151 citations · 197 across the 6 of their papers we have counts for
9 papers
PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi +26
Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The researc…
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors
Samreen Anjum, Suyog Jain, Danna Gurari
We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a mo…
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani +98
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric…
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
Danna Gurari, Yinan Zhao, Suyog Dutt Jain +2
Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need o…
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
Bo Xiong, Suyog Dutt Jain, Kristen Grauman
We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all…
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
Danna Gurari, Kun He, Bo Xiong +6
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision syste…
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
Suyog Dutt Jain, Bo Xiong, Kristen Grauman
We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmenta…
Pixel Objectness
Suyog Dutt Jain, Bo Xiong, Kristen Grauman
We propose an end-to-end learning framework for generating foreground object segmentations. Given a single novel image, our approach produces pixel-level masks for all "object-like…
Click Carving: Segmenting Objects in Video with Point Clicks
Suyog Dutt Jain, Kristen Grauman
We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of intere…