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3,462 papers
Anatomy Segmentation Pretraining (ASP) is proposed, a novel method to incorporate anatomical information during foundation-model pretraining, which outperforms existing models in biological age estimation.
Experiments show that MyBuddy significantly enhances the performance of foundation models on BuddyVQA, a benchmark for companion-style question answering (QA) on egocentric streaming video, and generalize to other streaming and common video QA benchmarks, demonstrating the applicability and effectiveness of the approac...
The analysis shows that CoTs do not reliably track visual evidence that influences model predictions, and it is found that Predict-then-Explain explanations align more strongly with perturbation-induced probability shifts than pre-answer CoTs, while binary vCT scores are often nearly saturated.
Experimental evaluation on two public retinal fundus datasets demonstrates that deep feature-based methods consistently outperform handcrafted feature-based methods, while the proposed attention-enhanced framework achieves the best overall performance.
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This work proposes a geometry-aware composite metric that jointly leverages Euclidean distance and cosine similarity to capture token diversity from complementary perspectives and uses these two metrics to estimate the redundancy level of each attention head, allowing adaptive weighting between diversity and importance...
The Flow State Attention Network (FSAN) separately encodes point cloud and flow conditions, then partitions the geometry into multiple flow states via learnable soft assignments, and uses flow features to update these state representations, which enables fine-grained, state-specific interaction between geometry and flo...
Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single reference annotation. We show that this disagreement is structured rather than random noise. Re-annotating the SMILE-Temporal benchmark (672 videos, 1,683 events) with 3-5 an...
The findings document prompt shortening under the configured gate penalty; they do not establish sample-specific length allocation, superiority over fixed short prompts, or clinical utility.
A training-free framework that elicits latent responses through counterfactual scale interventions and interprets candidate reliability from decoder-internal spatial convergence, semantic persistence, and cross-scale conflicts is proposed, which consistently improves average precision and average precision for small ob...
OracleZoom is presented, an on-policy distillation-inspired, reference-constrained framework that trains on its trajectory while carrying the last ground-truth evidence beyond the supervision boundary, and achieves the state-of-the-art SR quality across zooming scales.
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural language instructions. Current zero-shot VLN-CE methods either rely on pre-trained waypoint predictors or require multiple queries to large models per step. To address prohi...
An abundance-guided self-attention network (AGSA-Net), an abundance-guided self-attention network that explicitly integrates spectral unmixing priors into the classification process, and demonstrates the benefit of incorporating abundance-guided contextual modeling, particularly in heterogeneous urban scenes.
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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