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- Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression6,253
This paper introduces a generalized version of IoU ( GIoU) as a loss into the state-of-the art object detection frameworks, and shows a consistent improvement on their performance using both the standard, IoU based, and new, GIo U based, performance measures on popular object detection benchmarks.
- MonoSLAM: Real-Time Single Camera SLAM4,359
The first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches is presented.
- Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age.3,655
What is now the de-facto standard formulation for SLAM is presented, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers.
- RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation3,218
RefineNet is presented, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections and introduces chained residual pooling, which captures rich background context in an efficient manner.
- Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age3,128
- Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments2,014
This work provides the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset and presents the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery.
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue1,670
This work proposes a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground-truth depths, and shows that this network trained on less than half of the KITTI dataset gives comparable performance to that of the state-of-the-art supervised methods for singleView depth estimation.
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- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields1,303
A deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF is presented, and a deep structured learning scheme which learns the unary and pairwise potentials of continuousCRF in a unified deep CNN framework is proposed.
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- Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation941
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
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- Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction703
The use of stereo sequences for learning depth and visual odometry enables the use of both spatial and temporal photometric warp error, and constrains the scene depth and camera motion to be in a common, real-world scale.
- Online Multi-Target Tracking Using Recurrent Neural Networks549
This work proposes for the first time, an end-to-end learning approach for online multi-target tracking based on recurrent neural networks (RNNs) and shows promising results obtained at ~300 Hz on a standard CPU.
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- SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning535
SayPlan is introduced, a scalable approach to LLM-based, large-scale task planning for robotics using 3D scene graph (3DSG) representations that exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a 'semantic search' for task-relevant subgraphs from a smaller, collapsed representation of the full graph.
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- Articulated Body Motion Capture by Stochastic Search482
A modified particle filter is developed which is shown to be effective at searching the high-dimensional configuration spaces encountered in visual tracking of articulated body motion and to be capable of recovering full articulated bodymotion efficiently.
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- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur464
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
- MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking418
This paper collects the first three releases of MOTChallenge and provides a categorization of state-of-the-art trackers and a broad error analysis, to help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
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- Multi-modal Cycle-Consistent Generalized Zero-Shot Learning389
This paper proposes the use of a constraint based on a new regularization for the GAN training that forces the generated visual features to reconstruct their original semantic features in a multi-modal cycle-consistent manner.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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