44 citations · 90 across the 34 of their papers we have counts for
5 papers · 1 filter
Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment
Sina Malakouti, Adriana Kovashka
Existing domain adaptation (DA) and generalization (DG) methods in object detection enforce feature alignment in the visual space but face challenges like object appearance variabi…
Hypernymization of named entity-rich captions for grounding-based multi-modal pretraining
Giacomo Nebbia, Adriana Kovashka
Named entities are ubiquitous in text that naturally accompanies images, especially in domains such as news or Wikipedia articles. In previous work, named entities have been identi…
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth
Cagri Gungor, Adriana Kovashka
Despite recent attention and exploration of depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier metho…
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection
Kyle Buettner, Adriana Kovashka
Vision-language alignment learned from image-caption pairs has been shown to benefit tasks like object recognition and detection. Methods are mostly evaluated in terms of how well…
VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection
Arushi Rai, Adriana Kovashka
The use of large-scale vision-language datasets is limited for object detection due to the negative impact of label noise on localization. Prior methods have shown how such large-s…