11 papers
T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding
Chaohong Guo, Yihan He, Yongwei Nie +3
Video Temporal Grounding (VTG) aims to localize the video segment that corresponds to a natural language query, which requires a comprehensive understanding of complex temporal dyn…
Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection
Yuyang Yu, Zhengwei Chen, Xuemiao Xu +4
3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based…
Enhancing Long Video Question Answering with Scene-Localized Frame Grouping
Xuyi Yang, Wenhao Zhang, Hongbo Jin +5
Current Multimodal Large Language Models (MLLMs) often perform poorly in long video understanding, primarily due to resource limitations that prevent them from processing all video…
TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
Chaohong Guo, Xun Mo, Yongwei Nie +3
Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-Language Models (LVLMs) employ Reinforc…
Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability
Zhaoyu Chen, Hongnan Lin, Yongwei Nie +4
Temporal Video Grounding (TVG) aims to localize video segments corresponding to a given textual query, which often describes human actions. However, we observe that current methods…
RecDreamer: Consistent Text-to-3D Generation via Uniform Score Distillation
Chenxi Zheng, Yihong Lin, Bangzhen Liu +3
Current text-to-3D generation methods based on score distillation often suffer from geometric inconsistencies, leading to repeated patterns across different poses of 3D assets. Thi…
FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images
Jinchen Yu, Yongwei Nie, Fei Qi +2
The Segment Anything Model (SAM) has gained popularity as a versatile image segmentation method, thanks to its strong generalization capabilities across various domains. However, w…
Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive Losses
Yongwei Nie, Changzhen Liu, Chengjiang Long +3
Besides a 3D mesh, Human Mesh Recovery (HMR) methods usually need to estimate a camera for computing 2D reprojection loss. Previous approaches may encounter the following problem:…
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery
Yongwei Nie, Mingxian Fan, Chengjiang Long +3
Human Mesh Recovery (HMR) is the task of estimating a parameterized 3D human mesh from an image. There is a kind of methods first training a regression model for this problem, then…
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
Yongwei Nie, Hao Huang, Chengjiang Long +3
Video Anomaly Detection (VAD) has been extensively studied under the settings of One-Class Classification (OCC) and Weakly-Supervised learning (WS), which however both require labo…
Disentangled Representation Learning for Controllable Person Image Generation
Wenju Xu, Chengjiang Long, Yongwei Nie +1
In this paper, we propose a novel framework named DRL-CPG to learn disentangled latent representation for controllable person image generation, which can produce realistic person i…