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Found 24 papers, 8 papers with code
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
The alignment module is responsible for cross-style and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events.
Single-View View Synthesis with Self-Rectified Pseudo-Stereo
In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction.
Monocular BEV Perception of Road Scenes via Front-to-Top View Projection
We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding.
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem.
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps
In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories.
Ranked #2 on
Trajectory Prediction
on Stanford Drone
Semi-Supervised Domain Generalization with Evolving Intermediate Domain
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement
In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations.
Ranked #2 on
Land Cover Classification
on DeepGlobe
Reciprocal Transformations for Unsupervised Video Object Segmentation
Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers.
Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck
Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis.
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Autonomous Driving
Monocular Cross-View Road Scene Parsing(Road)
+2
An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data
CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution.
Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation.
HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions
To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
Recurrent Distillation based Crowd Counting
Besides, leveraging our density map generation method, we propose an iterative distillation algorithm to progressively enhance our model with identical network structures, without significantly sacrificing the dimension of the output density maps.
Over-crowdedness Alert! Forecasting the Future Crowd Distribution
In this paper, we formulate a novel crowd analysis problem, in which we aim to predict the crowd distribution in the near future given sequential frames of a crowd video without any identity annotations.
Learning Resilient Behaviors for Navigation Under Uncertainty
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically.
Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery
We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles.
Enhancement Mask for Hippocampus Detection and Segmentation
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance
To our best knowledge, this is the first attempt to analyze individual extraversion of crowd motions based on trajectories.
Deformable Object Tracking with Gated Fusion
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations.
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature.
Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking
We present a multiple-person tracking algorithm, based on combining particle filters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion.
