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Found 15 papers, 1 papers with code
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
GLOW: Global Layout Aware Attacks on Object Detection
More desired attacks, to this end, should be able to fool defenses with such consistency checks.
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
ESCNet: Gaze Target Detection With the Understanding of 3D Scenes
This paper aims to address the single image gaze target detection problem.
The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation
We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences.
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction
Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes.
Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts
We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space.
Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing
Face anti-spoofing (FAS) seeks to discriminate genuine faces from fake ones arising from any type of spoofing attack.
SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction
Our second contribution is a novel method that generates diverse predictions while accounting for scene semantics and multi-agent interactions, with constant-time inference independent of the number of agents.
Understanding Road Layout from Videos as a Whole
(2) Introducing the LSTM and FTM modules improves the prediction consistency in videos.
Active Adversarial Domain Adaptation
Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains.
A Parametric Top-View Representation of Complex Road Scenes
In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input.
Active Learning for Human Pose Estimation
Annotating human poses in realistic scenes is very time consuming, yet necessary for training human pose estimators.
Learning Dynamic Hierarchical Models for Anytime Scene Labeling
With increasing demand for efficient image and video analysis, test-time cost of scene parsing becomes critical for many large-scale or time-sensitive vision applications.
Multiclass Semantic Video Segmentation With Object-Level Active Inference
To scale up our method, we adopt an active inference strategy to improve the efficiency, which adaptively selects object subgraphs in the object-augmented dense CRF.
