Search Results for author:
Found 18 papers, 6 papers with code
PosSAM: Panoptic Open-vocabulary Segment Anything
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework.
Ranked #1 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+2
Multi-camera Bird's Eye View Perception for Autonomous Driving
Surround vision systems that are pretty common in new vehicles use the IPM principle to generate a BEV image and to show it on display to the driver.
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e. g., drivable area, in autonomous driving.
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
X$^3$KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information.
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
Ranked #5 on
3D Object Detection
on nuscenes Camera-Radar
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations.
Learning Implicit Feature Alignment Function for Semantic Segmentation
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Simple and Efficient Architectures for Semantic Segmentation
Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware.
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers.
Ranked #4 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
In order to enable such knowledge distillation across two different visual tasks, we introduce a small, trainable network that translates the predicted depth map to a semantic segmentation map, which can then be supervised by the teacher network.
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation
Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames.
Perceptual Consistency in Video Segmentation
More specifically, by measuring the perceptual consistency between the predicted segmentation and the available ground truth on a nearby frame and combining it with the segmentation confidence, we can accurately assess the classification correctness on each pixel.
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries.
Ranked #5 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
