4 citations · 7 across the 14 of their papers we have counts for
17 papers
DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence
Xu Huang, Ye Huang, Zijun Liao +6
High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity…
Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion
Jiayi Yuan, Na Zhao, De Wen Soh
Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence of explicit visual cues. In…
Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes
Shanhong Liu, Pai Chet Ng, De Wen Soh +2
Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful inten…
I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition
Shanhong Liu, Rui Cao, Pai Chet Ng +1
Multimodal memes are dynamic and often require up to date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowle…
Leveraging Text-to-Image Diffusion Models for Unsupervised Visual Object Tracking
Zhengbo Zhang, Zhigang Tu, Junsong Yuan +2
Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotations. Despite considerable pro…
VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation
Jiayi Yuan, Haobo Jiang, De Wen Soh +1
This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-independent training-free approaches,…
Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection
Olivia Shanhong Liu, Pai Chet Ng, De Wen Soh +1
Humorous memes blend visual and textual cues to convey irony, satire, or social commentary, posing unique challenges for AI systems that must interpret intent rather than surface c…
OnlineSplatter: Pose-Free Online 3D Reconstruction for Free-Moving Objects
Mark He Huang, Lin Geng Foo, Christian Theobalt +2
Free-moving object reconstruction from monocular video remains challenging, particularly without reliable pose or depth cues and under arbitrary object motion. We introduce OnlineS…
TSTMotion: Training-free Scene-aware Text-to-motion Generation
Ziyan Guo, Haoxuan Qu, Hossein Rahmani +4
Text-to-motion generation has recently garnered significant research interest, primarily focusing on generating human motion sequences in blank backgrounds. However, human motions…
Visual Prompting for One-shot Controllable Video Editing without Inversion
Zhengbo Zhang, Yuxi Zhou, Duo Peng +4
One-shot controllable video editing (OCVE) is an important yet challenging task, aiming to propagate user edits that are made -- using any image editing tool -- on the first frame…
POPEN: Preference-Based Optimization and Ensemble for LVLM-Based Reasoning Segmentation
Lanyun Zhu, Tianrun Chen, Qianxiong Xu +5
Existing LVLM-based reasoning segmentation methods often suffer from imprecise segmentation results and hallucinations in their text responses. This paper introduces POPEN, a novel…
MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm
Ziyan Guo, Zeyu Hu, De Wen Soh +1
Human motion generation and editing are key components of computer vision. However, current approaches in this field tend to offer isolated solutions tailored to specific tasks, wh…
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
Shuyi Jiang, Qihao Zhao, Hossein Rahmani +3
Recently, with the development of Neural Radiance Fields and Gaussian Splatting, 3D reconstruction techniques have achieved remarkably high fidelity. However, the latent representa…
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning
Huiming Wang, Zhaodonghui Li, Liying Cheng +2
Recently, large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to…
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Huiming Wang, Liying Cheng, Wenxuan Zhang +2
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recogniti…
Joint Network Topology Inference via Structured Fusion Regularization
Yanli Yuan, De Wen Soh, Xiao Yang +2
Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely…
Identifiability in Gaussian Graphical Models
De Wen Soh, Sekhar Tatikonda
In high-dimensional graph learning problems, some topological properties of the graph, such as bounded node degree or tree structure, are typically assumed to hold so that the samp…