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SkillsMP は ADu2021/skillXiv から 1,228 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
ADu2021/skillXiv収集済み skill 1,228 件中 40 件を表示しています。
Enable VLMs to find relevant clips in long videos through sparse observation and graph-based propagation. Iteratively hypothesize promising segments, extract multimodal evidence (captions, OCR, speech), and propagate relevance scores via visual-temporal…
原文の言語: 英語
Replace zero-order hold assumptions with perception-constrained approximation via four-module caching system: motion-aware skip thresholds, saliency-weighted drift, least-squares blending, and adaptive scheduling. Achieves 2.1–2.3× speedup at 2B scale with…
原文の言語: 英語
Categorize ML/AI research papers into 11 types based on their title and abstract. Returns structured JSON with a primary category, optional secondary categories, extractability rating, and rationale. Designed for the SkillXiv paper2skill pipeline as a triage…
原文の言語: 英語
Convert arXiv papers that apply ML techniques to real-world domains into application-transfer skills. Extracts problem formulation, domain adaptation gaps, and deployment recipes. Use this skill when extracting skills from Category 1 (Application Transfer)…
原文の言語: 英語
Convert component innovation papers into drop-in replacement guides. Extracts what was swapped, why, conditions for when it helps, and the performance delta. Use this skill when extracting skills from Category 5 (Component Innovation) papers — BatchNorm-style…
原文の言語: 英語
Convert dataset and benchmark papers into evaluation infrastructure skills. For datasets: extracts collection protocol, annotation design, quality control. For benchmarks: extracts task definition, metric selection, leaderboard design. Use this skill when…
原文の言語: 英語
Convert foundational papers that create new subfields into conceptual framework skills. Extracts problem definitions, vocabulary, founding experiments, and opened research directions. Use this skill when extracting skills from Category 8 (Field Foundation)…
原文の言語: 英語
Convert insight-driven papers into minimal reproducible recipes built around a single non-obvious observation. Extracts the key insight, why the problem seemed hard, and the minimal implementation. Use this skill when extracting skills from Category 6…
原文の言語: 英語
Convert mechanistic analysis papers into transferable analytical methodology skills. Extracts the research question, analytical instrument, controlled confounds, and practitioner implications. Use this skill when extracting skills from Category 9 (Mechanistic…
原文の言語: 英語
Convert papers that disprove conventional wisdom into paradigm-challenge skills. Extracts the prior belief, the falsifying experiment, and the revised principle. Use this skill when extracting skills from Category 3 (Paradigm Challenge) papers — papers that…
原文の言語: 英語
Convert research infrastructure papers into design pattern guides. Extracts capability gaps addressed, API design decisions, performance/usability trade-offs, and integration patterns. Use this skill when extracting skills from Category 7 (Research…
原文の言語: 英語
Convert scaling and efficiency papers into practical resource planning guides. Extracts empirical scaling laws, compute-optimal allocation rules, and budget-performance trade-offs. Use this skill when extracting skills from Category 11 (Scaling and…
原文の言語: 英語
Convert survey and synthesis papers into field navigation guides. Extracts taxonomies, method selection decision trees, literature navigation heuristics, and open problems. Use this skill when extracting skills from Category 10 (Survey and Synthesis) papers —…
原文の言語: 英語
Convert systematic empiricism papers into ranked practitioner checklists. Extracts implementation tricks, hyperparameter findings, and design choice ablations with conditions of applicability. Use this skill when extracting skills from Category 4 (Systematic…
原文の言語: 英語
Convert arXiv and ML/AI research papers into ready-to-use Claude agent skills in seconds — so anyone can apply cutting-edge techniques without reading the full paper. Use this skill whenever the user wants to turn a paper into a skill, extract practical…
原文の言語: 英語
Mitigate long-tail distribution problems in VLM training data through adaptive rebalancing and diffusion-based synthesis. Uses entity distribution analysis to identify head/tail imbalance and applies targeted data augmentation, improving LLaVA 1.5 performance…
原文の言語: 英語
Adaptively prune visual tokens from vision encoders by reconstructing discarded features from retained ones, reducing computational cost by 50% while maintaining task performance on OCR and image understanding tasks.
原文の言語: 英語
Train efficient reasoning models using stage-wise context scaling and complexity-aware data selection. Achieves 49.6% accuracy on AIME 2024 while reducing training steps by 50% through alternating compress-extend cycles that progressively refine reasoning…
原文の言語: 英語
Edit 3D faces with flexible mask layouts using only a few training samples. FFaceNeRF employs geometry adapters with feature injection and latent mixing for tri-plane augmentation, enabling rapid NeRF adaptation without fixed segmentation masks. Ideal for…
原文の言語: 英語
Build a conversational AI that combines image geolocalization with contextual geographical knowledge. GAEA enables users to query precise GPS locations from images while receiving conversational responses about places, their attributes, and regional…
原文の言語: 英語
Segment novel 3D point cloud classes with few support samples by combining dense but noisy pseudo-labels from 3D vision-language models with precise sparse few-shot annotations. GFS-VL adapts to new classes while retaining base class performance, using…
原文の言語: 英語
Learn to enhance LLM post-training for diverse creative outputs by weighting training pairs using deviation metrics (semantic and style diversity). Applies to models where standard alignment reduces diversity, enabling competitive quality with higher output…
原文の言語: 英語
Coordinate specialized agents with distinct personality traits (Openness, Agreeableness, Conscientiousness, Extraversion) to solve complex scientific problems across text and vision, using a Critic agent to apply Socratic questioning for iterative refinement…
原文の言語: 英語
Optimize task-specific prompts using five cooperative agents (Planner, Teacher, Critic, Student, Target) in a POMDP framework, where the Planner generates adaptive trajectories and a Teacher-Critic-Student triad refines prompts through Socratic dialogue,…
原文の言語: 英語
Improve mathematical problem-solving in multimodal models by decoupling visual perception from inference reasoning. A two-stage pipeline extracts essential visual information and reasoned properties before passing enriched text to inference models,…
原文の言語: 英語
Improve diffusion model alignment with human preferences by handling subjective and conflicting annotations. Adaptive-DPO incorporates minority-instance metrics (intra-annotator confidence and inter-annotator stability) to distinguish majority from minority…
原文の言語: 英語
Train vision-language models for complex reasoning by alternating SFT (supervised fine-tuning via text-only reasoning models) and curriculum RL (Group Relative Policy Optimization). Progressively improve through iterative cycles where each iteration generates…
原文の言語: 英語
Enable identity-aware video question answering with one-shot learning using Mixture-of-Heads enhanced ViLLM. Learns subject-specific features from single video through synthetic augmentation and progressive image-to-video training, enabling recognition of…
原文の言語: 英語
Learn coordinated manipulation behaviors for multi-robot systems using compositional constraints that enforce safe and efficient collaboration. Generate training data through automated collection with task-specific constraint interfaces, then train imitation…
原文の言語: 英語
Personalize image generation and editing from a single reference image through inference-time LoRA optimization. Iteratively update model parameters based on visual similarity scores without training encoders or fine-tuning on multiple images.
原文の言語: 英語
Create real-time full-body talking avatars for AR using hybrid parametric-Gaussian representations. Teacher-student distillation transfers pose-dependent deformations from a large network to a compact student model, enabling 90+ FPS rendering on mobile…
原文の言語: 英語
Evaluate text-to-video alignment through fine-grained semantic understanding via multi-agent question generation and knowledge-augmented answering. Generate 12,000 atomic yes/no questions from 2,000 prompts across 10 evaluation categories, achieving 58.47…
原文の言語: 英語
Build autoregressive image generators using post-training quantization that bridges continuous VAE tokens with discrete vocabulary modeling. Achieves state-of-the-art visual quality via dimension-wise token prediction without training instability.
原文の言語: 英語
Enable flexible control over video diffusion models through multi-modal control signals (edges, masks, poses) without retraining. Apply lightweight Transformer-based auxiliary modules to add Canny edge, segmentation, and pose constraints to frozen pre-trained…
原文の言語: 英語
Replace monolithic 3D Gaussian Splatting with two-expert architecture separating geometry (pose) estimation from appearance synthesis. Converges 30× faster (5K vs 150K iterations) while matching pose-dependent methods. Works best for multi-view reconstruction…
原文の言語: 英語
Replace standard likelihood-based video diffusion training with decoupled physics discriminators and DPO post-training to suppress physically implausible behaviors (object penetration, anti-gravity motion) in robotic manipulation videos. Use when generating…
原文の言語: 英語
Replace standard supervised fine-tuning loss with a dual-objective loss that jointly optimizes over both concrete instances and their abstract representations (entity-masked versions), eliminating need for replay buffers and improving cumulative accuracy by…
原文の言語: 英語
Automate systematic literature reviews in epidemiology using agentic AI pipelines. Achieves 58x speed-up (7 weeks to 20 hours) by automating article retrieval, screening, data extraction, and report synthesis. Demonstrates that review quality depends on model…
原文の言語: 英語
Reduce video token overhead by 4-100x through autoregressive patch selection, enabling MLLMs to process 1K-frame 4K video efficiently. Uses next-token prediction to identify multi-scale patches that matter. Achieves 19x speedup on vision transformers. Use…
原文の言語: 英語
Establishes Active-Vision Foundation Models (AVFM) as a new problem class and proposes CanViT: a retinotopic ViT backbone with Canvas Attention that decouples thinking (glimpse processing) from memory (scene canvas). Dense latent distillation from DINOv3…
原文の言語: 英語