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3,462 papers
MIDR (Multimodal Indexing for Document Retrieval), a training-free framework for enrichment-augmented indexing that shifts multimodal reasoning to index time, establishes index-time multimodal reasoning as a compelling accuracy-deployment alternative to serving-time visual late interaction.
The results show strong performance on colonoscopy images and competitive performance on prostate MRI, supporting gaze-guided prototype refinement as a promising approach for segmentation-label-efficient, human-in-the-loop medical image segmentation.
HiLRP alone preserves conservation across windowed, spatial-reduction, multi-axis, and linear-attention models, where naive extensions can produce zero or inflated relevance, and localizes attribution failures in class activation mapping.
A training-free framework that localizes each utterance's source span through Source Span Localization and transfers the associated audio-visual latent content from the source interval to the specified target interval through Region-Aware Latent Remapping is proposed.
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This work identifies two complementary laundering regimes: OpenAI models produce the strongest payload disruption across the evaluated schemes, whereas Nano Banana 2 shows that DwtDct remains vulnerable under high-fidelity reconstruction.
This work proposes StainPresetNet - a novel framework that combines structural preservation with dataset-level color mapping while maintaining computational efficiency, and implements pixel-wise normalization guided by preset reference images, enabling multi-directional adaptability without retraining.
The feasibility of using generative AI tools such as Grok, ChatGPT and Gemini to create images of imagined monozygotic twins as a means to increase representation of twins in face recognition training sets is discussed.
This work introduces ViTAMINS, a method that integrates synthetic hard negatives into unsupervised vision transformer pretraining to improve representation quality and motivate reconsidering contrastive learning as a simpler yet powerful alternative to dominant generative and self-distillation approaches.
SinkPruner is proposed, a training-free visual token pruning framework for efficient MLLM inference that follows a coarse-to-fine design with two key modules: a visual sanitizer that filters high-norm redundancies and alleviates attention sink and attention dispersion, and a text-guided pruner that further retains toke...
A stable semi-supervised virtual staining framework that jointly exploits both limited paired data and abundant unpaired source images is proposed, which suppress structural and appearance drift, stabilize semi-supervised stain translation, and promote the preservation of diagnostically relevant information.
A framework for generating large-scale diagram-grounded instruction data by leveraging terminology derived from scientific curricula is introduced, and augmenting existing models such as LLaVA OneVision with SciGram establishes new state-of-the-art performance on diagram question answering.
This work introduces PLANET, an end-to-end multi-object tracker designed to move beyond the image plane, and lifts existing 2D tracking datasets into 3D by embedding reconstructed 3D scene geometry into the features and positional encodings used during query formation.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
Human-Computer Interaction and Visualization
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