125 citations · 350 across the 33 of their papers we have counts for
43 papers
Autonomous Driving Research Requires a Community-Driven Data Paradigm
Jinsu Yoo, Zanming Huang, Katie Z Luo +5
Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from its unive…
Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs
Qiyuan Wu, Katie Z Luo, Bharath Hariharan +2
Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast modes, causing problems for mode…
When the City Teaches the Car: Label-Free 3D Perception from Infrastructure
Zhen Xu, Jinsu Yoo, Cristian Bautista +7
Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes impractical as deployment expand…
Color Bind: Exploring Color Perception in Text-to-Image Models
Shay Shomer-Chai, Wenxuan Peng, Bharath Hariharan +1
Text-to-image generation has recently seen remarkable success, granting users with the ability to create high-quality images through the use of text. However, contemporary methods…
MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
Shreelekha Revankar, Utkarsh Mall, Cheng Perng Phoo +2
Natural disasters cause devastating damage to communities and infrastructure every year. Effective disaster response is hampered by the difficulty of accessing affected areas durin…
RanDeS: Randomized Delta Superposition for Multi-Model Compression
Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov +1
From a multi-model compression perspective, model merging enables memory-efficient serving of multiple models fine-tuned from the same base, but suffers from degraded performance d…
Towards LLM Agents for Earth Observation
Chia Hsiang Kao, Wenting Zhao, Cheryl Lam +10
Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. In this work we ask: Are A…
Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene
Tai-Yu Pan, Sooyoung Jeon, Mengdi Fan +6
Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects. Collaborative autonomous driving (CAV) se…
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
Utkarsh Mall, Cheng Perng Phoo, Mia Chiquier +3
Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to mak…
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…
Scale-Aware Recognition in Satellite Images under Resource Constraints
Shreelekha Revankar, Cheng Perng Phoo, Utkarsh Mall +2
Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This p…
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo +3
Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmar…
Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning
Chia-Hsiang Kao, Bharath Hariharan
Despite its widespread use in neural networks, error backpropagation has faced criticism for its lack of biological plausibility, suffering from issues such as the backward locking…
Learning 3D Perception from Others' Predictions
Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan +7
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs re…
DiffuBox: Refining 3D Object Detection with Point Diffusion
Xiangyu Chen, Zhenzhen Liu, Katie Z Luo +10
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining h…
Better Monocular 3D Detectors with LiDAR from the Past
Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz +5
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their wid…
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu +3
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken o…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen +7
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we in…
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
Samar Khanna, Bram Wallace, Kavita Bala +1
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium re…
Stay Positive: Non-Negative Image Synthesis for Augmented Reality
Katie Luo, Guandao Yang, Wenqi Xian +3
In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an e…
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Jang Hyun Cho, Utkarsh Mall, Kavita Bala +1
We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric i…
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Yurong You, Carlos Andres Diaz-Ruiz, Yan Wang +4
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have show…
Few-Shot Classification with Feature Map Reconstruction Networks
Davis Wertheimer, Luming Tang, Bharath Hariharan
In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support feature…