1 code implementation • 14 Mar 2024 • Vibashan VS, Shubhankar Borse, Hyojin Park, Debasmit Das, Vishal Patel, Munawar Hayat, Fatih Porikli
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
no code implementations • 16 Sep 2023 • David Unger, Nikhil Gosala, Varun Ravi Kumar, Shubhankar Borse, Abhinav Valada, Senthil Yogamani
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.
no code implementations • 6 Jun 2023 • Shubhankar Borse, Senthil Yogamani, Marvin Klingner, Varun Ravi, Hong Cai, Abdulaziz Almuzairee, Fatih Porikli
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e. g., drivable area, in autonomous driving.
no code implementations • ICCV 2023 • Minghan Zhu, Shizhong Han, Hong Cai, Shubhankar Borse, Maani Ghaffari, Fatih Porikli
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
no code implementations • 3 Mar 2023 • Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar, Behnaz Rezaei, Venkatraman Narayanan, Senthil Yogamani, Fatih Porikli
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.
no code implementations • CVPR 2023 • Shubhankar Borse, Debasmit Das, Hyojin Park, Hong Cai, Risheek Garrepalli, Fatih Porikli
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.
no code implementations • 24 Feb 2023 • Debasmit Das, Shubhankar Borse, Hyojin Park, Kambiz Azarian, Hong Cai, Risheek Garrepalli, Fatih Porikli
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.
no code implementations • CVPR 2023 • Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar, Behnaz Rezaei, Venkatraman Narayanan, Senthil Yogamani, Fatih Porikli
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
1 code implementation • 13 Oct 2022 • Kaifeng Zhang, Yang Fu, Shubhankar Borse, Hong Cai, Fatih Porikli, Xiaolong Wang
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.
no code implementations • 13 Oct 2022 • Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar, Hong Cai, Abdulaziz Almuzairee, Senthil Yogamani, Fatih Porikli
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
1 code implementation • 17 Jun 2022 • Hanzhe Hu, Yinbo Chen, Jiarui Xu, Shubhankar Borse, Hong Cai, Fatih Porikli, Xiaolong Wang
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
1 code implementation • 16 Jun 2022 • Dushyant Mehta, Andrii Skliar, Haitam Ben Yahia, Shubhankar Borse, Fatih Porikli, Amirhossein Habibian, Tijmen Blankevoort
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.
no code implementations • CVPR 2022 • Shubhankar Borse, Hyojin Park, Hong Cai, Debasmit Das, Risheek Garrepalli, Fatih Porikli
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
no code implementations • 3 Nov 2021 • Shubhankar Borse, Hong Cai, Yizhe Zhang, Fatih Porikli
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)
no code implementations • 24 Oct 2021 • Hong Cai, Janarbek Matai, Shubhankar Borse, Yizhe Zhang, Amin Ansari, Fatih Porikli
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.
no code implementations • 24 Oct 2021 • Yizhe Zhang, Shubhankar Borse, Hong Cai, Ying Wang, Ning Bi, Xiaoyun Jiang, Fatih Porikli
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.
1 code implementation • 24 Oct 2021 • Yizhe Zhang, Shubhankar Borse, Hong Cai, Fatih Porikli
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.
1 code implementation • CVPR 2021 • Shubhankar Borse, Ying Wang, Yizhe Zhang, Fatih Porikli
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)