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Found 15 papers, 9 papers with code
Better Call SAL: Towards Learning to Segment Anything in Lidar
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.
I Can't Believe It's Not Scene Flow!
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.
Revisiting Few-Shot Object Detection with Vision-Language Models
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.
Long-Tailed 3D Detection via 2D Late Fusion
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).
An Empirical Analysis of Range for 3D Object Detection
LiDAR-based 3D detection plays a vital role in autonomous navigation.
ZeroFlow: Scalable Scene Flow via Distillation
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
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning
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.
A Brief Survey on Person Recognition at a Distance
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.
Towards Long-Tailed 3D Detection
Moreover, semantic classes are often organized within a hierarchy, e. g., tail classes such as child and construction-worker are arguably subclasses of pedestrian.
Forecasting from LiDAR via Future Object Detection
Object detection and forecasting are fundamental components of embodied perception.
A Synthesis-Based Approach for Thermal-to-Visible Face Verification
Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
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.
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
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.
Deep k-NN Defense against Clean-label Data Poisoning Attacks
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.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
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
