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20152022
most citedTraining Deeper Convolutional Networks with Deep Supervision

167 citations · 237 across the 7 of their papers we have counts for

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13 papers · 1 filter

cs.CV20221 cited

Robust Online Video Instance Segmentation with Track Queries

Zitong Zhan, Daniel McKee, Svetlana Lazebnik

Recently, transformer-based methods have achieved impressive results on Video Instance Segmentation (VIS). However, most of these top-performing methods run in an offline manner by…

cs.CV20222 cited

Transfer of Representations to Video Label Propagation: Implementation Factors Matter

Daniel McKee, Zitong Zhan, Bing Shuai +3

This work studies feature representations for dense label propagation in video, with a focus on recently proposed methods that learn video correspondence using self-supervised sign…

cs.CV2021

Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents

Shivansh Patel, Saim Wani, Unnat Jain +4

Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable…

cs.CV2021

Multi-Object Tracking with Hallucinated and Unlabeled Videos

Daniel McKee, Bing Shuai, Andrew Berneshawi +4

In this paper, we explore learning end-to-end deep neural trackers without tracking annotations. This is important as large-scale training data is essential for training deep neura…

cs.CV2021

GridToPix: Training Embodied Agents with Minimal Supervision

Unnat Jain, Iou-Jen Liu, Svetlana Lazebnik +3

While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via care…

cs.CV20201 cited

A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks

Unnat Jain, Luca Weihs, Eric Kolve +4

Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-a…