Across camera-space and world-space benchmarks, FactorizedHMR remains competitive with strong baselines, with the clearest gains in occlusion-heavy recovery and drift-sensitive world-space metrics.
Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively through scheduling and weighting, whereas the impact of the noise realization at a given timestep is still underexplored. In this work, we examin...
Haokai Zhao, Da Xing, Hanqun Cao et al.· 0 citations
This visually grounded, multi-probe evaluation first verifies that a model recognizes each concept from the image, then tests its recoverability through multiple-choice, short-answer, and indirect queries.
Zhang-Yun Tan, Ze-Liang Zhang, Jia-Ni Liu et al.· 1 citation
This work introduces COMiT, a communication-inspired framework for learning structured discrete visual representations that substantially improves compositional generalization and relational reasoning over prior methods.
Aram Davtyan, Y. Şahin, Yasaman Haghighi et al.· 0 citations
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Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety concerns about unauthorized style mimicry. Existing model-side protection methods generally follow ordinary concept erasure, emphasizing aggressive deletion or redirection of target...
Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and generalized to new tasks. While foundational for image and video generation, the application of tokens to physical simulation remains nascent....
Levi Lingsch, Georgios Kissas, Johannes Jakubik et al.· 0 citations
Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds or design dedicated modules for instance association. However, these approaches perform redundant point processing and consequently become computationally expensive, yet still ov...
Gyeongrok Oh, Youngdong Jang, Jonghyun Choi et al.· 0 citations
Conformal Prediction Active TTA is proposed, which first brings principled, conformal uncertainty with coverage-aware online calibration into ATTA, and consistently outperforms the state-of-the-art ATTA methods by around 5% in accuracy.
Ting-Yu Shi, Fan Lyu, Hai-Hua Zhu et al.· 1 citation
Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical to improving survival rates, but misdiagnosis or delayed diagnosis can lead to poor outcomes. Despite advancements in AI-based image analysis, diagnosing choroidal nevi in colour f...
Mohammadmahdi Eshragh, Emad A. Mohammed, Behrouz Far et al.· 0 citations
A novel STN framework that decomposes affine transformations into interpretable primitives, regressed under adaptable geometric constraints, thereby preventing the training instability typically caused by degenerate transformations.
Johann Schmidt, Tom Siegl, Martin Becker et al.· arXiv.org· 3 citations
It is demonstrated that FOCUS offers a general solution for enhancing multi-image reasoning without additional training or architectural modifications, and that FOCUS generalizes to video understanding, extending its applicability beyond static multi-image inputs.
Yeji Park, Minyoung Lee, Sanghyuk Chun et al.· 0 citations
A modular manipulation framework that separates high-level planning from low-level control and coupling grounded plan generation with a 3D-based execution policy, this framework achieves state-of-the-art performance on the challenging GemBench benchmark and demonstrates promising transfer to real robots.
Shi-Zhe Chen, Ricardo Garcia, Paul Pacaud et al.· 2 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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