53 citations · 321 across the 50 of their papers we have counts for
52 papers
Bridging Day and Night: Target-Class Hallucination Suppression in Unpaired Image Translation
Shuwei Li, Lei Tan, Robby T. Tan
Day-to-night unpaired image translation is important to downstream tasks but remains challenging due to large appearance shifts and the lack of direct pixel-level supervision. Exis…
Aggregating Diverse Cue Experts for AI-Generated Image Detection
Lei Tan, Shuwei Li, Mohan Kankanhalli +1
The rapid emergence of image synthesis models poses challenges to the generalization of AI-generated image detectors. However, existing methods often rely on model-specific feature…
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks
Hongchao Jiang, Yiming Chen, Yushi Cao +2
Large Language Models (LLMs) have significantly advanced the state-of-the-art in various coding tasks. Beyond directly answering user queries, LLMs can also serve as judges, assess…
Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets
Mikhail Kennerley, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb +1
Combining multiple object detection datasets offers a path to improved generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some metho…
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Xin Jin +134
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are devel…
Representation Learning of Structured Data for Medical Foundation Models
Vijay Prakash Dwivedi, Viktor Schlegel, Andy T. Liu +6
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-t…
VoiceBench: Benchmarking LLM-Based Voice Assistants
Yiming Chen, Xianghu Yue, Chen Zhang +3
Building on the success of large language models (LLMs), recent advancements such as GPT-4o have enabled real-time speech interactions through LLM-based voice assistants, offering…
Beyond Single-Audio: Advancing Multi-Audio Processing in Audio Large Language Models
Yiming Chen, Xianghu Yue, Xiaoxue Gao +4
Various audio-LLMs (ALLMs) have been explored recently for tackling different audio tasks simultaneously using a single, unified model. While existing evaluations of ALLMs primaril…
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization
Aishik Nagar, Yutong Liu, Andy T. Liu +6
Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce c…
Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models
Yiming Chen, Chen Zhang, Danqing Luo +3
The automatic evaluation of natural language generation (NLG) systems presents a long-lasting challenge. Recent studies have highlighted various neural metrics that align well with…
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using…
ORTexME: Occlusion-Robust Human Shape and Pose via Temporal Average Texture and Mesh Encoding
Yu Cheng, Bo Wang, Robby T. Tan
In 3D human shape and pose estimation from a monocular video, models trained with limited labeled data cannot generalize well to videos with occlusion, which is common in the wild…
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
Chenxin Xu, Robby T. Tan, Yuhong Tan +3
Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structure…
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
Yeying Jin, Beibei Lin, Wending Yan +3
Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources.…
Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen, Simin Chen, Zexin Li +4
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream appr…
DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment
Heyuan Li, Bo Wang, Yu Cheng +2
Sensitivity to severe occlusion and large view angles limits the usage scenarios of the existing monocular 3D dense face alignment methods. The state-of-the-art 3DMM-based method,…
Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
Jiadong Wang, Xinyuan Qian, Malu Zhang +2
Talking face generation, also known as speech-to-lip generation, reconstructs facial motions concerning lips given coherent speech input. The previous studies revealed the importan…
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Chenxin Xu, Robby T. Tan, Yuhong Tan +4
Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geomet…
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remain…
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
Xin Yang, Michael Bi Mi, Yuan Yuan +2
Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, renderin…
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
Yeying Jin, Ruoteng Li, Wenhan Yang +1
Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often re…
MIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning
Shuwei Li, Jikai Wang, Michael S. Brown +1
The assumption of a uniform light color distribution is no longer applicable in scenes that have multiple light colors. Most color constancy methods are designed to deal with a sin…
DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
Yeying Jin, Wei Ye, Wenhan Yang +2
Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing metho…
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Yeying Jin, Wending Yan, Wenhan Yang +1
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or…