11 citations · 29 across the 6 of their papers we have counts for
7 papers
Grounded Image Text Matching with Mismatched Relation Reasoning
Yu Wu, Yana Wei, Haozhe Wang +3
This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities o…
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
Zhili Ng, Haozhe Wang, Zhengshen Zhang +2
In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic da…
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
Haozhe Wang, Chao Du, Panyan Fang +3
The proliferation of the Internet has led to the emergence of online advertising, driven by the mechanics of online auctions. In these repeated auctions, software agents participat…
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Haozhe Wang, Chao Du, Panyan Fang +4
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject t…
PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes
Haozhe Wang, Zhiyang Liu, Lei Zhou +2
Vision-based grasp estimation is an essential part of robotic manipulation tasks in the real world. Existing planar grasp estimation algorithms have been demonstrated to work well…
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
Dehong Gao, Linbo Jin, Ben Chen +5
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry. Different from the matching in the general domain, the fashion matching is r…
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning
Haozhe Wang, Jiale Zhou, Xuming He
Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks. While previous efforts attem…