no code implementations • 19 Mar 2024 • Aljoša Ošep, Tim Meinhardt, Francesco Ferroni, Neehar Peri, Deva Ramanan, Laura Leal-Taixé
We propose $\texttt{SAL}$ ($\texttt{S}$egment $\texttt{A}$nything in $\texttt{L}$idar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision.
1 code implementation • 7 Mar 2024 • Ishan Khatri, Kyle Vedder, Neehar Peri, Deva Ramanan, James Hays
Current scene flow methods broadly fail to describe motion on small objects, and current scene flow evaluation protocols hide this failure by averaging over many points, with most drawn larger objects.
1 code implementation • 22 Dec 2023 • Anish Madan, Neehar Peri, Shu Kong, Deva Ramanan
In this work, we propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external datasets and fine-tuned on K-shots per target class.
no code implementations • 18 Dec 2023 • Yechi Ma, Neehar Peri, Shuoquan Wei, Wei Hua, Deva Ramanan, Yanan Li, Shu Kong
Autonomous vehicles (AVs) must accurately detect objects from both common and rare classes for safe navigation, motivating the problem of Long-Tailed 3D Object Detection (LT3D).
no code implementations • 8 Aug 2023 • Neehar Peri, Mengtian Li, Benjamin Wilson, Yu-Xiong Wang, James Hays, Deva Ramanan
LiDAR-based 3D detection plays a vital role in autonomous navigation.
1 code implementation • 17 May 2023 • Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays
Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds.
Ranked #3 on Self-supervised Scene Flow Estimation on Argoverse 2
1 code implementation • 11 Mar 2023 • Wesley Chen, Andrew Edgley, Raunak Hota, Joshua Liu, Ezra Schwartz, Aminah Yizar, Neehar Peri, James Purtilo
Lately, the academic community has studied 3D object detection in the context of autonomous vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse 2. 0.
no code implementations • 17 Dec 2022 • Chrisopher B. Nalty, Neehar Peri, Joshua Gleason, Carlos D. Castillo, Shuowen Hu, Thirimachos Bourlai, Rama Chellappa
Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras.
1 code implementation • 16 Nov 2022 • Neehar Peri, Achal Dave, Deva Ramanan, Shu Kong
Moreover, semantic classes are often organized within a hierarchy, e. g., tail classes such as child and construction-worker are arguably subclasses of pedestrian.
1 code implementation • CVPR 2022 • Neehar Peri, Jonathon Luiten, Mengtian Li, Aljoša Ošep, Laura Leal-Taixé, Deva Ramanan
Object detection and forecasting are fundamental components of embodied perception.
no code implementations • 21 Aug 2021 • Neehar Peri, Joshua Gleason, Carlos D. Castillo, Thirimachos Bourlai, Vishal M. Patel, Rama Chellappa
Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
1 code implementation • NeurIPS 2021 • Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson
In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs.
no code implementations • ECCV 2020 • Pirazh Khorramshahi, Neehar Peri, Jun-Cheng Chen, Rama Chellappa
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.
1 code implementation • 29 Sep 2019 • Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson
Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference.
1 code implementation • ICCV 2019 • Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation Vehicle Re-Identification