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Found 14 papers, 4 papers with code
UPSNet: A Unified Panoptic Segmentation Network
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
Ranked #3 on
Panoptic Segmentation
on Indian Driving Dataset
Deep Watershed Transform for Instance Segmentation
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.
Ranked #1000000000 on
Instance Segmentation
on Cityscapes test
Learning deep structured active contours end-to-end
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications.
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors.
TorontoCity: Seeing the World with a Million Eyes
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
Exploiting Semantic Information and Deep Matching for Optical Flow
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving.
Deep Multi-Sensor Lane Detection
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving.
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time.
Improving self-supervised representation learning via sequential adversarial masking
Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision.
Implicit Surface Contrastive Clustering for LiDAR Point Clouds
The first task focuses on learning semantic information by sorting local groups of points in the scene into a globally consistent set of semantically meaningful clusters using contrastive learning.
AffordanceLLM: Grounding Affordance from Vision Language Models
Affordance grounding refers to the task of finding the area of an object with which one can interact.
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities.
