most citedLearning to Ground Multi-Agent Communication with Autoencoders

16 citations · 43 across the 13 of their papers we have counts for

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cs.CV2022

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed +4

We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of t…

cs.CV20228 cited

CNeRV: Content-adaptive Neural Representation for Visual Data

Hao Chen, Matt Gwilliam, Bo He +2

Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…

cs.CV20221 cited

Diversified Dynamic Routing for Vision Tasks

Botos Csaba, Adel Bibi, Yanwei Li +2

Deep learning models for vision tasks are trained on large datasets under the assumption that there exists a universal representation that can be used to make predictions for all s…

cs.CV20221 cited

Raising the Bar on the Evaluation of Out-of-Distribution Detection

Jishnu Mukhoti, Tsung-Yu Lin, Bor-Chun Chen +4

In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datas…

cs.CV20226 cited

VRAG: Region Attention Graphs for Content-Based Video Retrieval

Kennard Ng, Ser-Nam Lim, Gim Hee Lee

Content-based Video Retrieval (CBVR) is used on media-sharing platforms for applications such as video recommendation and filtering. To manage databases that scale to billions of v…

cs.CV2021

Cross-Modal Retrieval Augmentation for Multi-Modal Classification

Shir Gur, Natalia Neverova, Chris Stauffer +3

Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…