Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

古生物學(xué)

書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




書目名稱Neural Information Processing年度引用




書目名稱Neural Information Processing年度引用學(xué)科排名




書目名稱Neural Information Processing讀者反饋




書目名稱Neural Information Processing讀者反饋學(xué)科排名

監(jiān)禁

小卒

Ru Peng,Nankai Lin,Yi Fang,Shengyi Jiang,Tianyong Hao,Boyu Chen,Junbo Zhaorial case studies, they capture the wealth of knowledge that eight companies have gathered during the introduction of the software product line engineering approach in their daily practice. After reading this b978-3-642-09061-5978-3-540-71437-8

遺產(chǎn)

Yihong Zhuang,Changxing Jing,Senlin Cai,Lexing Huang,Yue Huang,Xiaotong Tu,Xinghao Dingg, Software Demonstrations and Training, The Market / Your Customers, or Organizational Maturity..The book helps product people to maximize their impact and effectiveness. Whether you’re a seasoned practitioner978-3-030-19873-2978-3-030-19871-8Series ISSN 2192-8096 Series E-ISSN 2192-810X

壕溝

Zhen Gao,Xiaowen Chen,Jingning Xu,Rongjie Yu,Jiaqi Zong ensure they are delivered throughout the entire lifecycle. Next it presents the main concepts and techniques of continuous quality control, discussing the quality control loop and its main techniques such as r978-3-642-44190-5978-3-642-38571-1

顛簸地移動

不可磨滅

北極人

Banister

Spatial and?Temporal Guidance for?Semi-supervised Video Object Segmentationto focus on long-term object-level feature from the first frame. The proposed spatial and temporal guidance effectively alleviates mismatching and makes the model more robust and efficient. Experiments on YouTube-VOS and DAVIS benchmarks show that our method outperforms previous state-of-the-art met

裙帶關(guān)系

Unsupervised Anomaly Segmentation for?Brain Lesions Using Dual Semantic-Manifold Reconstructionns not only on the semantic layout but also the image appearance. In this way a Dual Semantic-Manifold Reconstruction (DSMR) is trained and then used to detect the anomalies accurately. Experiments reveal that the proposed DSMR approach exceeds the state-of-the-art performance on the benchmark datas