basis of previous studies, the diffusivity and solubility of hydrogen in FLiNaK have been revised, and the expressions can be correlated to the following Arrhenius equations: DH2 = 1.62 × 10−5exp (−48.20 × 103/Rg·T) [m2/s] and SH2 = 1.03 × 10−4exp (−14.96 × 103/Rg·T) [mol-H2/m3/Pa], respectively. The behavior characteristics of deuterium in FLiNaK were studied and compared with the hydrogen behaviors in FLiNaK. The results showed the behaviors of deuterium were consistence with the behaviors of hydrogen in FLiNaK. The difference between hydrogen and deuterium has not been observed upon the experimental research of the behavior characteristics of hydrogen and deuterium in FLiNaK, which suggested the results obtained here might apply equally to the behavior characteristics of tritium in FLiNaK. Keywords: Hydrogen, Deuterium, Molten FLiNaK, Diffusion, Permeatio

Validation of stage groupings in the eighth edition of the tumor node metastasis classification for lung adenocarcinoma

Background The purpose of this study was to validate stage groupings in the 8th edition of the tumor node metastasis (TNM) classification for lung adenocarcinoma and explore the non‐anatomic factors that influence the prognosis of lung adenocarcinoma patients in China. Methods We retrospectively analyzed the data of 291 lung adenocarcinoma patients at our department between 2008 and 2013. Logrank tests and Cox regression models were used to analyze survival among adjacent stage groupings. Kaplan–Meier curves were used to estimate overall survival (OS). Results There were significant differences in OS in adjacent stage groupings in early stages in the 8th edition. There were also significant differences between patients treated with radical surgery and limited resection (P = 0.027). Lepidic predominant adenocarcinoma (LPA) had better survival rates than acinar predominant (APA), papillary predominant, and solid predominant with mucin production adenocarcinoma (SPA) (P = 0.008). Survival rates of micropapillary predominant adenocarcinoma were lower than the others (P = 0.003). EGFR mutations were closely associated with lepidic predominant (65%, P = 0.56) but less commonly associated with solid predominant with mucin production adenocarcinoma (24%, P = 0.02). There was no significant difference in survival between EGFR gene mutation‐positive and negative groups (P = 0.402). Conclusion The 8th edition TNM may be more accurate and applicable than the 7th edition for Chinese lung adenocarcinoma patients who have undergone surgical treatment. Stage IV patients may gain survival improvement from radical surgery