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新论文 · v1arXiv:2611.17284

增强型混合电视服务：西班牙消费者的习惯、偏好、意见与期望

Augmented Hybrid TV Services. Habits, Preferences, Opinions and Expectations of Spanish Consumers

Fernando Boronat, Bernat Roig, Enric Sigalat, Santiago J. Flores

AI 速读 基于摘要

该研究提出“增强型混合电视服务”概念，并通过西班牙626名用户的调查，评估五个代表性用例的感知有用性与接受意愿。

cs.MM · cs.ET

arXiv ↗PDF ↗
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The convergence of hybrid broadcast and broadband delivery, companion devices, and immersive technologies enables new forms of interactive, personalized, and accessible TV services. This paper introduces the concept of Augmented Hybrid TV services and presents an exploratory user requirements and preliminary acceptance assessment for 5 representative use cases. They were selected through an exploratory stakeholder consultation and 3 co-design workshops with audiovisual professionals and participants from organizations supporting people with visual or hearing impairments. They combine conventional TV with complementary content delivered to connected devices, including AR/VR HMD. They were subsequently evaluated through a cross-sectional CAWI survey of 626 Internet and connected TV users in Spain. Each participant viewed standardized video demonstrations of 2 randomly assigned cases and assessed their perceived usefulness, anticipated benefits/limitations, and interest in them. The results show substantial interest across all five use cases, ranging from 64.7% to 76.9%. Adaptive and personalized augmented subtitles received the highest level of interest and were particularly valued for their accessibility, language-learning, and personalization benefits. Immersive services involving complementary AR content, virtual screens, 3D objects, and 360 video were also evaluated positively, although participants identified device cost, ergonomics, connectivity, usability, and synchronization as anticipated limitations. The findings provide user-centered evidence that can inform the prioritization, design, and subsequent prototype-based evaluation of standards-compliant Augmented Hybrid TV services. Because the study assessed video-based scenarios rather than functional systems, the results represent perceived usefulness and stated interest rather than observed adoption or technically validated QoE.

新论文 · v1arXiv:2611.16382

RADIANT: Redundancy-Aware Information Allocation for Universal Multi-Modal Fusion

Shenghai Wu, Yue Lin

原文摘要

Universal multi-modal fusion often fails not because modalities are insufficiently aligned, but because the fusion process allocates information poorly: redundant evidence is repeatedly amplified, modality-unique evidence is over-smoothed during alignment, and task-dependent synergistic evidence is insufficiently activated. We present RADIANT (Redundancy-Aware Dynamic Information Allocation Network), a task-general framework that organizes fusion around three complementary roles: suppressing redundant cross-modal overlap, preserving modality-unique evidence, and composing task-conditioned synergy through cross-modal interaction. Specifically, stable matrix-exponential projections are used to reduce interference among shared and modality-unique streams, while task memory with hard positive updates selects which synergistic interactions should be activated for the current fusion regime. Extensive experiments across six heterogeneous fusion tasks show consistent improvements over the compared methods. On FLIR, RADIANT improves downstream detection mAP@0.5 from 91.2% to 96.8%, a gain of 5.6 percentage points (6.1% relative) over GIF. RADIANT also exhibits strong direct cross-dataset transfer, with further gains from inference-time task-memory adaptation while keeping all network weights frozen. Additional ablations and sensitivity studies support the robustness of the allocation mechanism.

cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07338

Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Chenying Lin, Ying Peng, Fengyuan Liu, Qianqian Xie, Stavroula Golfomitsou 等 10 位作者

原文摘要

Museum records express how people understand heritage objects through cultural and historical relations. Recovering these human attributions from images is a technical challenge for vision-language models (VLMs). We introduce Appear2Meaning, a cross-cultural benchmark of 750 museum objects for image-only recovery of title, culture, period, origin, and creator. Its evaluation framework distinguishes field-level from complete-record compatibility and uses human validation for semantic judgement. Across nine VLMs, mean field accuracy is 36.16--49.82%; 7.20--17.02% of records have all four contextual fields compatible, and 2.13--7.87% have all five, including title. For the two tested models, image-only input yields 12.6--32.2 percentage points higher reference compatibility for culture, period, and origin than a museum-and-title-only control.

cs.CV · cs.CL · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27378

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

Xiaohan Li, Xinyu Liu, Chang Liu, Kuofeng Hung, Sum Wing Au Yeung, Jun Liu, Yixuan Yuan, Hui Chen

原文摘要

Accurate evaluation of multimodal large language models in dental panoramic radiography is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work introduces PanDent, a large-scale benchmark built on expert-validated tooth-level annotations. It comprises 9,554 panoramic radiographs with comprehensive structured findings produced by experienced dentists and further validated by an oral and maxillofacial radiologist. Clinician-guided reports are constructed from these findings using clinician-defined reporting logic, establishing explicit correspondence between structured clinical evidence and free-text descriptions. The dataset supports complementary evaluation of report-level textual agreement and tooth-level clinical consistency, with 9,049 public-source cases for supervised training and an independent 505-case in-house benchmark. Experiments cover proprietary, general-domain open-source, and medical-specific models. Current models generate fluent reports but exhibit substantial errors in fine-grained localization and tooth-level diagnosis. Supervised fine-tuning on PanDent markedly improves report alignment and diagnostic accuracy, bringing predictions closer to expert dental interpretation. These results establish PanDent as a rigorous benchmark and training resource for clinically grounded dental multimodal reasoning. Dataset sample: https://huggingface.co/datasets/Desperado1103/Pandent_Sample; code: https://github.com/HKUDentistry/PanDent. Full data will be released upon publication under source redistribution terms.

cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.24921

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking

Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo, Eduardo Montijano

原文摘要

Unmanned aerial vehicles (UAVs) have become powerful tools across domains from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet UAVs also raise ethical concerns like the risk of disturbing the animals they aim to capture. This paper introduces CineWild, a model predictive control (MPC) formulation for autonomous aerial cinematography that combines robotics, cinematography, and ethics. Building on an MPC solution that jointly controls drone motion and camera focal length, CineWild dynamically adjusts flight paths and camera settings to balance cinematic quality with animal welfare. Key features include adaptive zoom for filming from acoustic and visual safe distances, path-planning that avoids an animal's field of view, and smooth, low-noise maneuvers. CineWild exemplifies interdisciplinary innovation, bridging engineering, visual storytelling, and environmental ethics. We evaluate the formulation in simulation, with ablation studies and a comparison against a cinematographic baseline and a welfare-aware baseline under identical weights, and we demonstrate the perception and camera-actuation pipeline in real-world proof-of-concept trials. We will release the code.

cs.RO · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.21776

PulseGuard: rPPG-Derived Temporal Signals as a Forensic Modality for Talking-Face Deepfake Detection

Othmane Harraq, Tamer Aldwairi

原文摘要

Talking-face (TF) deepfake generation synthesizes photorealistic facial video from a static source image and an audio signal, producing forgeries that current image-based detectors consistently fail to identify. Unlike face-swap manipulation, TF synthesis has no underlying real video from which to inherit physiological characteristics, motivating remote photoplethysmography (rPPG) as a detection modality for this forgery category. We propose PulseGuard, a detection framework that extracts per-window rPPG waveforms via RhythmFormer and trains a suite of lightweight classifiers to distinguish real from synthesized traces. Evaluated on the TF subset of Celeb-DF++ under a strict subject-independent protocol, where test identities are completely separated from training identities, our 1D ResNet achieves a window-level AUC of 0.826 at EER 25.1% (0.842 pooled to video level, the granularity at which DeepFakesON-Phys is scored) while operating exclusively on the extracted temporal signal, and retains AUC 0.754 on generators withheld entirely from training. We document a controlled reproduction study of DeepFakesON-Phys, the representative prior rPPG detector, demonstrating degradation from AUC 0.999 on legacy face-swap data to 0.576 on the TF subset of Celeb-DF++. We further show that detection difficulty is strongly method-dependent: AUC ranges from 0.937 (Real3DPortrait) to 0.617 (IP-LAP) across the seven TF generators, with the ordering broadly preserved under per-method training. This spread is stable across training regimes rather than reflecting evaluation noise, and constitutes the primary empirical contribution of the work. We report these as empirical baselines and do not establish that the discriminative content is cardiac in origin.

cs.LG · cs.CR · cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14004

LUMIR: Decoupled Multimodal Representation Learning from Heterogeneous Data for Next-POI Recommendation

Zehui Wang, Wolfram Höpken, Dietmar Jannach

原文摘要

As multimodal tourism-related information collected from heterogeneous data sources has become increasingly available, learning effective representations has attracted attention for improving recommendation performance. Existing approaches often tightly couple multimodal fusion with the architecture and optimization objective of a specific downstream model, which limits the generality and reusability of the learned representations. To address these limitations, we propose LUMIR, a decoupled multimodal representation learning framework based on contrastive learning. Multimodal information collected from heterogeneous data sources is first semantically unified into a shared textual representation space using large language models (LLMs). An interaction-aware fusion mechanism is then designed to model cross-modal interactions, while sequential signals are incorporated as a structural constraint to guide representation learning. To evaluate the learned representations, experiments are conducted on a multimodal point-of-interest (POI) dataset under the next-POI recommendation setting across multiple sequential recommendation backbones. The results show that LUMIR consistently outperforms existing multimodal fusion baselines in both accuracy-oriented and beyond-accuracy metrics, demonstrating that decoupling representation learning from downstream optimization provides an effective paradigm for learning reusable multimodal representations. The code is publicly available at https://github.com/wzehui/LUMIR.

cs.IR · cs.LG · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13972

Data Quality Challenges in Multimodal Tourism Recommender Systems

Zehui Wang, Wolfram Höpken, Dietmar Jannach

原文摘要

Modern recommender systems increasingly consider diverse data modalities to enhance model performance. However, the quality of the underlying multimodal data (e.g. data from social media platforms) has received limited attention so far. Focusing on tourism recommendation, we examine real-world challenges encountered when using datasets from Yelp and Instagram. Issues include noise from fake reviews, hallucinated content from LLMs, modality contradictions, and demographic bias. We outline future directions such as quality-aware fusion, structured and controlled LLM-based content generation to reduce hallucinations, and user group differentiation to improve the robustness and reliability of multimodal recommendation systems.

cs.IR · cs.LG · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13707

Joint Activation and Weight Quantization for Learned Image Compression

Yile Tu, Heming Sun

原文摘要

Learned image compression (LIC) models achieve competitive rate-distortion performance with increasingly large networks, making network quantization important for efficient deployment. Directly quantizing LIC models remains difficult in post-training quantization (PTQ) because compression performance is highly sensitive to quantization noise. Existing methods typically focus on optimizing either the weights or the activations, resulting in substantial rate-distortion degradation. To address this limitation, we introduce a two-stage pipeline that uses SmoothQuant to balance activation and weight ranges, followed by GPTQ to adapt the transformed weights. The proposed joint search framework optimizes the balancing transformation and adapted weights under the rate-distortion objective, accounting for the coupling between the two stages. Experiments across multiple LIC architectures and activation quantization configurations show that the joint search strategy consistently reduces rate-distortion degradation after quantization.

eess.IV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.28063

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

Yuyue Wang, Xihua Wang, Xin Cheng, Yijing Chen, Ruihua Song

原文摘要

Generating unified audio that naturally integrates speech and sounds from free-form text prompts remains challenging. For example, the prompt ``A fast drum roll ends with a cymbal crash, after which a man shouts: `Come on!'.'' describes several events that should appear in order and form a coherent recording rather than independent tracks that lack coherent temporal and semantic interactions. Current methods broadly follow two paradigms: pipeline methods couple separate text-to-sound and text-to-speech models, while unified models rely on a single generator that still requires structured, multi-field prompt inputs. Both paradigms require upstream prompt decomposition or rewriting, limiting free-form flexibility and risking cascading errors. To enable direct generation from free-form prompts within a single model, we introduce PlanAudio, an end-to-end framework that maps unconstrained free-form text to unified audio without intermediate prompt preprocessing. To address the challenges of direct free-form generation, PlanAudio integrates latent planning into a single autoregressive framework, predicting continuous audio layout before acoustic token synthesis. We construct a large-scale training corpus and introduce Free-Form Unified Audio Generation Benchmark (FUAG) for evaluation, both spanning speech, sound, and joint speech-and-sound generation. Experiments show that a single PlanAudio outperforms existing pipeline and unified baselines on speech-and-sound generation, while remaining competitive with specialist models on speech and sound generation. Further analysis validates the effectiveness of audio layout planning over textual planning, acoustic planning, and direct generation. Sample Available: https://planaudio.github.io/PlanAudio2026/.

cs.SD · cs.AI · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13544

DASWeave: Weaving Evidence across Speaking Conditions for Multimodal Depression, Anxiety, and Stress Screening

Binyan Chen, Tong Pan, Yadong Liu, Xin Yun, Shangfei Wang

原文摘要

Multimodal screening for depression, anxiety, and stress in children and adolescents uses verbal and nonverbal behaviour. Most existing multimodal methods learn from a single recording or interview. Yet a participant's behaviour changes with the speaking condition, and these changes hold evidence that is invisible in a single recording. We propose DASWeave to bring this evidence into a neural network that encodes each response and then combines all responses of a participant. We train supervised trees to find which behavioural statistics matter for screening, both within each response and in how they change across conditions. We feed the tree outputs into the network both before and after this combination. We fine-tune a large language model to relate what a participant says to how they say it across conditions, and feed its representation into the network after the combination. The language model reads behaviour as text descriptions. We therefore derive additional training targets for the network from a multimodal large language model that takes in the audio-visual features directly. On the official hidden test set of the ACM Multimedia 2026 AdoDAS Grand Challenge (Track 1), DASWeave ranks second among 95 eligible teams. Its mean F1 is 27.1% higher than that of the official audio-visual baseline. It also has the highest mean AUROC among the top five teams. Ablation studies on the validation set show that the supervised trees and the fine-tuned language model contribute most to mean F1.

cs.MM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.19019

Smiling Regulates Emotion During Traumatic Recollection

Marcus Ma, Emily Zhou, Leonard Ludwig, Julia Hörath, Christina Winkler, Kleanthis Avramidis, Tiantian Feng, Gabor Mihaly Toth 等 10 位作者

原文摘要

We study when, where, and why 978 Holocaust survivors smile in video testimonies. We create an automatic smile detection model from facial features with an F1 of 85% and annotate detected smiles under two established taxonomies of smiling. We produce narrative features on 1,083,417 transcript sentences as well as emotional valence from three different modalities: audio, eye gaze, and text transcript. Smiling rates are associated with specific semantic topics, narrative structures, and temporal syntaxes across the corpus. Smiles often occur during periods of intense negative affect and we find negative-affect smiles are associated with more positive subsequent valence trajectories across all three modalities. Smiling reduces eye dynamics and blink rates, with both of these effects modulated by narrative valence. Taken together, we conclude that smiling plays a critical role in regulating emotion and social interaction during traumatic recollection.

cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13310

ABRR: Transforming Temporal Sentence Grounding with Wasserstein-Adaptive Refinement and Precision Ranking

Xiang Fang, Wanlong Fang, Junhao Dong, Daizong Liu

原文摘要

Temporal Sentence Grounding (TSG) aims to localize temporal segments in untrimmed videos that correspond to natural language queries, a critical task for video understanding with applications in video retrieval, summarization, and human-computer interaction. Despite significant progress driven by transformer-based models, existing TSG methods suffer from two key limitations: quantization errors due to temporal downsampling, which misalign segment boundaries, and suboptimal ranking of predictions based on semantic matching rather than localization precision. To address these challenges, we propose the Adaptive Boundary Refinement and Ranking (ABRR) framework, a novel, model-agnostic post-processing approach that enhances the performance of any TSG model without retraining. ABRR integrates three innovative components: (1) Temporal Distribution Smoothing, which mitigates noise in predicted score distributions using Gaussian kernel convolution; (2) Boundary Refinement via Optimal Transport, which leverages the 2-Wasserstein distance and Sinkhorn iterations to align coarse segment boundaries with true temporal moments, grounded in Monge-Kantorovich optimal transport theory; and (3) Quality-Driven Re-Ranking, which reorders predictions using a composite score balancing localization precision and semantic relevance. By correcting quantization errors and prioritizing accurately localized segments, ABRR achieves substantial improvements over state-of-the-art TSG models. Extensive experiments on benchmark datasets, including Charades-STA, ActivityNet Captions, and QVHighlights, demonstrate consistent gains at stringent IoU thresholds (e.g., IoU=0.7), alongside enhanced ranking performance. The framework's computational efficiency and compatibility with existing models make it a versatile and impactful solution for real-world TSG applications.

cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13178

Multimodal Judges Flag, Humans Decide: A Review-and-Edit Framework for AI-Generated Product Video Ads

Anoubhav Agarwaal, Sukesh Perla, Karthikeyan Mohanraj, Susmita Ghose

原文摘要

Generative pipelines can now turn every product page of an e-commerce catalog into a short video ad, but deciding whether such an ad is usable is not one good-or-bad call. We describe how we operationalized the review and editing of these ads so that both can keep pace with generation: reviewers go through each ad scene by scene and answer six questions, on the product, scene quality, pronunciation, script, sound and captions; judges built from vision-language, speech and language models file each finding under the matching question, and reviewers accept or reject each finding and add what the judges missed. Each confirmed issue then opens an edit tool matched to it. The questions and the routing from a finding to its fix do not depend on the models that generate the ad. Across 120 ads from our pipeline, reviewed with judge findings on screen, reviewers marked the product wrong on 60% of scenes, and script, caption or sound problems on under 10% each. On 60 of these ads, they marked the product wrong on 58% of scenes, far more than the 26% marked by two reviewers who later saw the ads with the judges hidden; one reviewer who saw the ads both ways marked 57% with judge findings and 19% without. On 120 blind-labeled scenes, a product judge that watches the clip with the catalog photos reached AUC 0.84, while video-quality and photo-similarity scores were close to chance. We tune the judges for recall: the product judge catches nearly all defects but also flags many clean scenes, so reviewers decide; together, the judges widen what reviewers mark and reviewers add what the judges miss. Without judge findings, one reviewer's marking faded over a session. We release a review-portal template and our review data (120 ads and 562 scenes with judge findings and human marks, plus 120 blind-labeled scenes with baseline scores) at https://github.com/anoubhav/ad-review-and-edit.

cs.CV · cs.HC · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.09541

Memory-Efficient Active Perception for Embodied Question Answering in Dynamic Human-Populated Scenes

Xin Lu, Rui Li, Xun Huang, Weixin Li, Jiayuan Li, Chuanqing Zhuang, Zhengda Lu, Jun Xiao

原文摘要

Embodied Question Answering (EQA) couples active visual perception, vision-language reasoning, and sequential decision making, and has traditionally been evaluated in temporally stable environments where visual evidence can be accumulated reliably. In realistic multimedia settings such as dynamic human-populated scenes, however, moving people and transient occlusions introduce human-induced perceptual variations: task-relevant multimodal cues become transient, viewpoint-dependent, and incompletely captured in individual views. Conventional store-then-retrieve strategies generate redundant visual evidence and incur high inference cost, making them ill-suited to such variations. This exposes two core problems for embodied agents: resolving viewpoint-dependent ambiguity, and maintaining compact evidence for efficient inference. To address these challenges, we present DIVRR (Dynamic-Informed View Refinement with Relevance-guided Rotation and adaptive memory selection), a training-free active evidence-acquisition framework for EQA. DIVRR combines question-conditioned room guidance with relevance-triggered view augmentation to guide exploration and additional observation. Selective memory admission uses the same relevance signal as view augmentation to retain high-scoring evidence, reducing stored entries while keeping the retrieved visual context compact. To evaluate EQA under human-induced perceptual variations, we introduce DynHuman-EQA, a dynamic human-populated EQA dataset constructed in Habitat, comprising dynamic and static subsets under shared scene layouts. Experiments on DynHuman-EQA and HM-EQA show that DIVRR outperforms existing baselines in both accuracy and memory efficiency. The gains are most pronounced in dynamic human-populated scenes.

cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13029

Artifact Filtering versus Artifact-Compensated Training in JPEG to JPEG AI Recompression

Igor Podtsepko, Yuriy Kim, Karam Mahfod, Evgeny Belyaev, Kristian Kirs, Jianping Lin, Ilya Papiev

原文摘要

In this paper, we consider the problem of recompression of images from JPEG to JPEG AI format. We show that direct recompression from JPEG to JPEG~AI trained on uncompressed images is accompanied by the preservation of the JPEG image artifacts in the resulting JPEG~AI image. In order to avoid it, we evaluate an approach where an artifacts removal neural network is first applied to the input JPEG image as an external pre-filter. However, in this case, the processing time is increased by 29 times. Therefore, we propose an artifact-compensated training for JPEG AI and show that it is able to combine pre-filtering against target distortions with efficient compression without increase in the processing time.

eess.IV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12061

BARS: Beat-Adaptive Rap Synthesis via Flow Matching

Taemin Cho, Hector Martel, Enyan Koh

原文摘要

We present BARS (Beat-Adaptive Rap Synthesis), a non-autoregressive rapping voice generation model based on Flow Matching, conditioned on a beat track (instrumental backing), lyrics, and a short reference voice for zero-shot timbre control. Unlike prior autoregressive rap synthesis, where rap pace and output duration are strictly determined by the beat, BARS enables independent control over the rap tempo, allowing flexible-length generation without explicit duration prediction or phoneme-level alignment. We encode lyrics using a pretrained ByT5 encoder with a lightweight Transformer adapter trained jointly with the model, without relying on speech-pretrained text encoders, enabling natural handling of non-standard orthography (e.g., slang, abbreviations) and numerals common in rap lyrics. To address alignment instabilities, such as token skipping or repetition, under strong beat conditioning, we propose Asymmetric Scaled RoPE for cross-attention, which introduces a positional structure to facilitate sequential lyric-audio alignment learning. Furthermore, the model supports partial re-generation via span masking, enabling lyric-level editing while preserving the surrounding vocal style. Experiments demonstrate that the proposed model achieves high lyric fidelity, natural rap flow that is rhythmically coherent with the beat, and robust tempo control across a wide range of rapping styles and speeds.

cs.SD · cs.LG · cs.MM · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13002

YTShorts-92K: An Interpretable Multimodal Dataset of YouTube Shorts

Celian Charrin, Sidonie Abuzakuk, Artúr Vlaszov, Nándor Birher, Roland Molontay

原文摘要

Short-form video has become one of the dominant formats of online content, yet large-scale datasets enabling the analysis of what makes these videos successful remain limited. Existing resources often focus on post-publication popularity signals or rely on non-interpretable representations such as high-dimensional embeddings. Here, we introduce YTShorts-92K, a dataset of 92,654 YouTube Shorts collected from 345 creators and spanning 12 manually defined content categories. Each video is enriched with an interpretable multimodal feature set covering metadata, textual information, visual characteristics from frames and thumbnails, and cross-modal consistency measures. We describe the collection methodology, data cleaning process, and feature extraction pipeline, followed by descriptive analyses of the diversity of creators, topics, and extracted features. This dataset offers a reusable resource for studying short-form video content, online attention, and multimodal content analysis.

cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11438

When Coding Agents Get Arms: Safety Risks Across the Digital-Physical Boundary

Yiming Sun, Mi Zhang

原文摘要

Advances in spatial understanding have enabled frontier models to power coding agents that control a real robot arm zero-shot and end-to-end, without task-specific demonstrations or retraining. This brings two new safety challenges. First, the extensive knowledge of frontier models lets these agents perform tasks that earlier VLA and WAM paradigms could not, opening the door to new forms of capability misuse. Second, coding agents can access digital resources such as web pages and emails. Threats in these resources, such as indirect prompt injection, can carry over into the physical world when the agent controls a robot. We therefore introduce ArmedAgent, the first systematic benchmark to evaluate both risks for coding agents performing zero-shot robot control. It covers robustness, malicious use, and indirect injection across 36 tasks in 5 scenarios, evaluated in simulation and validated on a physical robot arm. A distinctive portion of the tasks target harmful behaviors that earlier VLA paradigms could not perform. Testing several frontier models, including GPT-6-Astra, under widely used agent frameworks, we find substantial safety risks in current systems.

cs.RO · cs.CR · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12997

TIB: Sample-wise Tempered Information Bottleneck for Multimodal Attribution beyond Alignment Assumption

Qiming Huang, Peixi Liu, Jianbo Jiao

原文摘要

Multimodal (e.g. vision-language, in this work) attribution aims to interpret the models without access to ground-truth explanatory supervision. Existing multimodal attribution methods typically rely on paired modalities as proxy supervision, implicitly assuming that image-text pairs are semantically aligned. Information bottleneck (IB)-based attribution methods follow this general paradigm and instantiate it through a global sufficiency-compression objective. In realistic multimodal datasets, however, this assumption is frequently violated due to partial semantic misalignment and unreliable cross-modal correspondences. Through theoretical analysis, we show that, under such misalignment, information-bottleneck-based attribution objectives exhibit a structural limitation: by assuming equal reliability across all proxy supervision signals, they induce posterior over-contraction and forced explanations. To address this issue, we propose TIB, a sample-wise Tempered Information Bottleneck framework that adaptively modulates attribution strength according to cross-modal reliability, without assuming semantic alignment. Extensive experiments on controlled benchmarks and large-scale datasets show the effectiveness of the proposed approach, while also validating the identified failure mode.

cs.CV · cs.AI · cs.MM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.25097

PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

Ruoran Xu, Wending Gao, Liyunfeng Chen, Aixin Shi, Haoyu Cheng, Zixiang Fang, Yiqiang Zou, Qiufeng Wang

原文摘要

Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. Our datasets are released at https://github.com/PremiLab-Math/PhysElite.

cs.AI · cs.MM · math-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.10648

JND-Guided Light-Weight Neural Pre-Filter for Perceptual Image Coding

Yangyang Wu, Chenlong He, Yuanheng Chen, Yibo Fan

原文摘要

Just Noticeable Distortion (JND)-guided pre-filter is a promising technique for improving the perceptual compression efficiency of image coding. However, existing methods are often computationally expensive, and the field lacks standardized benchmarks for fair comparison. To address these challenges, this paper introduces a twofold contribution. First, we develop and open-source FJNDF-Pytorch, a unified benchmark for frequency-domain JND-Guided pre-filters. Second, leveraging this platform, we propose a complete learning framework for a novel, lightweight Convolutional Neural Network (CNN). Experimental results demonstrate that our proposed method achieves state-of-the-art compression efficiency, consistently outperforming competitors across multiple datasets and encoders. In terms of computational cost, our model is exceptionally lightweight, requiring only 7.15 GFLOPs to process a 1080p image, which is merely 14.1% of the cost of recent lightweight network. Our work presents a robust, state-of-the-art solution that excels in both performance and efficiency, supported by a reproducible research platform. The open-source implementation is available at https://github.com/viplab-fudan/FJNDF-Pytorch.

eess.IV · cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.07061

Do Joint Audio-Video Generation Models Understand Physics?

Zijun Cui, Xiulong Liu, Hao Fang, Mingwei Xu, Jiageng Liu, Zexin Xu, Weiguo Pian, Shijian Deng 等 11 位作者

原文摘要

Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate plausible sounds and frames that violate real-world consistency? We introduce AV-Phys Bench, a benchmark for evaluating physical commonsense in joint audio-video generation. AV-Phys Bench tests models across three scene categories: Steady State, Event Transition, and Environment Transition. It covers physics-grounded subcategories drawn from real-world scenes, plus Anti-AV-Physics prompts that deliberately request physically inconsistent audio-video behavior. Each generation is evaluated along five dimensions: visual semantic adherence, audio semantic adherence, visual physical commonsense, audio physical commonsense, and cross-modal physical commonsense. Across three proprietary and four open-source models, we find that Seedance 2.0 performs best overall, but all models remain far from robust physical understanding. Performance drops sharply on event-driven and environment-driven transitions, and even strong proprietary systems collapse on Anti-AV-Physics prompts. We further introduce AV-Phys Agent, a ReAct-style evaluator that combines a multimodal language model with deterministic acoustic measurement tools, producing rankings that closely align with human ratings. Our results identify cross-modal physical consistency and transition-driven scene dynamics as key open challenges for joint audio-video generation.

cs.SD · cs.AI · cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.28685

Data Diversity, Not Frequency Invariance: A Controlled and Self-Audited Study of Compression-Robust Deepfake Detection

Abbas Aliyev, Samir Rustamov

原文摘要

Frequency features and compression-invariant representation learning are widely assumed to be key to deepfake detection that survives video compression. We test this with CAFRL - block-DCT and FFT-phase streams, compression-level-conditioned band attention, and adversarial (gradient-reversal) compression invariance - and report a controlled negative. Under a pre-registered protocol with capacity- and augmentation-matched controls, a plain EfficientNet-B0 on multi-quality data beat CAFRL as specified at every compression level on the FaceForensics++ test split, by 3.66 AUC points at CRF 40 (paired, single seed). A self-audit of our own negative found four defects biased against the frequency hypothesis, and pre-specified re-tests repairing all four showed the deficit to be a recipe artifact, not an architecture failure: the baseline recipe recovered 3.96 points over the matching shipped-recipe variant. The frequency path made no detectable difference: discriminative alone (standalone validation AUC 0.91-0.98 late in training) but of no marginal value under this fusion, at two feature widths of one 4.0 M trunk, every seed-pooled interval for the intra-dataset compression contrasts including zero; on the single held-out manipulation tested, the fair variants sat below the plain backbone. The adversarial branch, as specified, added nothing and degraded its own conditioning estimator; at the fair recipe it is untested. Robustness under single-pass H.264 re-encoding came instead from data diversity: real constant-rate-factor variants beat synthetic JPEG augmentation by 7.3 points (single runs, non-overlapping intervals). The evidence is FaceForensics++-family, GAN-era and single-codec. Match controls on training recipe as well as capacity, and buy compression robustness with codec diversity before architecture.

cs.CV · cs.LG · cs.MM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.28212

MuSP-Bench: Advanced Multimodal Benchmarking of Music Understanding across Score and Performance

Milan Liessens Dujardin, Song-Ze Yu, Kevin Miao

原文摘要

Musicians commonly communicate musical intent through scores and performances. To investigate whether models can reason across both modalities, we introduce MuSP-Bench, a human-authored benchmark of 490 questions assessing understanding across Musical Scores and Performances, both individually and in combination. The benchmark spans interpretive and long-horizon reasoning across classical piano and orchestral works, represented through ABC notation, score images, MIDI, and audio. Our evaluation of frontier multimodal large language models reveals substantial difficulties in understanding musical scores and even greater challenges in reasoning about audio.

cs.MM · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10599

The RenAIssance: Caption-Guided Diffusion Models for Historical-Art Visual Media

Zhanyu Tuo

原文摘要

Generative AI can expand cultural collections into prompt-responsive visual media, but historical artworks rarely provide the paired descriptions needed to adapt modern image generators. We present The RenAIssance, a scalable pipeline that turns a collection of historical European artworks into a reusable text-to-image media resource. The pipeline generates scene-level captions with BLIP and uses the resulting approximately 15,000 image--text pairs from DEArt to train a compact LoRA adapter for Stable Diffusion v1.5. The adapter shifts generated images toward the historical-art distribution while retaining the base model's ability to render novel concepts. On historical and contemporary prompts of different lengths, it improves FID to the DEArt reference distribution in every setting with only a modest decrease in CLIP text--image similarity. Qualitative results show that the same adapter can render both period-appropriate subjects and contemporary concepts in a coherent historical visual language. By decoupling a reusable style representation from the base generator, the approach offers a practical building block for controllable historical-art media creation, interactive authoring, and rich-media experiences.

cs.CV · cs.AI · cs.LG · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.26111

Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation

Shuhong Zheng, Aashish Kumar Misraa, Yu-Teng Li, Yu-Jhe Li, Igor Gilitschenski

原文摘要

Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode text and reference images separately. This limits cross-modal reasoning abilities and causes copy-paste artifacts. Recent frameworks that connect multimodal models and diffusion models improve instruction following, but largely overlook identity preservation. To address these limitations, we condition diffusion models on Multimodal Large Language Models (MLLMs) that jointly encode text and reference images, and augment it with VAE-based identity conditioning. A novel Dual Layer Aggregation (DLA) module is designed to aggregate multi-level MLLM features for optimal conditioning, and a multi-stage denoising strategy is applied to progressively balance the semantic information from MLLM and fine-detail identity from VAE during inference. Extensive experiments demonstrate that our approach harmonizes multimodal understanding with identity preservation, mitigates copy-paste issues, and achieves superior performance regarding human preference on subject-driven image generation. Our project website is available at https://zsh2000.github.io/squeeze-mllm-subject-gen/.

cs.CV · cs.AI · cs.GR · cs.LG · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21613

To Think or Not to Think: Pre-Decisional Reasoning Budgets for Referring Audio-Visual Segmentation

Haojie Hu, Senda Chen, Ying Shen, Lin Zhang

原文摘要

Multimodal reasoning systems increasingly assume that longer chain-of-thought (CoT) uniformly improves downstream grounding. We show that this assumption fails in referring audio-visual segmentation (Ref-AVS): simple queries can be harmed by excessive reasoning, creating an "overthinking trap" associated with phrase and localization drift, while genuinely ambiguous queries still benefit from multi-step reasoning. To systematically study this, we construct counterfactual reasoning-budget labels under a five-fold out-of-fold evaluation protocol. Our results recast adaptive Ref-AVS reasoning as a pre-decisional representation problem: before the first generated token, the generation-onset state contains linearly decodable information predictive of whether additional reasoning will improve segmentation. A linear probe predicts binary reasoning need with 70.1% accuracy, substantially outperforming text-only features and uncertainty-based proxy signals. As a router, the onset-state controller improves over downstream confidence routing and closely matches Short-then-Decide while using far fewer tokens. Proxy comparisons show that the onset representation contains predictive information not fully recovered by the evaluated hand-crafted features. Reusing this onset state for lightweight, single forward-pass routing reduces autoregressive token consumption by 60% while retaining about 96% of always-long performance. It further provides a cost-quality trade-off on $R^2$-AVSBench without target-label tuning, rather than an accuracy improvement over always-long. Error analysis links harmful long reasoning to phrase and localization drift, illustrating the benefit of bypassing unnecessary reasoning on simple queries. Ultimately, our results support efficient adaptive reasoning in Ref-AVS through pre-decisional internal representations.

cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09140

SPDMX: Large-Scale Open Stem Audio From Symbolic Music

Phillip Long, Eduardo Escoto, Julian McAuley, Zachary Novack

原文摘要

Large public datasets have driven recent progress in machine learning, from text and vision to speech and general audio. Music remains an outlier, as copyright blocks redistribution of training audio, while many modern tasks need individual stems rather than mixtures. Existing multitrack resources trade off realism, scale, and licensing, while synthetic alternatives rely on proprietary VSTs or naive open rendering that yields weak stems. We introduce SPDMX, a listening-guided rendering of public-domain PDMX scores into an openly-licensed stem corpus with aligned scores and an open render pipeline: $253{,}982$ songs, $490{,}159$ mono stems, and $5{,}469$ song-hours at $44.1$ kHz under CC BY 4.0, with paired MIDI and identifiers linking to PDMX metadata. $88{,}403$ songs ($34.8\%$; $2{,}734$ song-hours) provide multitrack supervision. To our knowledge, this is the largest openly-licensed synthetic multitrack subset by song-hours, enabling source separation, stem-conditioned synthesis, MIDI-to-audio, transcription, and joint symbolic-audio modeling. We release the corpus, metadata, and open pipeline for reuse on other symbolic collections. Demos and experimental notes are on the project page.

cs.SD · cs.AI · cs.LG · cs.MM · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08996

UniMore: Fine-Grained Supervision for Unified Understanding and Generation

Haoyue Bai, Yinyi Luo, Yicheng He, Qingsong Wen, Sharon Li, Jindong Wang

原文摘要

Unified multimodal models combine image understanding and generation within one system, creating an opportunity to improve both capabilities through paired image--text supervision. We examine the outputs of a pretrained unified model in both directions and observe a loss of specificity: generated images can contain the requested objects with incorrect attributes, while captions identify a scene without expressing its distinguishing details. These failures call for supervision that specifies both the visual content to realize and the descriptive differences to distinguish. We propose UniMore, a post-training method that derives these complementary learning tasks from the fine-grained content of image--text pairs. The generation route predicts grounded crops from their corresponding phrases, matching the scope of the condition and visual target while using the native visual objective. The understanding route combines positive-caption fitting with ranking against captions that omit or alter details, conditioned on the full image. The two routes are optimized independently, and their learned updates are combined into a single unified model without changing its inference procedure. Experiments show gains across multiple understanding and generation benchmarks.

cs.MM · cs.CV

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arXiv ↗PDF ↗