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20172025
most citedThe Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

14 citations · 40 across the 6 of their papers we have counts for

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11 papers · 1 filter

cs.CV2025

AutoScape: Geometry-Consistent Long-Horizon Scene Generation

Jiacheng Chen, Ziyu Jiang, Mingfu Liang +5

This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically cons…

cs.CV2024

AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

Mingfu Liang, Jong-Chyi Su, Samuel Schulter +4

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution,…

cs.CV202112 cited

The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

Jong-Chyi Su, Subhransu Maji

Semi-iNat is a challenging dataset for semi-supervised classification with a long-tailed distribution of classes, fine-grained categories, and domain shifts between labeled and unl…

cs.CV20217 cited

A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

Jong-Chyi Su, Zezhou Cheng, Subhransu Maji

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Ou…

cs.CV202114 cited

The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

Jong-Chyi Su, Subhransu Maji

This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~\cite{semi-aves} at the FGVC7 workshop at CVP…

cs.CV2020

On Equivariant and Invariant Learning of Object Landmark Representations

Zezhou Cheng, Jong-Chyi Su, Subhransu Maji

Given a collection of images, humans are able to discover landmarks by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely…