✨Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition
📝 Summary:
CroBo is a visual state representation framework that learns what-is-where composition for robotics. It uses global-to-local reconstruction to encode scene element identities and spatial locations in a compact token. This enables tracking scene dynamics for sequential decision making.
🔹 Publication Date: Published on Mar 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13904
• PDF: https://arxiv.org/pdf/2603.13904
• Project Page: https://seokminlee-chris.github.io/CroBo-ProjectPage/
• Github: https://github.com/SeokminLee-Chris/CroBo
==================================
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#Robotics #ComputerVision #SceneUnderstanding #AI #StateRepresentation
📝 Summary:
CroBo is a visual state representation framework that learns what-is-where composition for robotics. It uses global-to-local reconstruction to encode scene element identities and spatial locations in a compact token. This enables tracking scene dynamics for sequential decision making.
🔹 Publication Date: Published on Mar 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13904
• PDF: https://arxiv.org/pdf/2603.13904
• Project Page: https://seokminlee-chris.github.io/CroBo-ProjectPage/
• Github: https://github.com/SeokminLee-Chris/CroBo
==================================
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#Robotics #ComputerVision #SceneUnderstanding #AI #StateRepresentation
❤1
✨VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models
📝 Summary:
VFIG is a vision-language model that converts raster images into scalable vector graphics SVG. It employs a 66K dataset and hierarchical training for high-fidelity conversion, outperforming open-source models and matching proprietary ones.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.24575
• PDF: https://arxiv.org/pdf/2603.24575
• Project Page: https://vfig-proj.github.io/
• Github: https://github.com/RAIVNLab/VFig
🔹 Models citing this paper:
• https://huggingface.co/XunmeiLiu/VFIG-4B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/allenai/VFig-Image2SVG-Demo
==================================
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#VisionLanguageModels #SVG #VectorGraphics #AI #ComputerVision
📝 Summary:
VFIG is a vision-language model that converts raster images into scalable vector graphics SVG. It employs a 66K dataset and hierarchical training for high-fidelity conversion, outperforming open-source models and matching proprietary ones.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.24575
• PDF: https://arxiv.org/pdf/2603.24575
• Project Page: https://vfig-proj.github.io/
• Github: https://github.com/RAIVNLab/VFig
🔹 Models citing this paper:
• https://huggingface.co/XunmeiLiu/VFIG-4B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/allenai/VFig-Image2SVG-Demo
==================================
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#VisionLanguageModels #SVG #VectorGraphics #AI #ComputerVision
✨Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math
📝 Summary:
ScratchMath introduces a benchmark for analyzing errors in student handwritten math. It reveals MLLMs significantly lag human experts in visual and logical reasoning, but proprietary models show potential for error explanation.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24961
• PDF: https://arxiv.org/pdf/2603.24961
• Project Page: https://bbsngg.github.io/ScratchMath/
• Github: https://github.com/ai-for-edu/ScratchMath
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ScratchMath introduces a benchmark for analyzing errors in student handwritten math. It reveals MLLMs significantly lag human experts in visual and logical reasoning, but proprietary models show potential for error explanation.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24961
• PDF: https://arxiv.org/pdf/2603.24961
• Project Page: https://bbsngg.github.io/ScratchMath/
• Github: https://github.com/ai-for-edu/ScratchMath
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models
📝 Summary:
Language models typically give one answer, but many tasks have multiple solutions. This paper presents multi-answer RL, allowing LMs to generate multiple plausible answers with confidence in a single pass, improving diversity, accuracy, and computational efficiency.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24844
• PDF: https://arxiv.org/pdf/2603.24844
• Project Page: https://multi-answer-rl.github.io/
• Github: https://github.com/ishapuri/multi_answer_rl
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Language models typically give one answer, but many tasks have multiple solutions. This paper presents multi-answer RL, allowing LMs to generate multiple plausible answers with confidence in a single pass, improving diversity, accuracy, and computational efficiency.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24844
• PDF: https://arxiv.org/pdf/2603.24844
• Project Page: https://multi-answer-rl.github.io/
• Github: https://github.com/ishapuri/multi_answer_rl
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨AVO: Agentic Variation Operators for Autonomous Evolutionary Search
📝 Summary:
Agentic variation operators enable autonomous discovery of performance-critical micro-architectural optimizations for attention kernels, outperforming state-of-the-art implementations on advanced GPU ...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24517
• PDF: https://arxiv.org/pdf/2603.24517
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Agentic variation operators enable autonomous discovery of performance-critical micro-architectural optimizations for attention kernels, outperforming state-of-the-art implementations on advanced GPU ...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24517
• PDF: https://arxiv.org/pdf/2603.24517
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching
📝 Summary:
WAFT-Stereo achieves state-of-the-art stereo matching performance by replacing cost volumes with warping techniques, demonstrating superior efficiency and accuracy on major benchmarks. AI-generated su...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24836
• PDF: https://arxiv.org/pdf/2603.24836
• Github: https://github.com/princeton-vl/WAFT-Stereo
🔹 Models citing this paper:
• https://huggingface.co/MemorySlices/WAFT-Stereo
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
WAFT-Stereo achieves state-of-the-art stereo matching performance by replacing cost volumes with warping techniques, demonstrating superior efficiency and accuracy on major benchmarks. AI-generated su...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24836
• PDF: https://arxiv.org/pdf/2603.24836
• Github: https://github.com/princeton-vl/WAFT-Stereo
🔹 Models citing this paper:
• https://huggingface.co/MemorySlices/WAFT-Stereo
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
📝 Summary:
QuantAgent is a multi-agent LLM framework for high-frequency trading. It uses specialized agents for indicators, patterns, trends, and risk to make rapid decisions. It outperforms existing neural and rule-based systems in accuracy and returns.
🔹 Publication Date: Published on Sep 12, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.09995
• PDF: https://arxiv.org/pdf/2509.09995
• Project Page: https://Y-Research-SBU.github.io/QuantAgent/
• Github: https://github.com/Y-Research-SBU/QuantAgent
==================================
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#LLM #MultiAgent #HighFrequencyTrading #FinTech #AlgorithmicTrading
📝 Summary:
QuantAgent is a multi-agent LLM framework for high-frequency trading. It uses specialized agents for indicators, patterns, trends, and risk to make rapid decisions. It outperforms existing neural and rule-based systems in accuracy and returns.
🔹 Publication Date: Published on Sep 12, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.09995
• PDF: https://arxiv.org/pdf/2509.09995
• Project Page: https://Y-Research-SBU.github.io/QuantAgent/
• Github: https://github.com/Y-Research-SBU/QuantAgent
==================================
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#LLM #MultiAgent #HighFrequencyTrading #FinTech #AlgorithmicTrading
❤2
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❤1
✨ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling
📝 Summary:
ShotStream enables real-time interactive multi-shot video generation via a novel causal architecture. It uses dual-cache memory for visual consistency and two-stage distillation to reduce latency and error. This achieves high-quality, coherent videos at 16 FPS, paving the way for dynamic storytel...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25746
• PDF: https://arxiv.org/pdf/2603.25746
• Project Page: https://luo0207.github.io/ShotStream/
• Github: https://github.com/KlingAIResearch/ShotStream
🔹 Models citing this paper:
• https://huggingface.co/KlingTeam/ShotStream
==================================
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#VideoGeneration #GenerativeAI #RealTimeAI #DeepLearning #AIStorytelling
📝 Summary:
ShotStream enables real-time interactive multi-shot video generation via a novel causal architecture. It uses dual-cache memory for visual consistency and two-stage distillation to reduce latency and error. This achieves high-quality, coherent videos at 16 FPS, paving the way for dynamic storytel...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25746
• PDF: https://arxiv.org/pdf/2603.25746
• Project Page: https://luo0207.github.io/ShotStream/
• Github: https://github.com/KlingAIResearch/ShotStream
🔹 Models citing this paper:
• https://huggingface.co/KlingTeam/ShotStream
==================================
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#VideoGeneration #GenerativeAI #RealTimeAI #DeepLearning #AIStorytelling
✨Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
📝 Summary:
Hybrid Memory improves video world models by consistently tracking dynamic subjects during occlusion. It combines static background archiving with active dynamic subject tracking. This ensures motion continuity and outperforms existing methods in generation quality.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25716
• PDF: https://arxiv.org/pdf/2603.25716
• Project Page: https://kj-chen666.github.io/Hybrid-Memory-in-Video-World-Models/
• Github: https://github.com/H-EmbodVis/HyDRA
🔹 Models citing this paper:
• https://huggingface.co/H-EmbodVis/HyDRA
==================================
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#VideoWorldModels #ComputerVision #AI #MachineLearning #GenerativeAI
📝 Summary:
Hybrid Memory improves video world models by consistently tracking dynamic subjects during occlusion. It combines static background archiving with active dynamic subject tracking. This ensures motion continuity and outperforms existing methods in generation quality.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25716
• PDF: https://arxiv.org/pdf/2603.25716
• Project Page: https://kj-chen666.github.io/Hybrid-Memory-in-Video-World-Models/
• Github: https://github.com/H-EmbodVis/HyDRA
🔹 Models citing this paper:
• https://huggingface.co/H-EmbodVis/HyDRA
==================================
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#VideoWorldModels #ComputerVision #AI #MachineLearning #GenerativeAI
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✨Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models
📝 Summary:
Know3D integrates vision-language models into 3D generation via latent hidden-state injection. This enables language-controlled synthesis of unseen back-views, transforming stochastic hallucination into a semantically guided process for 3D assets.
🔹 Publication Date: Published on Mar 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22782
• PDF: https://arxiv.org/pdf/2603.22782
• Project Page: https://xishuxishu.github.io/Know3D.github.io/
• Github: https://github.com/xishuxishu/Know3D
✨ Spaces citing this paper:
• https://huggingface.co/spaces/xishushu/Know3D
==================================
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#3DGeneration #VisionLanguageModels #GenerativeAI #DeepLearning #AIResearch
📝 Summary:
Know3D integrates vision-language models into 3D generation via latent hidden-state injection. This enables language-controlled synthesis of unseen back-views, transforming stochastic hallucination into a semantically guided process for 3D assets.
🔹 Publication Date: Published on Mar 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22782
• PDF: https://arxiv.org/pdf/2603.22782
• Project Page: https://xishuxishu.github.io/Know3D.github.io/
• Github: https://github.com/xishuxishu/Know3D
✨ Spaces citing this paper:
• https://huggingface.co/spaces/xishushu/Know3D
==================================
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#3DGeneration #VisionLanguageModels #GenerativeAI #DeepLearning #AIResearch
✨Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models
📝 Summary:
Full-duplex speech models need high-quality multi-speaker conversational data, which is scarce and difficult to process due to natural dialogue dynamics. This paper introduces Sommelier, a robust, scalable, open-source data processing pipeline to address this data bottleneck.
🔹 Publication Date: Published on Mar 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25750
• PDF: https://arxiv.org/pdf/2603.25750
• Project Page: https://kyudan1.github.io/sommelier.github.io//
==================================
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#SpeechAI #AudioProcessing #DataProcessing #OpenSource #NLP
📝 Summary:
Full-duplex speech models need high-quality multi-speaker conversational data, which is scarce and difficult to process due to natural dialogue dynamics. This paper introduces Sommelier, a robust, scalable, open-source data processing pipeline to address this data bottleneck.
🔹 Publication Date: Published on Mar 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25750
• PDF: https://arxiv.org/pdf/2603.25750
• Project Page: https://kyudan1.github.io/sommelier.github.io//
==================================
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#SpeechAI #AudioProcessing #DataProcessing #OpenSource #NLP
✨Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
📝 Summary:
Trace2Skill generates transferable LLM agent skills by analyzing diverse execution traces in parallel and consolidating them via inductive reasoning. This framework significantly improves performance, transfers across LLM scales, and generalizes to new settings without model updates.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25158
• PDF: https://arxiv.org/pdf/2603.25158
==================================
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#LLM #AgentAI #TransferLearning #MachineLearning #AIResearch
📝 Summary:
Trace2Skill generates transferable LLM agent skills by analyzing diverse execution traces in parallel and consolidating them via inductive reasoning. This framework significantly improves performance, transfers across LLM scales, and generalizes to new settings without model updates.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25158
• PDF: https://arxiv.org/pdf/2603.25158
==================================
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#LLM #AgentAI #TransferLearning #MachineLearning #AIResearch
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✨PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference
📝 Summary:
PackForcing enables efficient, long-video generation via hierarchical KV-cache management and spatiotemporal compression, overcoming memory and consistency issues. It generates 2-minute coherent videos on a single GPU, demonstrating that short-video training suffices for high-quality long-video s...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25730
• PDF: https://arxiv.org/pdf/2603.25730
• Github: https://github.com/ShandaAI/PackForcing
==================================
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#VideoGeneration #GenerativeAI #DeepLearning #ModelEfficiency #LongContext
📝 Summary:
PackForcing enables efficient, long-video generation via hierarchical KV-cache management and spatiotemporal compression, overcoming memory and consistency issues. It generates 2-minute coherent videos on a single GPU, demonstrating that short-video training suffices for high-quality long-video s...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25730
• PDF: https://arxiv.org/pdf/2603.25730
• Github: https://github.com/ShandaAI/PackForcing
==================================
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#VideoGeneration #GenerativeAI #DeepLearning #ModelEfficiency #LongContext
✨Diffutron: A Masked Diffusion Language Model for Turkish Language
📝 Summary:
Diffutron introduces a compact masked diffusion language model for Turkish. It uses resource-efficient LoRA-based pre-training and progressive instruction tuning. The model achieves competitive performance for non-autoregressive Turkish text generation despite its small size.
🔹 Publication Date: Published on Mar 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20466
• PDF: https://arxiv.org/pdf/2603.20466
🔹 Models citing this paper:
• https://huggingface.co/diffutron/DiffutronLM-0.3B-Instruct
• https://huggingface.co/diffutron/DiffutronLM-0.3B-Base
• https://huggingface.co/diffutron/DiffutronLM-0.3B-1st-Stage
✨ Datasets citing this paper:
• https://huggingface.co/datasets/diffutron/DiffutronLM-Pretraining-Corpus
==================================
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✓ https://t.iss.one/DataScienceT
#LanguageModels #TurkishNLP #DiffusionModels #NLP #AI
📝 Summary:
Diffutron introduces a compact masked diffusion language model for Turkish. It uses resource-efficient LoRA-based pre-training and progressive instruction tuning. The model achieves competitive performance for non-autoregressive Turkish text generation despite its small size.
🔹 Publication Date: Published on Mar 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20466
• PDF: https://arxiv.org/pdf/2603.20466
🔹 Models citing this paper:
• https://huggingface.co/diffutron/DiffutronLM-0.3B-Instruct
• https://huggingface.co/diffutron/DiffutronLM-0.3B-Base
• https://huggingface.co/diffutron/DiffutronLM-0.3B-1st-Stage
✨ Datasets citing this paper:
• https://huggingface.co/datasets/diffutron/DiffutronLM-Pretraining-Corpus
==================================
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✓ https://t.iss.one/DataScienceT
#LanguageModels #TurkishNLP #DiffusionModels #NLP #AI
✨MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies
📝 Summary:
MEDOPENCLAW and MEDFLOWBENCH enable evaluating medical VLMs in interactive 3D environments, unlike static 2D images. Surprisingly, top VLMs struggle with professional tools due to poor spatial grounding. This work highlights a critical gap for auditable, full-study medical agents.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24649
• PDF: https://arxiv.org/pdf/2603.24649
• Project Page: https://jakobshen.github.io/MedOpenClaw
==================================
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#MedicalAI #VLMs #MedicalImaging #AuditableAI #3DImaging
📝 Summary:
MEDOPENCLAW and MEDFLOWBENCH enable evaluating medical VLMs in interactive 3D environments, unlike static 2D images. Surprisingly, top VLMs struggle with professional tools due to poor spatial grounding. This work highlights a critical gap for auditable, full-study medical agents.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24649
• PDF: https://arxiv.org/pdf/2603.24649
• Project Page: https://jakobshen.github.io/MedOpenClaw
==================================
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#MedicalAI #VLMs #MedicalImaging #AuditableAI #3DImaging
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✨LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset
📝 Summary:
This paper introduces KITScenes LongTail, a new dataset for long-tail driving events. It offers multi-view video, trajectories, and multilingual expert reasoning traces. This resource improves few-shot generalization and evaluates multimodal models instruction following capabilities.
🔹 Publication Date: Published on Mar 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23607
• PDF: https://arxiv.org/pdf/2603.23607
• Project Page: https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail
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#AutonomousDriving #ComputerVision #Datasets #LongTailLearning #MultimodalAI
📝 Summary:
This paper introduces KITScenes LongTail, a new dataset for long-tail driving events. It offers multi-view video, trajectories, and multilingual expert reasoning traces. This resource improves few-shot generalization and evaluates multimodal models instruction following capabilities.
🔹 Publication Date: Published on Mar 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23607
• PDF: https://arxiv.org/pdf/2603.23607
• Project Page: https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail
==================================
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✓ https://t.iss.one/DataScienceT
#AutonomousDriving #ComputerVision #Datasets #LongTailLearning #MultimodalAI
✨Natural-Language Agent Harnesses
📝 Summary:
Natural-Language Agent Harnesses NLAHs and Intelligent Harness Runtime IHR enable portable, executable agent harness design through natural language. This externalizes control logic from code, making harnesses easier to transfer, compare, and study.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25723
• PDF: https://arxiv.org/pdf/2603.25723
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#NaturalLanguageProcessing #AI #AIAgents #SoftwareEngineering #CodePortability
📝 Summary:
Natural-Language Agent Harnesses NLAHs and Intelligent Harness Runtime IHR enable portable, executable agent harness design through natural language. This externalizes control logic from code, making harnesses easier to transfer, compare, and study.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25723
• PDF: https://arxiv.org/pdf/2603.25723
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#NaturalLanguageProcessing #AI #AIAgents #SoftwareEngineering #CodePortability
✨RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation
📝 Summary:
RealChart2Code is a new benchmark assessing VLM ability to generate complex, multi-panel charts from real data. It reveals significant performance gaps between proprietary and open-weight models, highlighting VLM struggles with intricate plots.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25804
• PDF: https://arxiv.org/pdf/2603.25804
• Project Page: https://huggingface.co/datasets/zjj1233/RealChart2Code
• Github: https://github.com/Speakn0w/RealChart2Code
==================================
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✓ https://t.iss.one/DataScienceT
#VLM #ChartToCode #Benchmark #AI #DataScience
📝 Summary:
RealChart2Code is a new benchmark assessing VLM ability to generate complex, multi-panel charts from real data. It reveals significant performance gaps between proprietary and open-weight models, highlighting VLM struggles with intricate plots.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25804
• PDF: https://arxiv.org/pdf/2603.25804
• Project Page: https://huggingface.co/datasets/zjj1233/RealChart2Code
• Github: https://github.com/Speakn0w/RealChart2Code
==================================
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✓ https://t.iss.one/DataScienceT
#VLM #ChartToCode #Benchmark #AI #DataScience
✨GenMask: Adapting DiT for Segmentation via Direct Mask
📝 Summary:
GenMask directly trains a DiT for joint image generation and segmentation using a novel timestep sampling strategy. This strategy emphasizes extreme noise for masks, enabling harmonious training. It outperforms indirect adaptation, simplifying the workflow.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23906
• PDF: https://arxiv.org/pdf/2603.23906
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Segmentation #ImageGeneration #DiT #DeepLearning #ComputerVision
📝 Summary:
GenMask directly trains a DiT for joint image generation and segmentation using a novel timestep sampling strategy. This strategy emphasizes extreme noise for masks, enabling harmonious training. It outperforms indirect adaptation, simplifying the workflow.
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23906
• PDF: https://arxiv.org/pdf/2603.23906
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Segmentation #ImageGeneration #DiT #DeepLearning #ComputerVision
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