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新论文 · v1arXiv:2611.09071

Vertex Deletion on $H$-Induced-Minor-Free Graphs: Subexponential FPT Algorithms and Approximation Schemes

Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, Jie Xue, Meirav Zehavi

原文摘要

We study vertex-deletion problems on string graphs and, more generally, on graphs excluding a fixed graph as an induced minor. For every fixed graph $H$, fixed nonnegative integer $t$, and fixed monadic second-order formula $φ$, we consider the problem of finding a minimum-cardinality set $S$ such that $G-S$ has treewidth at most $t$ and satisfies $φ$. This captures classic problems such as Vertex Cover, Feedback Vertex Set, and deletion to graphs of any fixed bounded treewidth. We give an algorithm with running time $2^{k^{2/3} \log^{O(1)} k}n^{O(1)}$ on $H$-induced-minor-free graphs, where $k$ is the optimum deletion-set size. This resolves in the affirmative the open problem highlighted by Bonnet and Rzążewski [Algorithmica 2019] and Berthe, Bougeret, Gonçalves, and Raymond [ICALP 2025] regarding the existence of a subexponential parameterized algorithm for Feedback Vertex Set on string graphs. Our algorithm significantly improves over existing parameterized algorithms for more restricted classes of problems on more restricted classes of graphs. When the property defined by $φ$ is hereditary, we also obtain a quasi-polynomial time approximation scheme, namely an algorithm that produces a $(1+\varepsilon)$-approximate solution in quasi-polynomial time. Both the sub-exponential algorithm and the quasi-polynomial time approximation scheme build on the same new structural and algorithmic insights.

cs.DS · cs.DM

首次观测 · 记录 #1CVF · CVPR 2026:cvf:15205bf760acf7393078d8af917bc923CVF · CVPR 2026

StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

He, Boyu, Ye, Yunfan, Liu, Chang, Wu, Weishang, Liu, Fang, Cai, Zhiping

原文摘要

Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints (e.g., semantic masks) restricting applicability; 3) rigid feature associations lacking adaptive global-local alignment, failing to balance fine-grained stylization and global content preservation. These limitations, particularly the inability to flexibly leverage style inputs, fundamentally restrict style transfer in terms of personalization, accuracy, and adaptability.To address these, we propose StyleGallery, a training-free and semantic-aware framework that supports arbitrary reference images as input and enables effective personalized customization.It comprises three core stages: semantic region segmentation (adaptive clustering on latent diffusion features to divide regions without extra inputs); clustered region matching (block filtering on extracted features for precise alignment); and style transfer optimization (energy function-guided diffusion sampling with regional style loss to optimize stylization).Experiments on our introduced benchmark demonstrate that StyleGallery outperforms state-of-the-art methods in content structure preservation, regional stylization, interpretability, and personalized customization, particularly when leveraging multiple style references. The source code and dataset are available at https://github.com/iiiiiiiword/StyleGallery.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:8a2803cde5d39391caff414fa489e525CVF · CVPR 2026

FluxMem: Adaptive Hierarchical Memory for Streaming Video Understanding

Xie, Yiweng, He, Bo, Wang, Junke, Zheng, Xiangyu, Ye, Ziyi, Wu, Zuxuan

原文摘要

This paper presents FluxMem, a training-free framework for efficient streaming video understanding. FluxMem adaptively compresses redundant visual memory through a hierarchical, two-stage design: (1) a Temporal Adjacency Selection (TAS) module removes redundant visual tokens across adjacent frames, and (2) a Spatial Domain Consolidation (SDC) module further merges spatially repetitive regions within each frame into compact representations. To adapt effectively to dynamic scenes, we introduce a self-adaptive token compression mechanism in both TAS and SDC, which automatically determines the compression rate based on intrinsic scene statistics rather than manual tuning. Extensive experiments demonstrate that FluxMem achieves new state-of-the-art results on existing online video benchmarks, reaching 76.4 on StreamingBench and 67.2 on OVO-Bench under real-time settings, while reducing latency by 69.9% and peak GPU memory by 34.5% on OVO-Bench. Furthermore, it maintains strong offline performance, achieving 73.1 on MLVU while using 65% fewer visual tokens.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:edb444ec78e5c8263ea4f9847ad20ed3CVF · CVPR 2026

Asking like Socrates: Socrates helps VLMs understand remote sensing images

Shao, Run, Li, Ziyu, Zhang, Zhaoyang, Xu, Linrui, He, Xinran, Yuan, Hongyuan, He, Bolei, Dai, Yongxing 等 12 位作者

原文摘要

Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates/.

cs.CV · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表