activity
20142023
most citedDeep Domain Confusion: Maximizing for Domain Invariance

2.4k citations · 2.5k across the 21 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL20231 cited

WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset

Andrea Burns, Krishna Srinivasan, Joshua Ainslie +5

Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in…

cs.CV20232 cited

VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting

Dina Bashkirova, Samarth Mishra, Diala Lteif +16

Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…

cs.CV20231 cited

Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection

Kuniaki Saito, Donghyun Kim, Piotr Teterwak +2

Building object detectors that are robust to domain shifts is critical for real-world applications. Prior approaches fine-tune a pre-trained backbone and risk overfitting it to in-…

cs.CV20231 cited

MaskSketch: Unpaired Structure-guided Masked Image Generation

Dina Bashkirova, Jose Lezama, Kihyuk Sohn +2

Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels o…

cs.CV20232 cited

Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4

In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using label…