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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2153ACL Anthology · ACL 2026

An Information-Theoretic Foundation for the Subregular Hierarchy

Mai Phan Quoc Hung N2TP Technology Solutions JSC, Khanh Nguyen Quoc N2TP and Phenikaa University, Đoàn Minh Luong, Duong Thu Ngan, Duong Thi Phuong Thao, Tuan Do Phenikaa University and N2TP Technology Solutions JSC

原文摘要

The Subregular Hypothesis posits that phonological patterns in natural languages occupy a restricted region of the formal language hierarchy, yet the cognitive basis for this restriction remains unclear. We propose an information-theoretic characterization: Strictly Local languages, when formalized as shifts of finite type, are exactly those admitting stationary Markov sources, which exhibit zero conditional mutual information between distant positions given intervening symbols. We prove that certain non-subregular patterns such as first-last assimilation admit no such Markov realization, explaining their unlearnability. Empirical validation on English phonotactics versus Finnish, Turkish, and Hungarian vowel harmony confirms that MI profiles statistically distinguish SL-like from TSL-like patterns (p < 0.001, r = 0.84). This work bridges formal language theory and information theory, offering a unified framework for understanding computational restrictions on natural language phonology.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2159ACL Anthology · ACL 2026

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

Duy Nguyen Department of Computer Science, University of North Carolina at Chapel Hill, Archiki Prasad, Elias Stengel-Eskin University of Texas at Austin, Mohit Bansal University of North Carolina at Chapel Hill

原文摘要

Inference-time steering provides a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying model activations without updating weights. However, existing methods often rely on a global intervention vector, overlook token-level causal influence, and underutilize model logits, especially in multimodal settings where visual and textual inputs contribute unevenly. We propose GrAInS, a contrastive, gradient-based approach that leverages Integrated Gradients to identify top-k influential tokens and construct directional steering vectors based on their contribution to preferred over dispreferred outputs. These vectors guide activation intervention at each layer, preserving the representational scale. GrAInS outperforms fine-tuning and prior steering methods on both LLM and VLM tasks: improving TruthfulQA accuracy by 13.22% (Llama-3.1-8B), reducing MMHal-Bench hallucinations from 0.624 to 0.514 (LLaVA-1.6-7B), and increasing SPA-VL alignment by 8.11%, all without degrading fluency or general capabilities.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.285ACL Anthology · ACL 2026

UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware

Truong Dinh Do, Nguyen-Khang Le Japan Advanced Institute of Science and Technology, Tokyo Institute of Technology, Le-Minh Nguyen Japan Advanced Institute of Science and Technology, Tokyo Institute of Technology

原文摘要

Speculative decoding accelerates large language model (LLM) inference through a draft-and-verify paradigm, yet existing methods face three key limitations: reliance on fixed draft templates that ignore device-specific verification costs, lack of mechanisms to assess draft token quality, and suboptimal tree expansion strategies. We introduce UniSpec, a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages. UniSpec incorporates three novel components: (1) a device-aware calibration mechanism that determines the optimal draft size by measuring the acceptance-time trade-off on each target device; (2) a confidence score estimation module that assigns quality scores to n-grams based on the verifier’s token probabilities, enabling selective retention of high-quality draft candidates; and (3) an improved tree expansion strategy that broadens first-level exploration and applies threshold-based filtering to prune low-confidence nodes. To comprehensively evaluate multilingual performance, we create a comprehensive benchmark, covering seven languages across seven generation tasks. Experiments with various LLM architectures, hardware environments, and languages demonstrate that UniSpec consistently outperforms existing training-free methods, achieving speedups of up to 2.6x while maintaining output quality identical to standard autoregressive decoding. Our code and benchmark are publicly available.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.396ACL Anthology · ACL 2026

Beyond Transcripts: A Renewed Perspective on Audio Chaptering

Fabian Retkowski Karlsruher Institut für Technologie, Maike Züfle Karlsruher Institut für Technologie, Thai Binh Nguyen Karlsruher Institut für Technologie, Jan Niehues, Alexander Waibel

原文摘要

Audio chaptering, the task of automatically segmenting long-form audio into coherent sections, is increasingly important for navigating podcasts, lectures, and videos. Despite its relevance, research remains limited and text-based, leaving key questions unresolved about leveraging audio information, handling ASR errors, and transcript-free evaluation. We address these gaps through three contributions: (1) a systematic comparison between text-based models with acoustic features, a novel audio-only architecture (AudioSeg) operating on learned audio representations, and multimodal LLMs; (2) empirical analysis of factors affecting performance, including transcript quality, acoustic features, duration, and speaker composition; and (3) formalized evaluation protocols contrasting transcript-dependent text-space protocols with transcript-invariant time-space protocols. Our experiments on YTSeg reveal that AudioSeg substantially outperforms text-based approaches, pauses provide the largest acoustic gains, and current MLLMs struggle due to context limitations and weak instruction following.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.564ACL Anthology · ACL 2026

Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages

Saeed Almheiri, Bilal Elbouardi Mohamed bin Zayed University of Artificial Intelligence, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B Manipal University, Parameswari Krishnamurthy International Institute of Information Technology Hyderabad, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi Mohamed bin Zayed University of Artificial Intelligence 等 19 位作者

原文摘要

Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. Prior work has focused on high-resource languages typically evaluates isolated idiom-meaning questions, overlooking realistic discourse. We introduce MIDI, a multilingual idiom dataset spanning 3 high-, 3 medium-, and 12 low-resource languages, curated by native speakers. Unlike previous datasets, MIDI provides idioms embedded in both sentence-level and conversational contexts, capturing both literal and figurative readings. Benchmarking state-of-the-art models shows that idiom comprehension degrades in low-resource languages and that, in all resource tiers, literal interpretations are substantially harder than figurative ones. Conversational context improves performance but does not eliminate these disparities. Through controlled tests and interventions on hidden representations, we further separate memorization from reasoning, exposing core limitations of current models.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.67ACL Anthology · ACL 2026

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Austin Xu Anthropic, Yilun Zhou Massachusetts Institute of Technology, Xuan-Phi Nguyen, Caiming Xiong Salesforce Research, Shafiq Joty Nanyang Technological University and SalesForce.com

原文摘要

To keep pace with the increasing velocity of large language models (LLM) development, model output evaluation has transitioned away from time-consuming human evaluation to automatic evaluation, where LLMs themselves are tasked with assessing and critiquing other model outputs. LLM-as-judge models are a class of generative evaluators that excel in evaluating relatively simple domains, like chat quality, but struggle in reasoning intensive domains where model responses contain more substantive and challenging content. To remedy existing judge shortcomings, we explore training judges with reinforcement learning (RL). We make three key contributions: (1) We propose the Equivalent Initial State Group Relative Policy Optimization (EIS-GRPO) algorithm, which allows us to train our judge to be robust to positional biases that arise in more complex evaluation settings. (2) We introduce ReasoningJudgeBench, a benchmark that evaluates judges in diverse reasoning settings not covered by prior work. (3) We train Judge for Reasoning (J4R), a 7B judge trained with EIS-GRPO that outperforms GPT-4o and the next best small judge by 6.7% and 9%, matching or exceeding the performance of larger GRPO-trained judges on both JudgeBench and ReasoningJudgeBench.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.903ACL Anthology · ACL 2026

Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework

Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong Milltary central hospital 108, Thanh Trung Nguyen, Mai Hong Son Luthien 等 11 位作者

原文摘要

Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated datasets, particularly for low-resource languages. Current black-box methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) to derive diagnostic conclusions. In this paper, we bridge this gap by introducing VietPET-RoI, the first large-scale 3D PET/CT dataset with fine-grained RoI annotation for a low-resource language, comprising 600 PET/CT samples and 1,960 manually annotated RoIs, paired with corresponding clinical reports. Furthermore, to demonstrate the utility of this dataset, we propose HiRRA, a novel framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. This approach shifts from global pattern matching toward localized clinical findings. Additionally, we introduce new clinical evaluation metrics, namely RoI Coverage and RoI Quality Index, that measure both RoI localization accuracy and attribute description fidelity using LLM-based extraction. Extensive evaluation demonstrates that our framework achieves SOTA performance, surpassing existing models by 19.7% in BLEU and 4.7% in ROUGE-L, while achieving a remarkable 45.8% improvement in clinical metrics, indicating enhanced clinical reliability and reduced hallucination. Our code and dataset are available on GitHub.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-tutorials.1ACL Anthology · ACL 2026

Future of Work in the Age of LLMs

Zora Wang Carnegie Mellon University, Yijia Shao Stanford University, David Nguyen Stanford University, Diyi Yang Stanford University

原文摘要

The recent development of large language models (LLMs) has revolutionized the landscape of human work. These models possess the ability to follow complex human instructions and operate versatile computer software, enabling them to participate in, augment, or even automate realistic occupational tasks that once thought to be exclusive to humans. As LLMs are increasingly integrated into workplaces, they are already reshaping labor dynamics (Hoffmann et al., 2024; Demirci et al., 2025) and raising urgent concerns about job displacement, diminished human agency, and overreliance on automation (Hazra et al., 2025). As a result, the future of work is undergoing a profound transformation: How will human occupations and task requirements evolve over time? And what roles will LLM-based systems play, as they become increasingly capable collaborators and autonomous workers? And how can we build technological and data infrastructures to support human-AI collaboration? This tutorial will provide an overview of the future of work shaped by the interplay of LLMs and humans, examining the emerging challenges, opportunities, and ethical considerations in this dynamic landscape. We begin by examining the economic landscape of work and how NLP technologies drive automation, followed by methods for developing LLMs that augment human labor and recent advances in LLM-based agents. We then cover evaluation approaches for workplace contexts, including datasets, benchmarks, and metrics, and conclude with open questions on technical, human, and societal implications.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · 2026 · 正式发表

版本更新 · v6arXiv:2411.07207Google Research

通用地理空间推理的人口动态基础模型

General Geospatial Inference with a Population Dynamics Foundation Model

Mohit Agarwal, Mimi Sun, Chaitanya Kamath, Arbaaz Muslim, Prithul Sarker, Joydeep Paul, Hector Yee, Marcin Sieniek 等 34 位作者

AI 速读 基于摘要

提出人口动态基础模型（PDFM），通过图神经网络融合多源地理数据生成嵌入，在27个地理空间任务上取得领先表现。

cs.LG · cs.CY

arXiv ↗PDF ↗
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Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers.

首次观测 · 记录 #1CVF · CVPR 2026:cvf:077f50c1123edf59b10c9b0965d5e102CVF · CVPR 2026

Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration

Mo, Sicheng, Nguyen, Thao, Zhang, Richard, Kolkin, Nick, Iyer, Siddharth Srinivasan, Shechtman, Eli, Singh, Krishna Kumar, Lee, Yong Jae 等 10 位作者

原文摘要

In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be generated collaboratively. We propose Group Diffusion, unlocking the attention mechanism to be shared across images, rather than limited to just the patches within an image. This enables images to be jointly denoised at inference time, learning both intra and inter-image correspondence. We observe a clear scaling effect -- larger group sizes yield stronger cross-sample attention and better generation quality. Furthermore, we introduce a qualitative measure to capture this behavior and show that its strength closely correlates with FID. Built on standard diffusion transformers, our GroupDiff achieves up to 32.2% FID improvement on ImageNet-256x256. Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:0d216fc1a48def118321bd389687b5b9CVF · CVPR 2026

Verifying Neural Network Robustness with Dual Perturbations

Duong, Hai, Nguyen, Lam, Le, Thanh, Nguyen, ThanhVu

原文摘要

Safety-critical deep learning systems must be robust against real-world corruptions combining spatially correlated distortions and independent noise.Current deep neural network verification methods handle these perturbations separately, either checking independent pixel-wise perturbations or restricted convolutional transformations using predefined patterns.This gap prevents assessing robustness under realistic conditions where both perturbation types occur simultaneously.To address these limitations, we propose VeriDou, a framework that introduces:(i) universal convolutional perturbations that enable verification across continuous spatial distortion spaces, and(ii) dual perturbations that capture both convolutional distortions and independent pixel-level variations.Our evaluation on a set of diverse benchmarks with 14340 instances shows VeriDou's dual perturbations approach found substantially more adversarial examples on networks that existing methods claimed to be highly robust.This shows that VeriDou is able to explore a broader range of unsafe regions and thus enhances formal assessment of robustness. VeriDou is available at https://github.com/dynaroars/VeriDou.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:0fdac74b5bc6cf988d435ddce4c4dc0eCVF · CVPR 2026

Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification

Nguyen, Nghia, Ding, Tianjiao, Vidal, René

原文摘要

Interpretable-by-design models are gaining traction in computer vision because they provide faithful explanations for their predictions. In image classification, these models typically recover human-interpretable concepts from an image and use them for classification. Sparse concept recovery methods leverage the latent space of vision-language models to represent image embeddings as sparse combinations of concept embeddings. However, by ignoring the hierarchical structure of semantic concepts, these methods may produce correct predictions with explanations that are inconsistent with the hierarchy. In this work, we propose Hierarchical Concept Embedding & Pursuit (HCEP), a framework that induces a hierarchy of concept embeddings in the latent space and performs hierarchical sparse coding to recover the concepts present in an image. Given a hierarchy of semantic concepts, we introduce a geometric construction for the corresponding hierarchy of embeddings. Under the assumption that the true concepts form a rooted path in the hierarchy, we derive sufficient conditions for their recovery in the embedding space. We further show that hierarchical sparse coding reliably recovers hierarchical concept embeddings, whereas standard sparse coding fails. Experiments on real-world datasets show that HCEP improves concept precision and recall compared to existing methods while maintaining competitive classification accuracy. Moreover, when the number of samples available for concept estimation and classifier training is limited, HCEP achieves superior classification accuracy and concept recovery. Our results demonstrate that incorporating hierarchical structure into sparse concept recovery leads to more faithful and interpretable image classification models.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1d2a7595b585f2b852eb69ec239dbf05CVF · CVPR 2026

Consistent Instance Field for Dynamic Scene Understanding

Wu, Junyi, Nguyen, Van Nguyen, Planche, Benjamin, Tao, Jiachen, Sun, Changchang, Gao, Zhongpai, Zhao, Zhenghao, Choudhuri, Anwesa 等 14 位作者

原文摘要

We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution. To realize this, we introduce a novel instance-embedded representation based on deformable 3D Gaussians, which jointly encode radiance and semantic information and are learned directly from input RGB images and instance masks through differentiable rasterization.Furthermore, we introduce new mechanisms to calibrate per-Gaussian identities and resample Gaussians toward semantically active regions, ensuring consistent instance representations across space and time. Experiments on HyperNeRF and Neu3D datasets demonstrate that our method significantly outperforms state-of-the-art methods on novel-view panoptic segmentation and open-vocabulary 4D querying tasks.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:245fce70a9a28a97ed0b75f39d0fc55dCVF · CVPR 2026

Relational Visual Similarity

Nguyen, Thao, Mo, Sicheng, Singh, Krishna Kumar, Wang, Yilin, Shi, Jing, Kolkin, Nicholas, Shechtman, Eli, Lee, Yong Jae 等 9 位作者

原文摘要

Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but the Earth is also like a peach: its crust, mantle, and core correspond to the peach's skin, flesh, and pit. This ability to perceive and recognize relational similarity, is arguable by cognitive scientist to be what distinguishes humans from other species. Yet, all widely used visual similarity metrics today (e.g., LPIPS, CLIP, DINO) focus solely on perceptual attribute similarity and fail to capture the rich, often surprising relational similarities that humans perceive. How can we go beyond the visible content of an image to capture its relational properties? How can we bring images with the same relational logic closer together in representation space? To answer these questions, we first formulate relational image similarity as a measurable problem: two images are relationally similar when their internal relations or functions among visual elements correspond, even if their visual attributes differ. We then curate 114k image-caption dataset in which the captions are anonymized -- describing the underlying relational logic of the scene rather than its surface content. Using this dataset, we finetune a Vision-Language model to measure the relational similarity between images. This model serves as the first step toward connecting images by their underlying relational structure rather than their visible appearance. Our study shows that while relational similarity has a lot of real-world applications, existing image similarity models fail to capture it -- revealing a critical gap in visual computing.

cs.CV · cs.CL

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:37e2ef82746b2e1df1f8acafe3f866d8CVF · CVPR 2026

LS-ViT: Least-Squares Hessian Based Block Reconstruction for Low-Bit Post-Training Quantization of Vision Transformers

Hwang, Hyunha, Nguyen, Xuan Truong, Lee, Hyuk-Jae

原文摘要

Vision Transformers (ViTs) have achieved state-of-the-art results across various vision tasks. To enable a practical deployment of ViTs on modern hardware systems, post-training quantization (PTQ) has been actively studied in recent years. In particular, Hessian-based block reconstruction approaches have demonstrated promising results in quantizing ViT models to ultra-low bitwidths (e.g., 4-bit). However, finding a representative approximate Hessian, a fundamental step in recent approaches such as APHQ-ViT and FIMA-Q, remains underexplored in terms of the quantization-induced error and estimation cost. To address these shortcomings, we first reveal that the sample independence assumption used in recent works, which ignores the covariance term, can lead to a significant approximation error, especially for sub-four-bits. Inspired by least-squares regression, we propose LS-ViT, a block reconstruction framework that effectively estimates a representative Hessian by explicitly minimizing this approximation error across all samples. Extensive experiments with various ViT models across different vision tasks demonstrate that LS-ViT achieves new state-of-the-art performance. In addition, LS-ViT reduces quantization time compared to prior work, enabling a practical, plug-and-play, quantization-aware deployment for ViTs. The source code is available.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3ace07a60db96f2e02b56bec26b54907CVF · CVPR 2026

WorldGen: From Text to Traversable and Interactive 3D Worlds

Wang, Dilin, Jung, Hyunyoung, Monnier, Tom, Sohn, Kihyuk, Zou, Chuhang, Xiang, Xiaoyu, Yeh, Yu-Ying, Liu, Di 等 23 位作者

原文摘要

We introduce WorldGen, a method for generating large, fully formed, navigable 3D worlds from a single text prompt. Existing approaches to 3D scene generation often trade off scene diversity, completeness, and correctness in different ways. We push this envelope by producing large scenes explicitly decomposed into individual, high-quality 3D meshes, making them compatible with standard game engines. Our approach first uses a language-driven procedural generator to lay out the scene's basic volumes and navigable regions. An image generator then establishes the scene's theme, style, and details. Next, we obtain a high-quality, compositional 3D reconstruction of the planned scene. This step first uses an image-to-3D model to perform a holistic reconstruction that implicitly determines the shape and location of all scene objects, accounting for context and navigability. The reconstruction is then decomposed into individual entities, which are regenerated at higher resolution, synthesizing additional details with guidance from the image generator. We ablate key design choices and compare qualitatively against existing scene generators, showing that our design addresses their gaps.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3d81793a5904d6a2bf0d66b7d8aa5256CVF · CVPR 2026

TV2TV: A Unified Framework for Interleaved Language and Video Generation

Han, Xiaochuang, Emad, Youssef, Hall, Melissa, Nguyen, John, Padthe, Karthik, Robbins, Liam, Bar, Amir, Chen, Delong 等 17 位作者

原文摘要

Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about what should happen next. In this paper, we introduce a new class of omni video-text models that integrate ideas from recent LM reasoning advances to address this challenge. More specifically, we present TV2TV, a unified generative modeling framework which decomposes video generation into an interleaved text and video generation process. TV2TV jointly learns language modeling (next-token prediction) and video flow matching (next-frame prediction) using a Mixture-of-Transformers architecture. At inference time, TV2TV decides when to alternate between generating text and video frames, allowing the model to "think in words" about subsequent content before "acting in pixels" to produce frames. This design offloads much of the responsibility for deciding what should happen next to the language modeling tower, enabling improved visual quality and prompt alignment of generated videos. It also enables fine-grained controllability, allowing users to modify the video generation trajectory through text interventions at any point in the process. In controlled experiments on video game data, TV2TV demonstrates substantial improvements in both visual quality (preferred 91% of the time in human evaluations vs. a comparable text-to-video model) and controllability (19 point improvement in fine-grained instruction following accuracy vs. a "think-then-act" approach). TV2TV also scales to natural videos, as we show by augmenting sports videos with interleaved natural language action descriptions using VLMs. Training TV2TV on this corpus yields strong visual quality and prompt alignment, showcasing the model's ability to reason about and generate complex real-world action sequences. Together, these results highlight TV2TV as a promising step toward video generation with open-ended textual reasoning and control.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:4420c943be0bdec38f7b27ee4747f904CVF · CVPR 2026

Anti-I2V: Safeguarding your Photos from Malicious Image-to-video Generation

Vu, Duc, Nguyen, Anh, Tran, Chi, Tran, Anh

原文摘要

Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and text prompts. Recent efforts have focused on adversarial attacks that introduce crafted perturbations to protect images from diffusion-based models. However, most existing approaches target image generation, while relatively few explicitly address image-to-video diffusion models (VDMs), and most primarily focus on UNet-based architectures. Hence, their effectiveness against Diffusion Transformer (DiT) models remains largely under-explored, as these models demonstrate improved feature retention, and stronger temporal consistency due to larger capacity and advanced attention mechanisms. In this work, we introduce Anti-I2V, a novel defense against malicious human image-to-video generation, applicable across diverse diffusion backbones. Instead of restricting noise updates to the RGB space, Anti-I2V operates in both the L*a*b* and frequency domains, improving robustness and concentrating on salient pixels. We then identify the network layers that capture the most distinct semantic features during the denoising process to design appropriate training objectives that maximize degradation of temporal coherence and generation fidelity. Through extensive validation, Anti-I2V demonstrates state-of-the-art defense performance against diverse video diffusion models, offering an effective solution to the problem.

cs.CV

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

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Flowception: Temporally Expansive Flow Matching for Video Generation

Ifriqi, Tariq Berrada, Nguyen, John, Alahari, Karteek, Verbeek, Jakob, Chen, Ricky T. Q.

原文摘要

We present Flowception, a novel non-autoregressive and variable-length video generation framework. Flowception learns a probability path that interleaves discrete frame insertions with continuous frame denoising. Compared to autoregressive methods, Flowception alleviates error accumulation/drift as the frame insertion mechanism during sampling serves as an efficient compression mechanism to handle long-term context. Compared to full-sequence flows, our method reduces FLOPs for training three-fold, while also being more amenable to local attention variants, and allowing to learn the length of videos jointly with their content. Quantitative experimental results show improved FVD and VBench metrics over autoregressive and full-sequence baselines, which is further validated with qualitative results. Finally, by learning to insert and denoise frames in a sequence, Flowception seamlessly integrates different tasks such as image-to-video generation and video interpolation.

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

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FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

Le, Huy Q., Nguyen, Loc X., Qiao, Yu, Kim, Seong Tae, Huh, Eui-Nam, Hong, Choong Seon

原文摘要

Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensitive applications. However, in real-world FL scenarios, clients often hold data from distinct domains, leading to severe domain shift and degraded global model performance. To address this, prototype learning has been emerged as a promising solution, which leverages class-wise feature representations. Yet, existing methods face two key limitations: (1) Existing prototype-based FL methods typically construct a single global prototype per class by aggregating local prototypes from all clients without preserving domain information. (2) Current feature-prototype alignment is domain-agnostic, forcing clients to align with global prototypes regardless of domain origin. To address these challenges, we propose Federated Domain-Aware Prototypes (FedDAP) to construct domain-specific global prototypes by aggregating local client prototypes within the same domain using a similarity-weighted fusion mechanism. These global domain-specific prototypes are then used to guide local training by aligning local features with prototypes from the same domain, while encouraging separation from prototypes of different domains. This dual alignment enhances domain-specific learning at the local level and enables the global model to generalize across diverse domains. Finally, we conduct extensive experiments on three different datasets: DomainNet, Office-10, and PACS to demonstrate the effectiveness of our proposed framework to address the domain shift challenges.

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

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MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding

Su, Yuhao, Choudhuri, Anwesa, Gao, Zhongpai, Planche, Benjamin, Nguyen, Van Nguyen, Zheng, Meng, Shen, Yuhan, Innanje, Arun 等 11 位作者

原文摘要

Large vision-language models struggle with medical video understanding, where spatial precision, temporal reasoning, and clinical semantics are critical. To address this, we first introduce MedVidBench, a large-scale benchmark of 531,850 video-instruction pairs across 8 medical sources spanning video, segment, and frame-level tasks, curated through a rigorous quality assurance pipeline with expert-guided prompting and dual-model validation. While supervised fine-tuning on MedVidBench yields noticeable gains, standard Reinforcement Learning (RL) fails due to imbalanced reward scales across datasets, which destabilizes optimization and leads to training collapse. To overcome this, we introduce MedGRPO, a novel RL framework for balanced multi-dataset training with two key innovations: (1) cross-dataset reward normalization that maps each dataset's median performance to a common reward value, ensuring fair optimization regardless of difficulty, and (2) a medical LLM judge that evaluates caption quality on five clinical dimensions through comparative similarity scoring. Supervised fine-tuning Qwen2.5-VL-7B on MedVidBench substantially outperforms GPT-4.1 and Gemini-2.5-Flash across all tasks, demonstrating MedVidBench's efficacy, while our MedGRPO framework further improves upon the SFT baseline across grounding and captioning tasks. Our work establishes a foundational benchmark and robust training methodology for advancing vision-language models in medical domains.

cs.CV · cs.CL

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

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Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations

Nguyen, Tuan Dung, Ho, Minh Khoi, Chen, Qi, Xie, Yutong, Nguyen, Cam-Tu, Nguyen, Minh Khoi, Nguyen, Dang Huy Pham, van den Hengel, Anton 等 11 位作者

原文摘要

Large vision-language models (LVLMs) achieve strong performance on visual reasoning tasks but remain highly susceptible to hallucination. Existing detection methods predominantly rely on coarse, whole-image measures of how an object token relates to the input image. This global strategy is limited: hallucinated tokens may exhibit weak but widely scattered correlations across many local regions, which aggregate into deceptively high overall relevance, thus evading the current global hallucination detectors. We begin with a simple yet critical observation: a faithful object token must be strongly grounded in a specific image region. Building on this insight, we introduce a patch-level hallucination detection framework that examines fine-grained token-level interactions across model layers. Our analysis uncovers two characteristic signatures of hallucinated tokens: (i) they yield diffuse, non-localized attention patterns, in contrast to the compact, well-focused attention and (ii) they fail to exhibit meaningful semantic alignment with any visual region. Guided by these findings, we develop a lightweight and interpretable detection method that leverages patch-level statistical features, combined with hidden-layer representations. Our approach achieves up to 90% accuracy in token-level hallucination detection, demonstrating the superiority of fine-grained structural analysis for detecting hallucinations.

cs.CV · cs.CL

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

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RI-Mamba: Rotation-Invariant Mamba for Robust Text-to-Shape Retrieval

Nguyen, Khanh, de Silva Edirimuni, Dasith, Hassan, Ghulam Mubashar, Mian, Ajmal

原文摘要

3D assets have rapidly expanded in quantity and diversity due to the growing popularity of virtual reality and gaming. As a result, text-to-shape retrieval has become essential in facilitating intuitive search within large repositories. However, existing methods require canonical poses and support few object categories, limiting their real-world applicability where objects can belong to diverse classes and appear in random orientations. To address this challenge, we propose RI-Mamba, the first rotation-invariant state-space model for point clouds. RI-Mamba defines global and local reference frames to disentangle pose from geometry and uses Hilbert sorting to construct token sequences with meaningful geometric structure while maintaining rotation invariance. We further introduce a novel strategy to compute orientational embeddings and reintegrate them via feature-wise linear modulation, effectively recovering spatial context and enhancing model expressiveness. Our strategy is inherently compatible with state-space models and operates in linear time. To scale up retrieval, we adopt cross-modal contrastive learning with automated triplet generation, allowing training on diverse datasets without manual annotation. Extensive experiments demonstrate RI-Mamba's superior representational capacity and robustness, achieving state-of-the-art performance on the OmniObject3D benchmark across more than 200 object categories under arbitrary orientations. The project page is available at ndkhanh360.github.io/project-rimamba.

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

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Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression

Nguyen, Minh-Duong, Wanasekara, Senura, Nguyen, Le-Tuan, Pham, Quoc-Viet, Yong, Ken-Tye, Tran, Nguyen H., Le, Dung D.

原文摘要

Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regulatory requirements. Despite its potential, progress in FUL has been limited due to several challenges, including the cross-client knowledge inaccessibility and high computational and communication costs. To overcome these challenges, we propose Federated On-server Unlearning (FOUL), a novel framework that comprises two key stages. The learning-to-unlearn stage serves as a preparatory learning phase, during which the model identifies and encodes the key features associated with the forget clients. This stage is communication-efficient and establishes the basis for the subsequent unlearning process. Subsequently, on-server knowledge aggregation phase aims to perform the unlearning process at the server without requiring access to client data, thereby preserving both efficiency and privacy. We introduce a new data setting for FUL, which enables a more transparent and rigorous evaluation of unlearning. To highlight the effectiveness of our approach, we propose a novel evaluation metric termed time-to-forget, which measures how quickly the model achieves optimal unlearning performance. Extensive experiments conducted on three datasets under various unlearning scenarios demonstrate that FOUL outperforms the Retraining in FUL. Moreover, FOUL achieves competitive or superior results with significantly reduced time-to-forget, while maintaining low communication and computation costs.

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

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Edit-aware RAW reconstruction

Punnappurath, Abhijith, Zhao, Luxi, Zhao, Ke, Nguyen, Hue, Grzeszczuk, Radek, Brown, Michael S.

原文摘要

Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater accuracy and flexibility, most edits are performed on the camera's display-referred output (e.g., 8-bit sRGB JPEG) since RAW images are rarely stored. Existing RAW reconstruction methods can recover RAW data from sRGB images, but these approaches are typically optimized for pixel-wise RAW reconstruction fidelity and tend to degrade under diverse rendering styles and editing operations. We introduce a plug-and-play, edit-aware loss function that can be integrated into any existing RAW reconstruction framework to make the recovered RAWs more robust to different rendering styles and edits. Our loss formulation incorporates a modular, differentiable image signal processor (ISP) that simulates realistic photofinishing pipelines with tunable parameters. During training, parameters for each ISP module are randomly sampled from carefully designed distributions that model practical variations in real camera processing. The loss is then computed in sRGB space between ground-truth and reconstructed RAWs rendered through this differentiable ISP. Incorporating our loss improves sRGB reconstruction quality by up to 1.5-2 dB PSNR across various editing conditions. Moreover, when applied to metadata-assisted RAW reconstruction methods, our approach enables fine-tuning for target edits, yielding further gains. Since photographic editing is the primary motivation for RAW reconstruction in consumer imaging, our simple yet effective loss function provides a general mechanism for enhancing edit fidelity and rendering flexibility across existing methods.

cs.CV

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

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InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting

Vu, Duc, Nguyen, Kien, Nguyen, Trong-Tung, Nguyen, Ngan, Nguyen, Phong, Nguyen, Khoi, Pham, Cuong, Tran, Anh

原文摘要

Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text-to-image models offer faster generation, but naively applying them to inpainting yields poor harmonization and artifacts between the background and inpainted region. We trace this cause to random Gaussian noise initialization, which under low function evaluations causes semantic misalignment and reduced fidelity. To overcome this, we propose InverFill, a one-step inversion method tailored for inpainting that injects semantic information from the input masked image into the initial noise, enabling high-fidelity few-step inpainting. Instead of training inpainting models, InverFill leverages few-step text-to-image models in a blended sampling pipeline with semantically aligned noise as input, significantly improving vanilla blended sampling and even matching specialized inpainting models at low NFEs. Moreover, InverFill does not require real-image supervision and only adds minimal inference overhead. Extensive experiments show that InverFill consistently boosts baseline few-step models, improving image quality and text coherence without costly retraining or heavy iterative optimization.

cs.CV

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

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SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Icons

Xu, Haiyang, Wu, Ronghuan, Wei, Li-Yi, Zhao, Nanxuan, Liu, Chenxi, Nguyen, Cuong, Tu, Zhuowen, Wang, Zhaowen

原文摘要

Graphic icons are a cornerstone of modern design workflows, yet they are often distributed as flattened single-path or compound-path graphics, where the original semantic layering is lost. This absence of semantic decomposition hinders downstream tasks such as editing, restyling, and animation. We formalize this problem as semantic layer construction for flattened vector art and introduce SemLayer, a visual generation empowered pipeline that restores editable layered structures. Given an abstract icon, SemLayer first generates a chromatically differentiated representation in which distinct semantic components become visually separable. To recover the complete geometry of each part, including occluded regions, we then perform a semantic completion step that reconstructs coherent object-level shapes. Finally, the recovered parts are assembled into a layered vector representation with inferred occlusion relationships. Extensive qualitative comparisons and quantitative evaluations demonstrate the effectiveness of SemLayer, enabling editing workflows previously inapplicable to flattened vector graphics and establishing semantic layer reconstruction as a practical and valuable task. Project page: https://xxuhaiyang.github.io/SemLayer/

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

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PyraTok: Language-Aligned Pyramidal Tokenizer for Video Understanding and Generation

Susladkar, Onkar, Prakash, Tushar, Juvekar, Adheesh, Nguyen, Kiet A., Jang, Dong-Hwan, Dhillon, Inderjit S, Lourentzou, Ismini

原文摘要

Discrete video VAEs underpin modern text-to-video generation and video understanding systems, yet existing tokenizers typically learn visual codebooks at a single scale with limited vocabularies and shallow language supervision, leading to poor cross-modal alignment and zero-shot transfer. We introduce PyraTok, a language-aligned pyramidal tokenizer that learns semantically structured discrete latents across multiple spatial-temporal resolutions. PyraTok builds on a pretrained video VAE and a novel Language aligned Pyramidal Quantization (LaPQ) module that discretizes encoder features at several depths using a shared large binary codebook, yielding compact yet expressive video token sequences. To tightly couple visual tokens with language, PyraTok jointly optimizes multi-scale text-guided quantization and a global autoregressive objective over the token hierarchy. Across ten benchmarks, PyraTok delivers state-of-the-art (SOTA) video reconstruction, consistently improves text-to-video quality, and sets new SOTA zero-shot performance on video instance segmentation, temporal action localization, and video understanding, scaling robustly to up to 4K/8K resolutions.

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

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Pixel Motion Diffusion is What We Need for Robot Control

Nguyen, E-Ro, Zhang, Yichi, Ranasinghe, Kanchana, Li, Xiang, Ryoo, Michael S.

原文摘要

We present DAWN (Diffusion is All We Need for robot control), a unified diffusion-based framework for language-conditioned robotic manipulation that bridges high-level motion intent and low-level robot action via structured pixel motion representation. In DAWN, both the high-level and low-level controllers are modeled as diffusion processes, yielding a fully trainable, end-to-end system with interpretable intermediate motion abstractions. DAWN achieves state-of-the-art results on the challenging CALVIN benchmark, demonstrating strong multi-task performance, and further validates its effectiveness on MetaWorld. Despite the substantial domain gap between simulation and reality and limited real-world data, we demonstrate reliable real-world transfer with only minimal finetuning, illustrating the practical viability of diffusion-based motion abstractions for robotic control. Our results show the effectiveness of combining diffusion modeling with motion-centric representations as a strong baseline for scalable and robust robot learning.

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

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Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation

Lin, Xin, Song, Meixi, Zhang, Dizhe, Lu, Wenxuan, Li, Haodong, Du, Bo, Yang, Ming-Hsuan, Nguyen, Truong 等 9 位作者

原文摘要

In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: https://insta360-research-team.github.io/DAP_website/

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

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