LiteMedCoT-VL is introduced, a pipeline that transfers chain-of-thought reasoning from a 235B teacher model to 2B student models through LoRA-based fine-tuning on explanation-enriched training data, and indicates that a 2B model with reasoning distillation can match or exceed models with twice the parameters.
This work proposes REALM, a cross-modal framework that learns an RGB- and Event-Aligned Latent Manifold by projecting event representations into the pretrained latent space of RGB foundation models, and demonstrates state-of-the-art performance in wide-baseline feature matching, significantly outperforming specialized...
Vincenzo Polizzi, David B. Lindell, Jonathan Kelly· arXiv.org· 0 citations
Temporal Retinal U-Net is developed, a deterministic single-pass predictor conditioned on irregular imaging history and prediction horizon that achieved the strongest overall performance among the evaluated classical and deep-learning comparators on image-level and eye-specific progression measures.
Li-Yin Chen, Souvick Mukherjee, Ines Maria De Carvalho Lains et al.· 0 citations
While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts or focus exclusively on human speech. This leaves a significant gap in real-time, sound-centric manipulation where fleeting environmental acoustics provide critical state verifica...
Chang Nie, Tianchen Deng, Guangming Wang et al.· 0 citations
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The MAMA-MIA Challenge provides standardized datasets, evaluation protocols, and public resources to promote the development of robust and equitable artificial intelligence systems for breast cancer imaging.
Lidia Garrucho, Smriti Joshi, Kaisar Kushibar et al.· IEEE Transactions on Medical...· 2 citations
This study presents a comparative case study of representative motion planning methods drawn from major benchmark ecosystems, including CARLA, nuPlan, and the Waymo Open Dataset to highlight the strengths and weaknesses of current approaches.
Merve Atasever, Alfredo Reina Corona, Zhuo-Chen Liu et al.· 1 citation
CRYSTAL is introduced, a diagnostic benchmark with 6,372 instances that evaluates multimodal reasoning through verifiable intermediate steps, and the Causal Process Reward (CPR), a multiplicative reward that couples answer correctness with step-level alignment, and CPR-Curriculum, which progressively increases reasonin...
Vision-Language Models (VLMs) have recently been proposed as promising tools for face recognition, as they can produce natural language explanations alongside similarity scores. This capability is considered appealing for face comparisons in forensic contexts, which require decisions to be transparent and auditable. Ho...
Factorized ablations further validate the complementary roles of CBA and DPA, supporting balanced prompt adaptation as an effective alternative to entropy minimization for test-time binary segmentation.
Zheng-Shan Wang, J. C. Webster-Ford, Yi-Fei Tian et al.· 0 citations
VidOmni-Bench is introduced, a benchmark that requires models to verify whether each event in dense video captions is supported by the video, and reveals three key findings: Video-LLMs frequently generate hallucinated descriptions in dense video captioning, and they also struggle as verifiers.
Changbeen Kim, Jun-Qing Chang, Kipyo Kim et al.· 0 citations
This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In this challenge, we address grasp motion generation for the 12-DoF LinkerHand O6, aiming to produce physically plausible reach-and-lift trajectories for unseen objects from r...
Muneeb A. Khan, Woojin Kim, Shinwoo Kim et al.· 0 citations
This work proposes LoG-VGGT, a memory-efficient framework for long-sequence 3D reconstruction that balances local temporal modeling with global camera consistency, and introduces cross-window attention at a small subset of transformer blocks, enabling effective information propagation across adjacent temporal windows w...
Jing-Ke Zhou, Chen-Hang Ma, Zhi-Zhou Zhong et al.· 1 citation
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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.
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