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20212026
most citedRegion-level Active Detector Learning

3 citations · 8 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Multi-axis Analysis of Image Manipulation Localization

Keanu Nichols, Divya Appapogu, Giscard Biamby +3

Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years due to advances in generati…

cs.CV2024★ 1 cited

Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark

Tsung-Han Wu, Giscard Biamby, Jerome Quenum +4

Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest…

cs.CV2023★ 1 cited

See, Say, and Segment: Teaching LMMs to Overcome False Premises

Tsung-Han Wu, Giscard Biamby, David Chan +5

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…

cs.CV2022★ 1 cited

G^3: Geolocation via Guidebook Grounding

Grace Luo, Giscard Biamby, Trevor Darrell +2

We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks th…

cs.CV2021★ 2 cited

Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation

Giscard Biamby, Grace Luo, Trevor Darrell +1

Detecting out-of-context media, such as "mis-captioned" images on Twitter, is a relevant problem, especially in domains of high public significance. In this work we aim to develop…

cs.CV2021★ 3 cited

Region-level Active Detector Learning

Michael Laielli, Giscard Biamby, Dian Chen +6

Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level…