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Found 40 papers, 23 papers with code
SVG-Net: An SVG-based Trajectory Prediction Model
Finally, we illustrate how, by using SVG, one can benefit from datasets and advancements in other research fronts that also utilize the same input format.
Keypoint Communities
We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation.
Ranked #1 on
Car Pose Estimation
on ApolloCar3D
Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
The ability to predict unseen vehicles is critical for safety in autonomous driving.
Are socially-aware trajectory prediction models really socially-aware?
An attack is a small yet carefully-crafted perturbations to fail predictors.
Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions.
Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance.
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on
Multi-Person Pose Estimation
on COCO
Social NCE: Contrastive Learning of Socially-aware Motion Representations
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
Ranked #1 on
Trajectory Forecasting
on TrajNet++
Detecting 32 Pedestrian Attributes for Autonomous Vehicles
By increasing the number of attributes jointly learned, we highlight an issue related to the scales of gradients, which arises in MTL with numerous tasks.
Pedestrian Intention Prediction: A Multi-task Perspective
This work tries to solve this problem by jointly predicting the intention and visual states of pedestrians.
Perceiving Traffic from Aerial Images
Drones or UAVs, equipped with different sensors, have been deployed in many places especially for urban traffic monitoring or last-mile delivery.
Perceiving Humans: from Monocular 3D Localization to Social Distancing
Our neural network estimates human 3D body locations and their orientation with a measure of uncertainty.
MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization
Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots.
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions.
Ranked #1 on
Vector Graphics Animation
on SVG-Icons8
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
Ranked #3 on
Trajectory Prediction
on TrajNet++
Using Image Priors to Improve Scene Understanding
We propose a simple yet effective method for leveraging these image priors to improve semantic segmentation of images from sequential driving datasets.
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images.
Deep Visual Re-Identification with Confidence
We argue that such loss function is not suited for the visual re-identification task hence propose to model confidence in the representation learning framework.
Convolutional Relational Machine for Group Activity Recognition
We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relations between individual persons in image or video.
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
Ranked #9 on
Keypoint Detection
on COCO test-dev
Collaborative Sampling in Generative Adversarial Networks
The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling.
Enhancing Discrete Choice Models with Representation Learning
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates.
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework.
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Ranked #12 on
Trajectory Prediction
on Stanford Drone
CAR-Net: Clairvoyant Attentive Recurrent Network
We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene.
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection.
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals
Physiological signals such as heart rate can provide valuable information about an individual's state and activity.
Characterizing and Improving Stability in Neural Style Transfer
Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods.
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window.
Unsupervised Learning of Long-Term Motion Dynamics for Videos
We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos.
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
We present a unified framework for understanding human social behaviors in raw image sequences.
Ranked #2 on
Action Recognition
on Volleyball
Recurrent Attention Models for Depth-Based Person Identification
We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark.
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Different from the conventional LSTM, we share the information between multiple LSTMs through a new pooling layer.
Ranked #1 on
Trajectory Prediction
on Stanford Drone
(ADE (8/12) @K=5 metric)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image.
Ranked #4 on
Nuclear Segmentation
on Cell17
Towards Viewpoint Invariant 3D Human Pose Estimation
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image.
Ranked #4 on
Pose Estimation
on ITOP top-view
Knowledge Transfer for Scene-specific Motion Prediction
When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future.
Forecasting Social Navigation in Crowded Complex Scenes
We present an extensive evaluation where different methods for trajectory forecasting are evaluated and compared.
Learning to Track: Online Multi-Object Tracking by Decision Making
Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving.
Ranked #17 on
Multiple Object Tracking
on KITTI Tracking test
RGB-W: When Vision Meets Wireless
Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional "quasi-free" modality, namely, the wireless signal (e. g., wifi or Bluetooth) emitted by individuals' cell phones, referred to as RGB-W.
Socially-aware Large-scale Crowd Forecasting
In crowded spaces such as city centers or train stations, human mobility looks complex, but is often influenced only by a few causes.