3 citations · 7 across the 19 of their papers we have counts for
20 papers · 1 filter
Does the VGGT Family Need All Its Layers?
Fengyi Zhang, Holger Caesar, Xiangyu Sun +3
Which layers of a feed-forward geometry model are needed to preserve both camera poses and dense 3D structure? We study layer redundancy in VGGT, , and VGGT-: 3,018 pruned…
Scene-SAM3D: Multi-View Scene Asset Generation Without Fine-Tuning
Yuqi Zhang, Yadan Luo, Xiangyu Sun +3
High-quality 3D scene assets are critical for embodied applications such as robotic manipulation, navigation, and simulation. Despite their strong object priors, recent single-imag…
From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting
Woojung Son, Won Suk Lee, Zijing Huang +4
Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing strawber…
Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
Shijie Wang, Zijian Wang, Yadan Luo +3
Ultra-fine-grained visual categorization (Ultra-FGVC) aims to classify highly similar subcategories within fine-grained objects using limited training samples. However, holistic ye…
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Shijie Wang, Yadan Luo, Zijian Wang +3
This paper investigates the intrinsic geometrical features of highly similar objects and introduces a general self-supervised framework called the Geometric Attribute Exploration N…
VGGT-World: Transforming VGGT into an Autoregressive Geometry World Model
Xiangyu Sun, Shijie Wang, Fengyi Zhang +5
World models that forecast scene evolution by generating future video frames devote the bulk of their capacity to photometric details, yet the resulting predictions often remain ge…
Language-driven Fine-grained Retrieval
Shijie Wang, Xin Yu, Yadan Luo +3
Existing fine-grained image retrieval (FGIR) methods learn discriminative embeddings by adopting semantically sparse one-hot labels derived from category names as supervision. Whil…
TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction
Fengyi Zhang, Tianjun Zhang, Kasra Khosoussi +3
3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass. However, when deployed…
Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving
Djamahl Etchegaray, Yuxia Fu, Zi Huang +1
Interpretable communication is essential for safe and trustworthy autonomous driving, yet current vision-language models (VLMs) often operate under idealized assumptions and strugg…
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
Huitong Yang, Zhuoxiao Chen, Fengyi Zhang +2
Maintaining robust 3D perception under dynamic and unpredictable test-time conditions remains a critical challenge for autonomous driving systems. Existing test-time adaptation (TT…
Test-Time 3D Occupancy Prediction
Fengyi Zhang, Xiangyu Sun, Huitong Yang +3
Self-supervised 3D occupancy prediction offers a promising solution for understanding complex driving scenes without requiring costly 3D annotations. However, training dense occupa…
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…
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…
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…
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
Yiyun Zhang, Zijian Wang, Yadan Luo +2
Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their application…
Open-CRB: Towards Open World Active Learning for 3D Object Detection
Zhuoxiao Chen, Yadan Luo, Zixin Wang +3
LiDAR-based 3D object detection has recently seen significant advancements through active learning (AL), attaining satisfactory performance by training on a small fraction of strat…
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis
Xiaoxiao Sun, Xingjian Leng, Zijian Wang +3
Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a t…