13 citations · 30 across the 18 of their papers we have counts for
18 papers
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
Bingxi Zhao, Lin Geng Foo, Ping Hu +3
Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways. This survey introduces a unifie…
Avatar Concept Slider: Controllable Editing of Concepts in 3D Human Avatars
Lin Geng Foo, Yixuan He, Ajmal Saeed Mian +3
Text-based editing of 3D human avatars to precisely match user requirements is challenging due to the inherent ambiguity and limited expressiveness of natural language. To overcome…
Learning to Unlearn for Robust Machine Unlearning
Mark He Huang, Lin Geng Foo, Jun Liu
Machine unlearning (MU) seeks to remove knowledge of specific data samples from trained models without the necessity for complete retraining, a task made challenging by the dual ob…
LAGA: Layered 3D Avatar Generation and Customization via Gaussian Splatting
Jia Gong, Shenyu Ji, Lin Geng Foo +3
Creating and customizing a 3D clothed avatar from textual descriptions is a critical and challenging task. Traditional methods often treat the human body and clothing as inseparabl…
Action Detection via an Image Diffusion Process
Lin Geng Foo, Tianjiao Li, Hossein Rahmani +1
Action detection aims to localize the starting and ending points of action instances in untrimmed videos, and predict the classes of those instances. In this paper, we make the obs…
LLMs are Good Sign Language Translators
Jia Gong, Lin Geng Foo, Yixuan He +2
Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language. Inspired by the strong translation capabilities of large language mod…
AI-Generated Content (AIGC) for Various Data Modalities: A Survey
Lin Geng Foo, Hossein Rahmani, Jun Liu
AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the potential of…
Distribution-Aligned Diffusion for Human Mesh Recovery
Lin Geng Foo, Jia Gong, Hossein Rahmani +1
Recovering a 3D human mesh from a single RGB image is a challenging task due to depth ambiguity and self-occlusion, resulting in a high degree of uncertainty. Meanwhile, diffusion…
Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
Tianjiao Li, Lin Geng Foo, Ping Hu +4
Learning with large-scale unlabeled data has become a powerful tool for pre-training Visual Transformers (VTs). However, prior works tend to overlook that, in real-world scenarios,…
System-status-aware Adaptive Network for Online Streaming Video Understanding
Lin Geng Foo, Jia Gong, Zhipeng Fan +1
Recent years have witnessed great progress in deep neural networks for real-time applications. However, most existing works do not explicitly consider the general case where the de…
GradMDM: Adversarial Attack on Dynamic Networks
Jianhong Pan, Lin Geng Foo, Qichen Zheng +4
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robus…
Progressive Channel-Shrinking Network
Jianhong Pan, Siyuan Yang, Lin Geng Foo +4
Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience…
DiffPose: Toward More Reliable 3D Pose Estimation
Jia Gong, Lin Geng Foo, Zhipeng Fan +3
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diff…
Improving the Reliability for Confidence Estimation
Haoxuan Qu, Yanchao Li, Lin Geng Foo +3
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to…
Heatmap Distribution Matching for Human Pose Estimation
Haoxuan Qu, Li Xu, Yujun Cai +2
For tackling the task of 2D human pose estimation, the great majority of the recent methods regard this task as a heatmap estimation problem, and optimize the heatmap prediction us…
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
Tianjiao Li, Lin Geng Foo, Qiuhong Ke +4
The goal of fine-grained action recognition is to successfully discriminate between action categories with subtle differences. To tackle this, we derive inspiration from the human…
ERA: Expert Retrieval and Assembly for Early Action Prediction
Lin Geng Foo, Tianjiao Li, Hossein Rahmani +2
Early action prediction aims to successfully predict the class label of an action before it is completely performed. This is a challenging task because the beginning stages of diff…
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation
Lin Geng Foo, Rui En Ho, Jiamei Sun +1
We consider the problem of segmenting cell nuclei instances from Hematoxylin and Eosin (H&E) stains with weak supervision. While most recent works focus on improving the segmentati…