Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small recognition errors can propagate into chemical databases and downstream models. In practice, recognized structures often require manual inspection and correction before...
Yujian Yuan, Xin Cai, Yufan Chen et al.· 0 citations
Post-training quantization (PTQ) is the standard way to run modern diffusion models on memory-constrained accelerators, yet every existing diffusion PTQ scheme fixes the $\mathit{number\ format}$ in advance and only tunes the scale, zero point, or per-layer bit-width. At a fixed bit-width the best format depends on the...
BiSSLight, which combines M-FAC-based implicit gradient approximation with parameter-efficient fine-tuning via LoRA, enabling efficient application at larger scales that were previously impractical, is introduced.
Gustav Wagner Zakarias, Zheng-Hua Tan· 0 citations
TTRSD separates update direction from update position, determined by group-relative advantages, from update position, determined by visual sensitivity, without requiring ground-truth labels, external verifiers, or a separate teacher, demonstrating cross-dataset generalization while preserving inherent reasoning integri...
Shu-Ning Wang, Zhi-Heng Wu, Xun-Lan Zhou et al.· 0 citations
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Despite rapid progress in vision-language-action (VLA) models, existing reasoning paradigms still face a fundamental \emph{state-representation mismatch} in open-loop planning. Given only an initial observation, models must internally simulate action-conditioned state transitions, whereas text-, pixel-, and latent-spac...
Jun-Hao Xiao, Hao-Xiang Zhao, Meng-Hao Fang et al.· 0 citations
The results demonstrate the feasibility of harness distillation for robotics: intervention experience can be accumulated, refined through execution, and reused across agents to improve manipulation.
Seungyeon Kim, Junhoo Lee, Minkyu Kim et al.· 1 citation
VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic, and benchmarks 12 state-of-the-art video world models.
Tian-Yi Wang, Wang-Sheng Du, Jia-Zhou Chen et al.· 0 citations
This work presents VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis, and introduces robust geometry conditioning, combining training-time regularization with inference-time guidance for improved robustness.
Kang-Jie Chen, Xiang-Yu Li, Dong-Bin Zhang et al.· 0 citations
DPP enables real-time dynamic manipulation on a single consumer GPU without additional training on dynamic data and constructs a counterfactual observation that places a predicted target position in a familiar robot context, allowing the model to invoke an existing manipulation skill rather than generate a recovery beh...
Sunwoo Park, Won-Sang Lee, Seonghyun Jin et al.· 0 citations
Progress in AI-based retinal image analysis has advanced with foundation models, yet evaluating their reliability remains challenging. Performance reported on a single dataset does not capture how models behave under dataset shift, across clinical definitions, or for different patient subgroups. This limitation is part...
David S. Restrepo, Chen-Wei Wu, L. Nakayama et al.· 0 citations
Grounding is a core capability of spatial vision-language models, yet most existing work focuses only on where a referred object is. Many 3D tasks also require knowing how it is oriented. Although existing 3D VLMs may predict oriented boxes, box pose does not explicitly capture object-centric orientation or symmetry-in...
Tuo Liang, Di-Sheng Liu, Neng-Bo Wang 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.
