Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this effect. We show that such mitigation is insufficient to ensure the validity of MC-VQA as a reliable benchmark for Multimodal Large Language Model (MLLMs): performance rema...
Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf et al.· 0 citations
DAGLFNet is proposed, a pseudo-image-based semantic segmentation framework designed to extract discriminative features that achieves an excellent balance between accuracy and efficiency.
ComplicitSplat is introduced, the first attack that exploits standard 3DGS shading methods to create viewpoint-specific camouflage to embed adversarial content in scene objects that are visible only from specific viewpoints and without requiring access to model architecture or weights.
Matthew Hull, Haoyang Yang, P. Mehta et al.· arXiv.org· 1 citation
Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data---simulation and free-form tactil...
Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh et al.· 0 citations
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It is argued that locality and hierarchy are useful priors for learning field representations that can be injected without compromising modality-agnosticism in LH-NeF, a framework to learn general-purpose tokenized representations of continuous signals.
Alonso Urbano, David W. Romero, Max Zimmer et al.· arXiv.org· 1 citation
Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This structural view motivates evaluating whether models can understand and manipulate charts at the layer level. Existing chart benchmarks, however, primarily assess the correctn...
Xiao-Chuan Zhong, Yi-Fan Hou, Chen-Xi Pang et al.· 0 citations
Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity, scene layout, motion, and fine details across time. Existing training-free methods mainly strengthen cross-frame attention or analyze loca...
Luyao Tang, Bingjun Luo, Dong Yi et al.· 0 citations
This work proposes Cross-Augmentation Similarity (CAS), a simple baseline that performs adaptation only when predictions across augmented views exhibit low similarity, which not only preserves but in some cases improves overall accuracy, even when skipping nearly 85% of the adaptation process.
Si-Ru Jiang, Yu-Wei Liang, Jian Liang et al.· 1 citation
It is shown that spatial coverage is associated with 3D reasoning performance and CoVeR, a deterministic, training-free selector that uses only token coordinates, with no learned signals is introduced, which outperforms prior SOTAs on all three 3D reasoning benchmarks and generalizes as a plug-and-play module tested ac...
This work introduces \emph{ActionSplice], which edits the interrupted state through Counterfactual State Transport (CST), a lightweight corrector that moves the interrupted solver state toward the matched counterfactual solver state induced by the revised action at the same solver step.
P. Taghavi, Ting-Yu Guo, Jonas Lossner et al.· 0 citations
Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition, and achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition.
Igor Pavlovic, Thiemo Wandel, Anton Obukhov et al.· 1 citation· ⚡1
A preliminary quantitative framework for temporal explainability is established and temporal-aware XAI methods that explicitly account for evolving representations in medical video models that explicitly account for evolving representations in medical video models are motivated.
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.