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Found 14 papers, 11 papers with code
Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation
Furthermore, the voxels in the boundary region are more challenging to differentiate than those in the interior.
MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction
Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information.
Osprey: Pixel Understanding with Visual Instruction Tuning
In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incorporating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding.
Label-efficient Segmentation via Affinity Propagation
In this work, we formulate the affinity modeling as an affinity propagation process, and propose a local and a global pairwise affinity terms to generate accurate soft pseudo labels.
Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport
Weakly-supervised image segmentation has recently attracted increasing research attentions, aiming to avoid the expensive pixel-wise labeling.
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation
To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud.
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution
In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention.
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.
Box-supervised Instance Segmentation with Level Set Evolution
A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance.
Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters
With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner.
Deep Level Set for Box-supervised Instance Segmentation in Aerial Images
Instead of learning the pairwise affinity, the level set method with the carefully designed energy functions treats the object segmentation as curve evolution, which is able to accurately recover the object's boundaries and prevent the interference from the indistinguishable background and similar objects.
Translational Symmetry-Aware Facade Parsing for 3D Building Reconstruction
Although enjoying the merits of promising results on the semantic parsing, deep learning methods cannot directly make use of the architectural rules, which play an important role for man-made structures.
Oriented RepPoints for Aerial Object Detection
In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings.
Ranked #7 on
Oriented Object Detection
on DOTA 1.0
Smart Pacing for Effective Online Ad Campaign Optimization
In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals.
