11 papers
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen +5
Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework…
TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation
Duc Nguyen, Sieu Tran, Hao Vo +6
Unsupervised video object-centric learning aims to decompose dynamic scenes into temporally persistent entity representations. Existing recurrent video slot-attention methods propa…
MVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints
Thinh Phan, Hao Vo, Khoa Vo +3
The core challenge in multi-view pedestrian detection (MVPD) lies in effective aggregation of visual features from different viewpoints for robust occlusion reasoning. Recent appro…
OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs
Hao Vo, Phu Loc Nguyen, Khoa Vo +7
Multimodal Large Language Models (MLLMs) have achieved remarkable performance on 2D visual tasks, yet enhancing their spatial intelligence for real-world applications such as Auton…
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose pro…
ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking
Hao Vo, Duc Nguyen, Ngan Le
Multi-Camera Multi-Target (MCMT) tracking has emerged as a critical capability for applications ranging from autonomous driving to animal behavior monitoring. While recent advances…
Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing
Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu +15
Diffusion-based Vision-Language-Action (VLA) policies enable strong generalization in robotic manipulation, but remain sensitive to spurious visual correlations and noisy action ge…
Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning
Sieu Tran, Duc Nguyen, Hao Vo +2
Unsupervised video object-centric learning aims to decompose dynamic scenes into persistent, object-level representations without supervision. However, existing slot-based methods…
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
Duy Le Dinh Anh, Kim Hoang Tran, Quang-Thuc Nguyen +1
Despite recent progress, Multi-Object Tracking (MOT) continues to face significant challenges, particularly its dependence on prior knowledge and predefined categories, complicatin…
TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
Duy Le Dinh Anh, Kim Hoang Tran, Ngan Hoang Le
While Multi-Object Tracking (MOT) has made substantial advancements, it is limited by heavy reliance on prior knowledge and limited to predefined categories. In contrast, Generic M…
Unifying Global and Local Scene Entities Modelling for Precise Action Spotting
Kim Hoang Tran, Phuc Vuong Do, Ngoc Quoc Ly +1
Sports videos pose complex challenges, including cluttered backgrounds, camera angle changes, small action-representing objects, and imbalanced action class distribution. Existing…