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Visão Computacional

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

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Blog Geração de Imagem

Diffusion enabled Optimal Transport distances for graph matching

arXiv:2607.06646v1 Announce Type: new Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and structural connectivity through optimal transport. While traditional Gromov-Wasserstein and semi-relaxed variants (srGW, srFGW) capture graph structure, they often struggle with sparse, noisy, or partially observed graphs. Inspired by Graph Diffusion Distance, which posits graphs are similar if they enab...

09.07.2026
Blog LLMs & Texto

SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts

arXiv:2607.06620v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry. Existing 3D-aware models either rely on costly 3D-specific data or utilize RGB-only inputs with heuristic sampling and monolithic, shallow fusion, which respectively disrupt essential spatiotemporal connectivity and induce modality contention across diverse spatial tasks. To overcome these bottleneck...

09.07.2026
Blog Dados & Embeddings

Ant Group’s Robbyant Open-Sources LingBot-Vision: A 1B Boundary-Centric Vision Foundation Model for Dense Spatial Perception

Ant Group's Robbyant open-sourced LingBot-Vision, a self-supervised ViT family for dense spatial perception. Masked boundary modeling makes image boundaries a native training signal. The 1B backbone matches or surpasses larger models, and initializes LingBot-Depth 2.0. The post Ant Group’s Robbyant Open-Sources LingBot-Vision: A 1B Boundary-Centric Vision Foundation Model for Dense Spatial Perception appeared first on MarkTechPost .

08.07.2026
Apollo economist warns AI profit gains outside tech could take "well beyond" what Wall Street expects
Blog Visão Computacional

Apollo economist warns AI profit gains outside tech could take "well beyond" what Wall Street expects

Apollo chief economist Torsten Slok sees no AI-driven margin gains outside tech. In regulated industries like healthcare, banking, or pharma, process overhauls and privacy rules could delay productivity boosts by years. If that takes five years instead of five months, many AI stocks face a painful repricing. The article Apollo economist warns AI profit gains outside tech could take "well beyond" what Wall Street expects appeared first on The Decoder .

07.07.2026
Blog Dados & Embeddings

How many labels do you need? A decision framework for cross-habitat marine species recognition

arXiv:2607.02559v1 Announce Type: new Abstract: Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort reliable deployment at new sites requires. We present a decision framework quantifying the trade-off between labelling effort and recognition accuracy when transferring vision systems across marine habitats. The benchmark spans five datasets, three oceans, and three taxono...

07.07.2026
Blog LLMs & Texto

Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

arXiv:2607.03093v1 Announce Type: new Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upon receiving a user response, inevitably introduces latency that compromises conversational fluidity. This stands in sharp contrast to human dialogue, where speakers proactively anticipate and plan future content during natural pauses to ensure seamless interaction. To bri...

07.07.2026
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