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Found 5 papers, 2 papers with code
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image.
G^3: Geolocation via Guidebook Grounding
We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game.
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation
Detecting out-of-context media, such as "mis-captioned" images on Twitter, is a relevant problem, especially in domains of high public significance.
Region-level Active Detector Learning
Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria.
Minimax Active Learning
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
