17 citations · 53 across the 29 of their papers we have counts for
9 papers · 1 filter
Color-Oriented Redundancy Reduction in Dataset Distillation
Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh +2
Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains consi…
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
Jia Syuen Lim, Yadan Luo, Zhi Chen +3
In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP l…
Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation
Zixin Wang, Dong Gong, Sen Wang +2
Contrastive Language-Image Pretraining (CLIP) excels at learning generalizable image representations but often falls short in zero-shot inference on certain downstream datasets. Te…
CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
Zhi Chen, Tianqi Wei, Zecheng Zhao +6
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge…
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning
Zhi Chen, Zecheng Zhao, Yadan Luo +1
Conventional Text-guided single-image editing approaches require a two-step process, including fine-tuning the target text embedding for over 1K iterations and the generative model…
DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection
Zhuoxiao Chen, Zixin Wang, Yadan Luo +2
LiDAR-based 3D object detection has seen impressive advances in recent times. However, deploying trained 3D detectors in the real world often yields unsatisfactory performance when…
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
Jia Syuen Lim, Zhuoxiao Chen, Mahsa Baktashmotlagh +4
Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discover…
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection
Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh +3
LiDAR-based 3D object detection is crucial for various applications but often experiences performance degradation in real-world deployments due to domain shifts. While most studies…
Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments
Djamahl Etchegaray, Zi Huang, Tatsuya Harada +1
In this work, we tackle the limitations of current LiDAR-based 3D object detection systems, which are hindered by a restricted class vocabulary and the high costs associated with a…