This work proposes Event-Causal RAG, a lightweight retrieval-augmented framework for ultra-long and streaming video reasoning and introduces ECV-1H, an hour-scale long-video QA benchmark dedicated to directed event-causal reasoning, with all source videos exceeding one hour.
P. Yan, Yu Zhao, Liang Xie et al.· arXiv.org· 0 citations
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``reinvent the wheel'', rediscovering solutions in isolated contexts wi...
Yuan Liang, Ruobin Zhong, Haoming Xu et al.· 0 citations
DistScan is presented, a backdoor detection framework based on a simple but previously unexploited observation: backdoor injection systematically shifts a model's pre-NMS prediction class distribution away from its training class frequencies, even on clean inputs without any trigger present.
Long-Tian Wang, Zheng-Yu Zhao, Chen-Hao Lin et al.· 0 citations
ReWEIGH is a training-free decoding intervention that aggregates vocabulary ranks across visual positions and compares each candidate with a token-specific reference estimated from unlabeled images and applies a bounded penalty only to candidates that fall below their reference.
Jihae Jeong, Jun-Ha Choi, Hwanjo Yu· 0 citations
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This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in Vision-Language-Action (VLA) policies without policy retraining. To our knowledge, this is the first approach to directly leverage 3D Gaussian-based novel-view synthesis for observation-space adaptation in VLA pol...
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are pl...
N. Meli, Tolga Birdal, Prayag Tiwari et al.· 0 citations
Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier's prediction. Existing VCE methods are inherently classifier-dependent and therefore susceptible to classifier biases and failure modes, such as s...
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a ne...
Jonel Roman, Ryan Sirjue, Peter Nguyen et al.· 0 citations
This work proposes GrabVG, a novel visual grounding framework inspired by human visual search that generates a compact set of reliable object hypotheses through distillation-guided proposal induction and text-aware hypothesis filtering, substantially reducing background distractions and semantic mismatches.
Chaowei Wang, Yan Di, Jingjun Sun et al.· 0 citations
Learning-state-aware dynamic generative data augmentation (LSADA) is proposed, which introduces a decoupled data augmentation and diffusion fusion strategy that applies strength-controlled transformations to class-relevant regions and generates diverse class-irrelevant regions, progressively fusing them to improve imag...
Ting Xiang, Chen-Xi Deng, Jinhui Zhao et al.· 0 citations
It is suggested that ten annotated cases are sufficient for clinically useful segmentation, effectively reducing bottlenecks for both image annotation and training time.
S. Nagaraju, B. Abrahamsen, Ashkan Moradi et al.· 0 citations
Improvements show that CutMix has only a minor impact on segmentation accuracy but consistently improves the reliability, particularly under distribution shifts, indicating that CutMix primarily enhances the trustworthiness of the model’s calibration and uncertainty rather than the raw segmentation prediction itself.
S. Landgraf, M. Ulrich· ISPRS Annals of the Photogra...· 0 citations
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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