Publications

Tracking Every Thing in the Wild

Tracking Every Thing in the Wild

ECCV 2022 We introduce a new metric, Track Every Thing Accuracy (TETA), and a Track Every Thing tracker (TETer), which performs association using Class Exemplar Matching (CEM).

Video Mask Transfiner for High-Quality Video Instance Segmentation

Video Mask Transfiner for High-Quality Video Instance Segmentation

ECCV 2022 We propose Video Mask Transfiner (VMT) method, capable of leveraging fine-grained high-resolution features thanks to a highly efficient video transformer structure.

SAGA: Stochastic Whole-Body Grasping with Contact

SAGA: Stochastic Whole-Body Grasping with Contact

ECCV 2022 We propose SAGA (StochAstic whole-body Grasping with contAct).

Learning Online Multi-Sensor Depth Fusion

Learning Online Multi-Sensor Depth Fusion

ECCV 2022 Our method fuses multi-sensor depth streams regardless of time synchronization and calibration and generalizes well with little training data.

TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation

TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation

ECCV 2022 We introduce the more general taxonomy adaptive cross-domain semantic segmentation (TACS) problem, allowing for inconsistent taxonomies between the two domains.

Mask Transfiner for High-Quality Instance Segmentation

Mask Transfiner for High-Quality Instance Segmentation

CVPR 2022 we present Mask Transfiner for high-quality and efficient instance segmentation, which predicts highly accurate instance masks at a low computational cost using quadtree transformer.

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

CVPR 2022 We introduce the largest synthetic dataset for autonomous driving to study continuous domain adaptation and multi-task perception.

Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences

Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences

CVPR 2022 We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching.

Transforming Model Prediction for Tracking

Transforming Model Prediction for Tracking

CVPR 2022 We propose a tracker architecture employing a Transformer-based model prediction module.

Generative Cooperative Learning for Unsupervised Video Anomaly Detection

Generative Cooperative Learning for Unsupervised Video Anomaly Detection

CVPR 2022 Our method exploits the low frequency of anomalies towards building a cross-supervision between a generator and a discriminator.

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

CVPR 2022 We propose a Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.

LiDAR Snowfall Simulation for Robust 3D Object Detection

LiDAR Snowfall Simulation for Robust 3D Object Detection

CVPR 2022 Oral We propose a physically based method to simulate the effect of snowfall on real clear weather LiDAR point clouds.

On the Practicality of Deterministic Epistemic Uncertainty

On the Practicality of Deterministic Epistemic Uncertainty

ICML 2022 We provide a taxonomy of DUMs, evaluate their calibration under continuous distributional shifts, and extend them to semantic segmentation.

Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking

TPAMI 2022 We combine quasi-dense tracking on 2D images and motion prediction in 3D space to achieve significant advance in 3D object tracking from monocular videos.

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

WACV 2022 We explore general flows as a fidelity-based alternative to the L1 objective.

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

NeurIPS 2021 Spotlight We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation.

Robust Object Detection via Instance-Level Temporal Cycle Confusion

Robust Object Detection via Instance-Level Temporal Cycle Confusion

ICCV 2021 We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors.

Warp Consistency for Unsupervised Learning of Dense Correspondences

Warp Consistency for Unsupervised Learning of Dense Correspondences

ICCV 2021 Oral We propose Warp Consistency, an unsupervised learning objective for dense correspondence regression.

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

ICCV 2021 Oral We propose a pixel-wise contrastive algorithm for semantic segmentation in the fully supervised setting.

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

ICCV 2021 Oral We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks.

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

ICCV 2021 We demonstrated that an RL coach (Roach) would be a better choice to supervise imitation learning agents.

Quasi-Dense Similarity Learning for Multiple Object Tracking

Quasi-Dense Similarity Learning for Multiple Object Tracking

CVPR 2021 Oral We propose a simple yet effective multi-object tracking method in this paper.

Instance-Aware Predictive Navigation in Multi-Agent Environments

Instance-Aware Predictive Navigation in Multi-Agent Environments

ICRA 2021 A new visual model-based RL method with consideration of multiple hypotheses for future object movement.

Dense Prediction with Attentive Feature Aggregation

Dense Prediction with Attentive Feature Aggregation

arXiv 2021 We propose Attentive Feature Aggregation (AFA) to exploit both spatial and channel information for semantic segmentation and boundary detection.