no code implementations • 11 Apr 2024 • Jamie Menjay Lin, Jisoo Jeong, Hong Cai, Risheek Garrepalli, Kai Wang, Fatih Porikli
Optical flow estimation is crucial to a variety of vision tasks.
no code implementations • 26 Mar 2024 • Jisoo Jeong, Hong Cai, Risheek Garrepalli, Jamie Menjay Lin, Munawar Hayat, Fatih Porikli
We propose OCAI, a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between.
no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #2 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • 13 Dec 2023 • Amirhossein Habibian, Amir Ghodrati, Noor Fathima, Guillaume Sautiere, Risheek Garrepalli, Fatih Porikli, Jens Petersen
This work aims to improve the efficiency of text-to-image diffusion models.
no code implementations • IEEE/CVF International Conference on Computer Vision (ICCV) 2023 • Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, Fatih Porikli
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #12 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 9 Jun 2023 • Risheek Garrepalli, Jisoo Jeong, Rajeswaran C Ravindran, Jamie Menjay Lin, Fatih Porikli
Also, we present a novel dynamic coarse-to-fine cost volume processing during various stages of refinement to avoid multiple levels of cost volumes.
no code implementations • CVPR 2023 • Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames.
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 • 22 Sep 2022 • Risheek Garrepalli
The second is to introduce Oracle representation learning, which produces a representation that is guaranteed to be sufficient for accurate anomaly detection.
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.
1 code implementation • ICML 2018 • Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks
Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates.