13 papers
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen +10
Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is challenging: visually realistic ou…
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen +17
Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-attention, often without retraining.…
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…
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…
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20
Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-shot imitation learning remains…