Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existing driving video generation methods may produce visually realistic videos, they often fail to deliver physically realistic simulations because they lack the capability to generate...
Xiangwen Zhang, Qian Zhang, Longfei Han et al.· 0 citations
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments uses encoders pretrained solely on ImageNet-1k with either supervised...
Scott C. Lowe, Joakim Bruslund Haurum, Sageev Oore et al.· 0 citations
A semi-supervised segmentation model that uses labeled SPs and unlabeled slices from 3D US volumes (non-SPs) that incorporates a classification mechanism to identify and filter out frames lacking the fetal brain, and to generate masks for those containing it, enhancing the relevance of plane pose regression in clinical...
Chiara Di Vece, Antonio Cirigliano, M. Le Lous et al.· arXiv.org· 3 citations
Ref-GeNVS inherits the strong generalizability of the multi-view diffusion backbone, while it does not require finetuning, and outperforms recent generative NVS methods by generating reflection-consistent and contextually coherent novel views.
GeonU Kim, Dong-Yeon Shin, Tae-Hyun Oh· 0 citations
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Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced. We study this behavior as a problem of conditional visual grounding: the visual target required for successful control changes with the manipulation ph...
Vivek Chavan, Peng-Tao Xie, Ya-Huan Shi et al.· 0 citations
This work proposes a unified Vision Transformer compression framework that combines Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation, and systematically identifies the most effective configuration within each com...
M. Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith et al.· 0 citations
A large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in VLA models, and establishes RoboSPA as a challenging diagnostic benchmark for developing more capable, reliable, and generalizable embodied agents.
Zhen-Xuan Fan, Bo Zhang, Yu-Tong Lin et al.· 0 citations
An adaptive temporal modeling framework for WSVAD that explicitly accounts for variations in video dynamics across multiple temporal granularities is proposed and an adaptive similarity-based fusion strategy that dynamically integrates anomaly scores into video-level predictions is proposed, replacing fixed top-k aggre...
VICAL is introduced, a consistency-driven framework that improves long-tailed recognition not by enforcing expert diversity, but by reducing prediction variance, and suggests that multi-expert models benefit more from variance reduction than diversity maximization.
Jianggang Zhu, Zheng Wang, Bin Zhu et al.· 0 citations
Modality-Contrastive Preference Optimization (MCPO), a highly sample-efficient two-stage length-compression method that requires fewer than 900 training samples, and adopts a highly nonlinear odds-ratio formulation that provides steep gradients in the with-image context to reinforce length constraints for preferred tra...
Guang-Heng Yang, Zhen-Liang Ni, Zhen-Kai Wu et al.· 0 citations
It is demonstrated that a single learned traffic prior can simultaneously improve motion planning and provide a realistic framework for systematic planner robustness evaluation.
Arka Pal, Rajesh Kumar, Hannes Eriksson et al.· 0 citations
This work introduces UnorthoDOS, a dataset and approach for training machine learning models directly on unorthorectified hyperspectral imagery, bypassing both orthorectification and matched-filter products, and demonstrates the feasibility of onboard deployment.
L. Marini, Maggie Chen, Hala Lamdouar 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.
