Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Pennsylvania State UniversityUS558 papers
- University of Maryland, College ParkUS521 papers
- Massachusetts Institute of TechnologyUS457 papers
- University of California, BerkeleyUS447 papers
- Centre National de la Recherche ScientifiqueFR442 papers
- Columbia UniversityUS374 papers
- Université Libre de BruxellesBE370 papers
- The University of AdelaideAU343 papers
- California Institute of TechnologyUS337 papers
- Goddard Space Flight CenterUS327 papers
- University of Wisconsin–MadisonUS321 papers
- University of DelawareUS313 papers
232 papers · 1 filter
Improving Lunar Topography with Deep Learning Schrödinger Bridges
Matthew Repasky, Erwan Mazarico, Michael K. Barker +3
Increasing the resolution of planetary topography models can enable a better understanding of surface processes and geomorphology; however, existing analytical super-resolution met…
LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction
Zhengyang Wei, Renzhi Jing, Yiyi He +1
Extracting routable road networks from satellite imagery requires accurate topology recovery beyond pixel-level segmentation. Recent methods decompose the task into keypoint detect…
EdgeAttNet: Towards Barb-Aware Filament Segmentation
Victor Solomon, Piet Martens, Jingyu Liu +1
Accurate segmentation of solar filaments in H-alpha observations is critical for determining filament chirality, a key factor in the behavior of Coronal Mass Ejections (CMEs). Howe…
Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
Mehrdad Moradi, Marco Grasso, Bianca Maria Colosimo +1
Generative models have demonstrated significant success in anomaly detection and segmentation over the past decade. Recently, diffusion models have emerged as a powerful alternativ…
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM
Xuhui Guo, Tanmoy Dam, Rohan Dhamdhere +2
3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-ran…
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs
Wenqian Ye, Bohan Liu, Guangtao Zheng +6
Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machin…
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
Bo Dang, Wenchao Zhao, Yufeng Li +3
The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the de…
Shotluck Holmes: A Family of Efficient Small-Scale Large Language Vision Models For Video Captioning and Summarization
Richard Luo, Austin Peng, Adithya Vasudev +1
Video is an increasingly prominent and information-dense medium, yet it poses substantial challenges for language models. A typical video consists of a sequence of shorter segments…
TrajPRed: Trajectory Prediction with Region-based Relation Learning
Chen Zhou, Ghassan AlRegib, Armin Parchami +1
Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully autonomous systems. Human future trajectories are driven by two major stimuli, social…
HyCTAS: Multi-Objective Hybrid Convolution-Transformer Architecture Search for Real-Time Image Segmentation
Hongyuan Yu, Cheng Wan, Xiyang Dai +6
Real-time image segmentation demands architectures that preserve fine spatial detail while capturing global context under tight latency and memory budgets. Image segmentation is on…
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation
H. S. Tan, Kuancheng Wang, Rafe Mcbeth
In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular ex…
ScaleNet: An Unsupervised Representation Learning Method for Limited Information
Huili Huang, M. Mahdi Roozbahani
Although large-scale labeled data are essential for deep convolutional neural networks (ConvNets) to learn high-level semantic visual representations, it is time-consuming and impr…
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression
Jin Heo, Gregorie Phillips, Per-Erik Brodin +1
Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object detection and simultaneous localization and mapping are computationally intensive to mobile devices of li…
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar +2
This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the…
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
Hyeokhyen Kwon, Chaitra Hegde, Yashar Kiarashi +10
Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. I…
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
Shay Snyder, Hunter Thompson, Md Abdullah-Al Kaiser +3
Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its b…
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Pierre Marza, Laetitia Matignon, Olivier Simonin +3
Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and care…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
Going Beyond Nouns With Vision & Language Models Using Synthetic Data
Paola Cascante-Bonilla, Khaled Shehada, James Seale Smith +8
Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot ope…
Auditing Gender Presentation Differences in Text-to-Image Models
Yanzhe Zhang, Lu Jiang, Greg Turk +1
Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide ra…
Explaining Deep Models through Forgettable Learning Dynamics
Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib
Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we a…
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal +10
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequ…
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada, Lakshmi Sathidevi, Ran Liu +10
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the i…
Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
John Lambert, James Hays
High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect…