ICCV 2023 A new image super-resolution model, dual aggregation Transformer (DAT), that aggregates spatial and channel features in the dual manner, achieves state-of-the-art performance.
ICCV 2023 We propose a graph-based method for the recognition of chemical structure images.
ICCV 2023 We propose 3D point PE with depth prior to localize the 2D feature, and it unifies representation of positional encoding for both image feature and object query.
IROS 2023 This paper presents a method for learning deep sensorimotor policies for vision-based drone racing with Learning by Cheating, which achieves robust performance against visual disturbances by learning well-aligned image embeddings using contrastive learning and data augmentation.
ICCV 2023 VTD is a promising new direction for exploring the unification of perception tasks in autonomous driving.
ICCV 2023 We propose a method for dense 3D reconstruction and ego-motion estimation from multi-camera input in dynamic environments.
arXiv 2023 Extending SAM to video segmentation with point-based tracking to demonstrate strong zero-shot performance across popular video segmentation benchmarks.
CVPR 2023 We introduce the first open-vocabulary multiple object tracker OVTrack trained from only static images and an evaluation benchmark.
CVPR 2023 We propose a monocular depth estimation method which represents internally the scene as a finite set of concepts via a continuous-discrete-continuous bottleneck
CVPR 2023 We remove video and image mask annptation necessity for training highly accurate VIS models.
RA-L 2023 We introduce an uncertainty-driven Dense Two-View SfM pipeline.
ICRA 2023 We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving.
WACV 2023 We propose Attentive Feature Aggregation (AFA) to exploit both spatial and channel information for semantic segmentation and boundary detection.
WACV 2023 We propose to enhance actor feature representation under large motion by tracking actors and performing temporal feature aggregation along the respective tracks.
WACV 2023 We find that jointly training a dense prediction task with a self-supervised task can consistently improve the performance of the target task.
CoRL 2022 We propose a method for panoramic 3D object tracking, called CC-3DT, that associates and models object trajectories both temporally and across views.
RA-L 2022 This paper introduces a ray-based volumetric uncertainty estimator, which computes the entropy of the weight distribution of the color samples along each ray of the object’s implicit neural representation.
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).
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.
ECCV 2022 We propose SAGA (StochAstic whole-body Grasping with contAct).
ECCV 2022 Our method fuses multi-sensor depth streams regardless of time synchronization and calibration and generalizes well with little training data.
ECCV 2022 We introduce the more general taxonomy adaptive cross-domain semantic segmentation (TACS) problem, allowing for inconsistent taxonomies between the two domains.
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.
CVPR 2022 We introduce the largest synthetic dataset for autonomous driving to study continuous domain adaptation and multi-task perception.