19 papers
DynaPix: Can Vision-Language Models Identify the Exact Future?
Thong Nguyen, Vinh-Hien Do, Quynh Vo +2
Acting in a physical scene requires knowing its real later state, not a plausible one. Current evaluations often accept words or a realistic-looking image, so the predicted state i…
Predict, Then Retrieve: Cross-Instance Future-State Retrieval from Video Prefixes
Quynh Vo, Thong Nguyen, Vinh-Hien Do +2
We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instance…
When Depth Is Better Told Than Shown: Depth-Ordinal Prompting for Vision-Language Spatial Reasoning
Quynh Vo, Phuc Dao, Cong-Duy Nguyen +1
The paper introduces Depth-Ordinal Prompting (DOP), a training‑free technique that converts monocular depth estimates into object‑level ordinal text cues, enabling vision‑language…
TIGER: Text-Conditioned Visual Gated Routing with Acceptance Alignment for Multimodal Speculative Decoding
Quynh Vo, Cong-Duy Nguyen, Ponhvoan Srey +2
The paper introduces TIGER, a framework that speeds up multimodal generation by dynamically selecting only the visual tokens relevant to the current textual context and training th…
Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Thong Nguyen, Khoi M. Le, Cong-Duy Nguyen +3
Recent advancements in image animation have utilized diffusion models to breathe life into static images. However, existing controllable frameworks typically rely on Lagrangian mot…
WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization
Phong Nam Huu Nguyen, Khoi M. Le, Cong-Duy T Nguyen +3
Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime rem…