8 citations · 24 across the 12 of their papers we have counts for
13 papers
Structured Context Learning for Generic Event Boundary Detection
Xin Gu, Congcong Li, Xinyao Wang +5
Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, ca…
High-Fidelity Image Inpainting with Multimodal Guided GAN Inversion
Libo Zhang, Yongsheng Yu, Jiali Yao +1
Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked con…
Context-Guided Spatio-Temporal Video Grounding
Xin Gu, Heng Fan, Yan Huang +2
Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suff…
Flow-Guided Diffusion for Video Inpainting
Bohai Gu, Yongsheng Yu, Heng Fan +1
Video inpainting has been challenged by complex scenarios like large movements and low-light conditions. Current methods, including emerging diffusion models, face limitations in q…
Local Compressed Video Stream Learning for Generic Event Boundary Detection
Libo Zhang, Xin Gu, Congcong Li +2
Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be…
Collaborative Three-Stream Transformers for Video Captioning
Hao Wang, Libo Zhang, Heng Fan +1
As the most critical components in a sentence, subject, predicate and object require special attention in the video captioning task. To implement this idea, we design a novel frame…
Accurate and Fast Compressed Video Captioning
Yaojie Shen, Xin Gu, Kai Xu +3
Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or capt…
Unsupervised Domain Adaptive Detection with Network Stability Analysis
Wenzhang Zhou, Heng Fan, Tiejian Luo +1
Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration fr…
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
Yunhao Li, Zhen Xiao, Lin Yang +4
Multi-object tracking (MOT) is a fundamental problem in computer vision with numerous applications, such as intelligent surveillance and automated driving. Despite the significant…
Deficiency-Aware Masked Transformer for Video Inpainting
Yongsheng Yu, Heng Fan, Libo Zhang
Recent video inpainting methods have made remarkable progress by utilizing explicit guidance, such as optical flow, to propagate cross-frame pixels. However, there are cases where…
MaGIC: Multi-modality Guided Image Completion
Yongsheng Yu, Hao Wang, Tiejian Luo +2
Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitig…
PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection
Libo Zhang, Lutao Jiang, Ruyi Ji +1
Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance…
High-Fidelity Image Inpainting with GAN Inversion
Yongsheng Yu, Libo Zhang, Heng Fan +1
Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as…