activity
20162026
most citedNatural Language Video Localization: A Revisit in Span-based Question Answering Framework

93 citations · 245 across the 29 of their papers we have counts for

collaborators

50 papers

cs.LG2026

Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation

Jia-Qi Lin, Yuangang Pan, Chang-Dong Wang +3

Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffective when the label distributi…

cs.CV2023

Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering

Chi Zhang, Wei Yin, Gang Yu +5

In this study, we address the challenge of 3D scene structure recovery from monocular depth estimation. While traditional depth estimation methods leverage labeled datasets to dire…

cs.CL2023

Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection

Kairui Hu, Ming Yan, Joey Tianyi Zhou +3

Stance detection aims to identify the attitude expressed in a document towards a given target. Techniques such as Chain-of-Thought (CoT) prompting have advanced this task, enhancin…

cs.CV2023

Noisy-Correspondence Learning for Text-to-Image Person Re-identification

Yang Qin, Yingke Chen, Dezhong Peng +3

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although nume…

cs.CV2023

Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification

Xinke Li, Junchi Lu, Henghui Ding +3

With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safe…

cs.LG202320 cited

Provable Dynamic Fusion for Low-Quality Multimodal Data

Qingyang Zhang, Haitao Wu, Changqing Zhang +4

The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction. To fully release the value of each mod…