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20232026
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7 papers · 1 filter

cs.CV2024

Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training

Haicheng Wang, Chen Ju, Weixiong Lin +9

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream…

cs.IR2024

Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation

Jiajun Cui, Xu Chen, Shuai Xiao +4

For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely use…

cs.IR2024

Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao

Xu Chen, Zida Cheng, Yuangang Pan +6

Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strengt…

cs.CV2024

Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models

Chen Ju, Haicheng Wang, Haozhe Cheng +6

Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and dela…

cs.CV2024

Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos

Zhengze Xu, Mengting Chen, Zhao Wang +6

Video try-on is a challenging task and has not been well tackled in previous works. The main obstacle lies in preserving the details of the clothing and modeling the coherent motio…

cs.CV2024

Cell Variational Information Bottleneck Network

Zhonghua Zhai, Chen Ju, Jinsong Lan +1

In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined wit…

cs.CV2024

Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment

Mengting Chen, Xi Chen, Zhonghua Zhai +4

This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way. Different from previous methods, Wear-Any-Way is a customizable solution. Besides generating high-f…