activity
20122026
most citedStepwise Goal-Driven Networks for Trajectory Prediction

213 citations · 963 across the 50 of their papers we have counts for

collaborators

55 papers

cs.CV2026

Semantic Privacy Protection with Utility Preservation for 3D Point Clouds

Jinchang zhang, Jiakai Lin, David Crandall +1

Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on geometric perturbation or de…

cs.CV2023★ 1 cited

Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision

Samuel Goree, David Crandall

In her influential 1988 paper, Situated Knowledges, Donna Haraway uses vision and perspective as a metaphor to discuss scientific knowledge. Today, egocentric computer vision discu…

cs.CV2023

SePaint: Semantic Map Inpainting via Multinomial Diffusion

Zheng Chen, Deepak Duggirala, David Crandall +2

Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the curr…

cs.CY2022

Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper

Samuel Goree, Gabriel Appleby, David Crandall +1

Research papers, in addition to textual documents, are a designed interface through which researchers communicate. Recently, rapid growth has transformed that interface in many fie…

cs.CV2022★ 92 cited

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds

Junbo Yin, Jianbing Shen, Xin Gao +2

Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in m…

cs.CV2022★ 21 cited

Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition

Satoshi Tsutsui, Yanwei Fu, David Crandall

One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image…