MPFlow is proposed, a zero-shot multi-modal reconstruction framework built on rectified flow that incorporates auxiliary MRI modalities at inference time without retraining the generative prior to improve anatomical fidelity.
Seung-Sik Kim, Chen Jin, H. Tregidgo et al.· arXiv.org· 0 citations
This study systematically dissects design choices along three dimensions: foundational components, perception essentials, and action modeling perspectives and distill 12 key findings that together form a practical recipe for building strong VLA models, resulting in a simple yet effective model, VLANeXt.
Xiao-Ming Wu, Kang Liao, Yi-Hang Luo et al.· 12 citations· ⚡3
A lightweight Cross-Layer Fusion Adapter (CLFA) framework that repurposes Contrastive Language--Image Pre-training for handwriting-based AD screening and progressively aligns pretrained vision--language representations with AD-related handwriting cues and supports transfer from supervised source tasks to task-disjoint...
Chang-Qing Gong, Hua-Feng Qin, Mounîm A. El-Yacoubi· 0 citations
The Band-Attention Modulation Network (BAM-Net) is proposed, a novel framework that pioneers learnable, fine-grained modulation of frequency components for forgery detection and exhibits exceptional generalization in cross-dataset, cross-compression, and cross-manipulation scenarios.
Zhi-Da Zhang, Wen-Kui Yang, Xin Ma et al.· 1 citation
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This work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration, using an object-centric relational program representation.
Yu-Yao Liu, Jia-Yuan Mao, David Hsu et al.· 0 citations
Rolling-WAM is presented, a formulation that distributes joint denoising across successive replanning cycles and delivers a 4.5x steady-state replanning speedup over standard joint WAMs.
Ying Zhou, Jun-Jie Ye, Yi-Qi Zhao et al.· 0 citations
PoEM, a framework to predict the outputs of RL on a new reward function using a set of models already post-trained on other rewards, is introduced by introducing PoEM, a framework to predict the outputs of RL on a new reward function using a set of models already post-trained on other rewards.
K. Hamidieh, G. Daras, Antonio Torralba· 0 citations
TrackEverything is the first 3D tracker capable of tracking all visible points across videos exceeding 1000 frames within 40 GB of GPU memory, and 3D WAFT is proposed, replacing memory-prohibitive 4D correlation volumes with efficient feature sampling in the scene cloud.
Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta et al.· 0 citations
TRACK: TRajectory-Aware Capacity routing via top-K selection is presented, a heterogeneous denoising strategy that switches between compatible large and small models at selected steps, reducing the average cost per denoising evaluation.
Mustafa Munir, H. Vũ, Shreyas Misra et al.· 0 citations
Post-training quantization of vision--language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving a headline score does not guarantee preservation of visual grounding behavior. We present GHOST-Q, a cross-precision controlled evaluation of three 8B VLM families under...
This work introduces AdaTex4D, an adaptive texture-capacity module for deformation-based 4D Gaussian Splatting, and shows that dynamic, anisotropic texture allocation provides a more efficient way to distribute local appearance capacity in 4D Gaussian representations.
Deng-Du Jiang, Pei-Qiang Wang, Ke-Hong Yuan et al.· 0 citations
SPMC, a training framework that identifies grounding-responsive heads and selectively regularizes their text-to-image attention, demonstrates that targeted guidance of sparse and implicit visual evidence pathways can directly improve multimodal reasoning.
Jia-Qi Deng, Zong-Han Wu, Zhan Heng et al.· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.