93 citations · 341 across the 14 of their papers we have counts for
14 papers
Semantic and Visual Evidence for Efficient Long-Video Reasoning: A Solution for the HD-EPIC VQA Challenge
Yinsong Xu, Wei Jing, Liuxin Zhang +2
Understanding long-form egocentric videos remains challenging for multimodal large language models (MLLMs) due to limited context length and insufficient grounding of fine-grained…
Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model
En Yen Puang, Hao Zhang, Hongyuan Zhu +1
In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data. The propose…
Temporal Sentence Grounding in Videos: A Survey and Future Directions
Hao Zhang, Aixin Sun, Wei Jing +1
Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically…
Towards Debiasing Temporal Sentence Grounding in Video
Hao Zhang, Aixin Sun, Wei Jing +1
The temporal sentence grounding in video (TSGV) task is to locate a temporal moment from an untrimmed video, to match a language query, i.e., a sentence. Without considering bias i…
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-…
Domain Generalization for Vision-based Driving Trajectory Generation
Yunkai Wang, Dongkun Zhang, Yuxiang Cui +5
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for visi…
Parallel Attention Network with Sequence Matching for Video Grounding
Hao Zhang, Aixin Sun, Wei Jing +3
Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequ…
Video Corpus Moment Retrieval with Contrastive Learning
Hao Zhang, Aixin Sun, Wei Jing +4
Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corres…
Natural Language Video Localization: A Revisit in Span-based Question Answering Framework
Hao Zhang, Aixin Sun, Wei Jing +3
Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve th…
Context Modeling with Evidence Filter for Multiple Choice Question Answering
Sicheng Yu, Hao Zhang, Wei Jing +1
Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that su…
Span-based Localizing Network for Natural Language Video Localization
Hao Zhang, Aixin Sun, Wei Jing +1
Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existi…
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
Tianying Wang, Hao Zhang, Wei Qi Toh +5
Learning-based methods have been used to pro-gram robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for…
RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization
Tianying Wang, Wei Qi Toh, Hao Zhang +4
Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system…
6D Pose Estimation with Correlation Fusion
Yi Cheng, Hongyuan Zhu, Ying Sun +6
6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illum…