3 citations · 8 across the 8 of their papers we have counts for
8 papers
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…
Pre-training Auto-regressive Robotic Models with 4D Representations
Dantong Niu, Yuvan Sharma, Haoru Xue +5
Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highli…
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…
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning
Dantong Niu, Yuvan Sharma, Giscard Biamby +5
In recent years, instruction-tuned Large Multimodal Models (LMMs) have been successful at several tasks, including image captioning and visual question answering; yet leveraging th…
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…
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…
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…
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…