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 ($A^2$GNN). 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 $A^2$GNN, 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 $A^2$GNN, 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\%, test: 75.2\%). 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.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TAPMI 2021

Correlation Filter Selection for Visual Tracking Using Reinforcement Learning

Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter based models are susceptible to wrong updates stemming from inaccurate tracking results. To date, little effort has been devoted towards handling the correlation filter update problem. In this paper, we propose a novel approach to address the correlation filter update problem. In our approach, we update and maintain multiple correlation filter models in parallel, and we use deep reinforcement learning for the selection of an optimal correlation filter model among them. To facilitate the decision process in an efficient manner, we propose a decision-net to deal target appearance modeling, which is trained through hundreds of challenging videos using proximal policy optimization and a lightweight learning network. An exhaustive evaluation of the proposed approach on the OTB100 and OTB2013 benchmarks show that the approach is effective enough to achieve the average success rate of 62.3% and the average precision score of 81.2%, both exceeding the performance of traditional correlation filter based trackers.Comment: 13 pages, 11 figure

Effect of Regional vs General Anesthesia on Incidence of Postoperative Delirium in Older Patients Undergoing Hip Fracture Surgery: The RAGA Randomized Trial

Importance: In adults undergoing hip fracture surgery, regional anesthesia may reduce postoperative delirium, but there is uncertainty about its effectiveness. Objective: To investigate, in older adults undergoing surgical repair for hip fracture, the effects of regional anesthesia on the incidence of postoperative delirium compared with general anesthesia. Design, Setting, and Participants: A randomized, allocation-concealed, open-label, multicenter clinical trial of 950 patients, aged 65 years and older, with or without preexisting dementia, and a fragility hip fracture requiring surgical repair from 9 university teaching hospitals in Southeastern China. Participants were enrolled between October 2014 and September 2018; 30-day follow-up ended November 2018. Interventions: Patients were randomized to receive either regional anesthesia (spinal, epidural, or both techniques combined with no sedation; n = 476) or general anesthesia (intravenous, inhalational, or combined anesthetic agents; n = 474). Main Outcomes and Measures: Primary outcome was incidence of delirium during the first 7 postoperative days. Secondary outcomes analyzed in this article include delirium severity, duration, and subtype; postoperative pain score; length of hospitalization; 30-day all-cause mortality; and complications. Results: Among 950 randomized patients (mean age, 76.5 years; 247 [26.8%] male), 941 were evaluable for the primary outcome (6 canceled surgery and 3 withdrew consent). Postoperative delirium occurred in 29 (6.2%) in the regional anesthesia group vs 24 (5.1%) in the general anesthesia group (unadjusted risk difference [RD], 1.1%; 95% CI, -1.7% to 3.8%; P =.48; unadjusted relative risk [RR], 1.2 [95% CI, 0.7 to 2.0]; P =.57]). Mean severity score of delirium was 23.0 vs 24.1, respectively (unadjusted difference, -1.1; 95% CI, -4.6 to 3.1). A single delirium episode occurred in 16 (3.4%) vs 10 (2.1%) (unadjusted RD, 1.1%; 95% CI, -1.7% to 3.9%; RR, 1.6 [95% CI, 0.7 to 3.5]). Hypoactive subtype in 11 (37.9%) vs 5 (20.8%) (RD, 11.5; 95% CI, -11.0% to 35.7%; RR, 2.2 [95% CI, 0.8 to 6.3]). Median worst pain score was 0 (IQR, 0 to 20) vs 0 (IQR, 0 to 10) (difference 0; 95% CI, 0 to 0). Median length of hospitalization was 7 days (IQR, 5 to 10) vs 7 days (IQR, 6 to 10) (difference 0; 95% CI, 0 to 0). Death occurred in 8 (1.7%) vs 4 (0.9%) (unadjusted RD, -0.8%; 95% CI, -2.2% to 0.7%; RR, 2.0 [95% CI, 0.6 to 6.5]). Adverse events were reported in 106 episodes in the regional anesthesia group and 102 in the general anesthesia group; the most frequently reported adverse events were nausea and vomiting (47 [44.3%] vs 34 [33.3%]) and postoperative hypotension (13 [12.3%] vs 10 [9.8%]). Conclusions and Relevance: In patients aged 65 years and older undergoing hip fracture surgery, regional anesthesia without sedation did not significantly reduce the incidence of postoperative delirium compared with general anesthesia. Trial Registration: ClinicalTrials.gov Identifier: NCT02213380

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV