Segvic, S.[Sinisa]
* 2003: Software Architecture for Distributed Visual Tracking in a Global Vision Localization System, A
* 2006: Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support
* 2006: Fast and globally convergent Structure and Motion estimation for General Camera Models
* 2007: Framework for Scalable Vision-Only Navigation, A
* 2007: Influence of numerical conditioning on the accuracy of relative orientation
* 2007: Large scale vision-based navigation without an accurate global reconstruction
* 2008: Online/Realtime Structure and Motion for General Camera Models
* 2009: mapping and localization framework for scalable appearance-based navigation, A
* 2010: Generative modeling of spatio-temporal traffic sign trajectories
* 2011: Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing
* 2011: Histogram-Based Description of Local Space-Time Appearance
* 2012: Towards Space-Time Semantics in Two Frames
* 2014: Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle
* 2015: Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification with Large Fisher Vectors
* 2015: Patch-Level Spatial Layout for Classification and Weakly Supervised Localization
* 2016: Convolutional Scale Invariance for Semantic Segmentation
* 2016: Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels
* 2017: Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images
* 2018: Sparse weakly supervised models for object localization in road environment
* 2019: In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving Images
* 2019: Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
* 2019: Single Level Feature-to-Feature Forecasting with Deformable Convolutions
* 2020: Multimodal Semantic Forecasting Based on Conditional Generation of Future Features
* 2020: Traffic Scene Classification on a Representation Budget
* 2020: Warp to the Future: Joint Forecasting of Features and Feature Motion
* 2021: baseline for semi-supervised learning of efficient semantic segmentation models, A
* 2021: Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images
* 2021: Efficient semantic segmentation with pyramidal fusion
* 2022: Dense open-set recognition based on training with noisy negative images
* 2022: DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition
* 2022: Multi-domain semantic segmentation with overlapping labels *
* 2023: Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
* 2023: On Advantages of Mask-level Recognition for Outlier-aware Segmentation
* 2023: Panoptic SwiftNet: Pyramidal Fusion for Real-Time Panoptic Segmentation
* 2024: Identifying Label Errors in Object Detection Datasets by Loss Inspection
Includes: Segvic, S.[Sinisa] Šegvic, S.[Siniša] Segvic, S. Šegvic, S.
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