22 citations · 47 across the 15 of their papers we have counts for
15 papers
Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding
Tianrui Hui, Zihan Ding, Junshi Huang +5
Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption. As a multimodal task, an essential aspect…
OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has no explicit head movement reference, thus it is difficult to generate talking heads with head motions. Some existing works only edit the mouth…
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Shaofei Huang, Han Li, Yuqing Wang +5
Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semant…
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model
Jin Liu, Xi Wang, Xiaomeng Fu +4
Face-to-face communication is a common scenario including roles of speakers and listeners. Most existing research methods focus on producing speaker videos, while the generation of…
A Unified Framework for Modality-Agnostic Deepfakes Detection
Cai Yu, Peng Chen, Jiahe Tian +7
As AI-generated content (AIGC) thrives, deepfakes have expanded from single-modality falsification to cross-modal fake content creation, where either audio or visual components can…
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has received growing attention in recent years, with various creative and practical applications. An ideal natural and vivid generated talking head…
OPT: One-shot Pose-Controllable Talking Head Generation
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to…
Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
Shaofei Huang, Zhenwei Shen, Zehao Huang +5
Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the b…
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation
Zihan Ding, Tianrui Hui, Junshi Huang +3
Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos. Previous methods either depend on 3D ConvNets…
Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation
Tianrui Hui, Shaofei Huang, Si Liu +5
Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. E…
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Jin Liu, Peng Chen, Tao Liang +5
Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial la…
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing
Shaofei Huang, Si Liu, Tianrui Hui +4
Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effect…
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning
Chunyuan Yuan, Qianwen Ma, Wei Zhou +2
The dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies ma…
Linguistic Structure Guided Context Modeling for Referring Image Segmentation
Tianrui Hui, Si Liu, Shaofei Huang +4
Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish…
Referring Image Segmentation via Cross-Modal Progressive Comprehension
Shaofei Huang, Tianrui Hui, Si Liu +5
Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approach…