12 citations · 29 across the 9 of their papers we have counts for
12 papers
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation
Jinming Li, Wentao Zhang, Tian Wang +3
Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models…
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…
Contrastive Learning for Interactive Recommendation in Fashion
Karin Sevegnani, Arjun Seshadri, Tian Wang +4
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate indepen…
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…
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…
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…
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,…
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…
GAN-Control: Explicitly Controllable GANs
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +1
We present a framework for training GANs with explicit control over generated images. We are able to control the generated image by settings exact attributes such as age, pose, exp…
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
Igor Kviatkovsky, Nadav Bhonker, Gerard Medioni
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: bei…
AOWS: Adaptive and optimal network width search with latency constraints
Maxim Berman, Leonid Pishchulin, Ning Xu +2
Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the targ…
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
Donghyun Kim, Tian Lan, Chuhang Zou +5
Prior work in multi-task learning has mainly focused on predictions on a single image. In this work, we present a new approach for multi-task learning from videos via efficient int…