activity
20202023
most citedGPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation

12 citations · 28 across the 7 of their papers we have counts for

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10 papers · 1 filter

cs.CV2023

Asymmetric Image Retrieval with Cross Model Compatible Ensembles

Ori Linial, Alon Shoshan, Nadav Bhonker +4

The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is u…

cs.CV2022

Identity Preserving Loss for Learned Image Compression

Jiuhong Xiao, Lavisha Aggarwal, Prithviraj Banerjee +2

Deep learning model inference on embedded devices is challenging due to the limited availability of computation resources. A popular alternative is to perform model inference on th…

cs.CV2022★ 7 cited

OutfitTransformer: Learning Outfit Representations for Fashion Recommendation

Rohan Sarkar, Navaneeth Bodla, Mariya I. Vasileva +4

Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We pr…

cs.CV2022★ 4 cited

Efficient Video Instance Segmentation via Tracklet Query and Proposal

Jialian Wu, Sudhir Yarram, Hui Liang +4

Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input e…

cs.CV2021

Synthetic Data for Model Selection

Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +2

Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore,…

cs.CV2021

Energy-Based Learning for Scene Graph Generation

Mohammed Suhail, Abhay Mittal, Behjat Siddiquie +4

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores…