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You Only Landmark Once: Lightweight U-Net Face Super Resolution with YOLO-World Landmark Heatmaps
arXiv:2605.14166v2 Announce Type: replace Abstract: Face image super-resolution aims to recover high-resolution facial images from severely degraded inputs. Under extreme upscaling factors, fine facial details are often lost, making accurate reconstruction challenging. Existing methods typically rely on heavy network architectures, adversarial training schemes, or separate alignment networks, increasing model complexity and computational cost.
ActionMap: Robot Policy Learning via Voxel Action Heatmap
Announce Type: new Abstract: Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state into a continuous control signal, has barely changed and remains a single-point predictor across the majority of current VLAs. Whether implemented via autoregressive token bins, L1 regression, or flow-matching denoising, the resulting decoder treats the action space as unstructured,...
Interpretable Modeling of Driver Attention Shifts with a Vision-Language Model
Announce Type: replace Abstract: Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being monitored or why an attention shift may matter. This study examines whether minimal human-grounded supervision can steer a vision--language model toward interpretable descriptions of driver attention shifts. Using selected high-change gaze moments from the Berkeley DeepDrive-Attention...
Interpretable Modeling of Driver Attention Shifts with a Vision--Language Model
arXiv:2508.05852v2 Announce Type: replace Abstract: Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being monitored or why an attention shift may matter. This study examines whether minimal human-grounded supervision can steer a vision--language model toward interpretable descriptions of driver attention shifts. Using selected high-change gaze moments from the Berkeley...
The IsUpMap lets you check the status of over 100 major sites at once
Live status for 80+ popular internet services isUpMap is a real-time status heatmap that checks whether the services you depend on are up, degraded, or down right now. The live dashboard requires JavaScript, but here's what we monitor: - AI: OpenAI, Anthropic, xAI, Groq, Perplexity, Hugging Face, ElevenLabs, Cursor and more. - Developer & Cloud: GitHub, Cloudflare, AWS, Vercel, Netlify, npm, Docker, GitLab, Supabase, Firebase. - Payments: Stripe, Coinbase, Shopify, Plaid, Square, Klarna. -...
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
arXiv:2606.06224v2 Announce Type: replace Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depend on interactions between tissue features.
Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback
arXiv:2606.06113v1 Announce Type: new Abstract: Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still...
Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
arXiv:2606.04039v1 Announce Type: new Abstract: Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps), yet deployed to guide iterative, long-horizon search processes. In this paper, we present DyNACO, a novel framework that achieves dynamic neural guidance by periodically observing the pheromone distribution and the incumbent solution. To make DyNACO tractable at scale,...
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Announce Type: new Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depend on interactions between tissue features.