Screen sharing is a common feature in video conferencing applications, especially for remote work and presentations. However, internet conditions such as limited bandwidth, packet loss, and compression can significantly reduce the visual quality of shared screen content. Most existing video quality metrics are designed for natural scenes and were not benchmarked on screen content. In this work, we present a large-scale subjective dataset of screen content videos captured from video conferencing apps. The dataset includes 1,600 distorted videos with corresponding subjective quality scores. Subjective scores were collected using crowdsourced pairwise comparisons. The dataset provides a valuable resource for developing and benchmarking video quality metrics tailored to screen content. The evaluation of objective metrics revealed that several general-purpose quality metrics outperform both full-reference and no-reference metrics. The dataset is available at the following link: https://videoprocessing.github.io/screen-content-dataset.
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The field of video generation has witnessed remarkable advances in recent years, driven by innovations in deep generative models. Nevertheless, the fidelity of AI-generated videos remains far from perfect, with synthesized content frequently exhibiting visual artifacts, such as temporally inconsistent motion, physically implausible trajectories, unnatural object deformations, and local blurring, that undermine realism and user trust. Precise detection and spatial localization of these artifacts are of critical importance: not only are they essential for automatic quality control pipelines that improves user experience, but they also provide actionable diagnostic signals for researchers and practitioners to guide model development and evaluation. Despite its significance, the research community currently lacks a comprehensive benchmark tailored for artifact localization in AI-generated videos. Existing datasets either focus solely on detection at the video or frame level, or lack fine-grained spatial annotations necessary for developing and benchmarking localization methods. To fill this gap, we present BrokenVideos, a benchmark dataset comprising ~3,254 AI-generated videos with carefully-annotated, pixel-level masks indicating regions of visual corruption. Each annotation is the result of careful human inspection, ensuring high-quality ground truth for artifact localization tasks. We demonstrate that training existing video artifact detection models and multi-modal large language models (MLLMs) on BrokenVideos substantially enhances their ability to localize corrupted regions within generated content. Through extensive experiments and cross-model evaluations, we show that BrokenVideos provides a critical foundation for both benchmarking and advancing artifact localization research. We hope our dataset can catalyze further innovation in both video generation and its quality assurance. The dataset is available at: https://broken-video-detection-datetsets.github.io/Broken-Video-Detection-Datasets.github.io/.
Emerging text-to-3D generative models based on large diffusion backbones have markedly lowered the barrier to high-quality 3D asset creation, yet rigorous quantitative evaluation remains elusive. We introduce T23D-QA, a novel open benchmark that couples diverse prompts, multiple generation paradigms, and fine-grained human judgements for text-conditioned 3D synthesis. The dataset comprises 1,710 textured meshes produced by nine state-of-the-art pipelines spanning feed-forward, optimization-based, and view-reconstruction families. Assets are driven by a tri-categorical prompt suite: single-object, multi-object, and primitive-anchored, covering 160 ShapeNet-level object classes. Each mesh is rated by 20 participants along three orthogonal dimensions: geometry, texture, and alignment. Building upon this corpus, we propose an evaluator that decouples multi-modal features via cross-attention. On T23D-QA, our baseline surpasses the strongest published metric by 8.1% (geometry), 6.1% (texture), and 1.9% (alignment) in Spearman rank correlation. Dataset and code are publicly available at https://t23d-qa.github.io to foster reproducible research.
The increasing demand for large-scale, high-quality datasets in dynamic point cloud compression (PCC) and human visual perception research underscores the limitations of existing datasets, which are often constrained by limited scale and insufficient dynamism, hindering algorithm validation and perceptual analysis in complex scenarios. To address this gap, we present DPCSet, a comprehensive dynamic point cloud dataset designed to support advanced research in PCC, human perception, and related domains. Comprising 100 dynamic object point clouds-the largest collection of its kind-DPCSet includes 200-frame sequences with geometry and attribute information, capturing diverse object types across real and virtual environments. Organized into seven superclasses, the dataset ensures broad scenario coverage. By rigorous selection, format conversion, quantization, DPCSet delivers standardized, high-precision point cloud data. Evaluation of multiple compression algorithms on a curated subset demonstrates DPCSet's efficacy in assessing trade-offs between compression efficiency and quality loss, positioning it as a potential benchmark for PCC. Furthermore, just noticeable distortion (JND) experiments on a compression-distorted subset reveal distinct perceptual characteristics of dynamic point clouds, offering valuable insights for perception-driven compression algorithms. The dataset is released at https://openi.pcl.ac.cn/gaowx/DPCSet.
Human-centric videos play a significant role in the pervasive video content of modern life. However, the capabilities of text-to-video (T2V) generation models and video-to-text (V2T) understanding models for human-centric videos remain largely unexplored. To this end, we present HVEval, the first comprehensive evaluation dataset focusing on human-centric videos, which consists of 20,000 videos, 60k MOS annotations across 3 dimensions (i.e., spatial quality, temporal quality, and text-video correspondence), and 20k category-specific Q&A pairs. Based on the HVEval dataset, this paper aims to answer three questions: (1) can today's T2V models effectively generate human-centric videos following the given prompts? (2) how effective are today's V2T LMMs in understanding and evaluating human-centric videos? (3) are current VQA metrics good enough for evaluating human-centric videos? Comprehensive evaluations of 24 T2V models, 20 LMMs, and 18 VQA metrics reveal their limitations in fine-grained text-controlled generation and human-aligned perception and understanding, highlighting the significant potential of our dataset and benchmarks to advance research in human-centric video generation and understanding.
Progress in understanding real-world canine vocal communication is constrained by datasets lacking scale and 'in-the-wild' diversity. We introduce DogSpeak, a large-scale public dataset of 77,202 Barkseqs (33.162 hours) from 156 dogs (5 breeds), uniquely sourced from online social media with accurate dog ID, sex, and breed labels. DogSpeak, one of the largest of its kind, addresses prior limitations. Benchmark tasks (sex, breed, individual dog recognition) demonstrate its utility and highlight how its inherent real-world challenges necessitate and foster research into more robust bioacoustic models, preprocessing, and feature representation.
Text removal is a crucial task in computer vision with applications such as privacy preservation, image editing, and media reuse. While existing research has primarily focused on scene text removal in natural images, limitations in current datasets hinder out-of-domain generalization or accurate evaluation. In particular, widely used benchmarks such as SCUT-EnsText suffer from ground truth artifacts due to manual editing, overly simplistic text backgrounds, and evaluation metrics that do not capture the quality of generated results. To address these issues, we introduce an approach to synthesizing a text removal benchmark applicable to domains other than scene texts. Our dataset features text rendered on complex backgrounds using object-aware placement and vision-language model-generated content, ensuring clean ground truth and challenging text removal scenarios. The dataset is available at https://huggingface.co/datasets/cyberagent/OTR.
Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the detection of hyper-realistic and semantically nuanced AI-generated videos. AEGIS comprises over 10,000 rigorously curated real and synthetic videos generated by diverse, state-of-the-art generative models, including Stable Video Diffusion, CogVideoX-5B, KLing, and Sora, encompassing open-source and proprietary architectures. In particular, AEGIS features specially constructed challenging subsets enhanced with GPT-4o-refined prompts, creating unprecedentedly realistic scenarios for rigorous robustness evaluation. Furthermore, we provide multimodal annotations spanning Semantic-Authenticity Descriptions, Motion Features, and Low-level Visual Features, facilitating authenticity detection and supporting downstream tasks such as multimodal fusion and forgery localization. Extensive experiments using advanced vision-language models demonstrate limited detection capabilities on the most challenging subsets of AEGIS, highlighting the dataset's unique complexity and realism beyond the current generalization capabilities of existing models. In essence, AEGIS establishes an indispensable evaluation benchmark, fundamentally advancing research toward developing genuinely robust, reliable, and broadly generalizable video authenticity detection methodologies capable of addressing real-world forgery threats. Our dataset is avaliable on https://huggingface.co/datasets/Clarifiedfish/AEGIS.
Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into natural scenes. Although RGB-based methods have advanced, their performance remains limited under challenging conditions. Multispectral imagery, providing rich spectral information, offers a promising alternative for enhanced foreground-background discrimination. However, existing COD benchmark datasets are exclusively RGB-based, lacking essential support for multispectral approaches, which has impeded progress in this area. To address this gap, we introduce MCOD, the first challenging benchmark dataset specifically designed for multispectral camouflaged object detection. MCOD features three key advantages: (i) Comprehensive challenge attributes: It captures real-world difficulties such as small object sizes and extreme lighting conditions commonly encountered in COD tasks. (ii) Diverse real-world scenarios: The dataset spans a wide range of natural environments to better reflect practical applications. (iii) High-quality pixel-level annotations: Each image is manually annotated with precise object masks and corresponding challenge attribute labels. We benchmark eleven representative COD methods on MCOD, observing a consistent performance drop due to increased task difficulty. Notably, integrating multispectral modalities substantially alleviates this degradation, highlighting the value of spectral information in enhancing detection robustness. We anticipate MCOD will provide a strong foundation for future research in multispectral camouflaged object detection. The dataset is publicly accessible at https://github.com/yl2900260-bit/MCOD.
Robotic task planning in real-world environments requires not only object recognition but also a nuanced understanding of spatial relationships between objects. We present a spatial-relationship-aware dataset of nearly 1,000 robot-acquired indoor images, annotated with object attributes, positions, and detailed spatial relationships. Captured using a Boston Dynamics Spot robot and labelled with a custom annotation tool, the dataset reflects complex scenarios with similar or identical objects and intricate spatial arrangements. We benchmark six state-of-the-art scene-graph generation models on this dataset, analysing their inference speed and relational accuracy. Our results highlight significant differences in model performance and demonstrate that integrating explicit spatial relationships into foundation models, such as ChatGPT 4o, substantially improves their ability to generate executable, spatially-aware plans for robotics. The dataset and annotation tool are publicly available at https://github.com/PengPaulWang/SpatialAwareRobotDataset, supporting further research in spatial reasoning for robotics.
Preserving the semantic integrity of image details is difficult in neural image compression. Failure to do so can result in miscompressions: reconstruction errors that change the meaning between the original and reconstructed images. Undetected miscompressions can compromise the reliability of reconstructed images and potentially reduce the accuracy of downstream computer vision tasks. To advance research on this problem, we present SCLIC, a curated dataset of 18k human-annotated miscompressions generated by 12 neural compression models. It includes images from three common benchmark datasets, compressed and reconstructed using codecs based on CNNs, GANs, diffusion models, and image transformers for different perceptual metrics and rate-distortion settings. We envision that this dataset will facilitate the development of strategies to mitigate miscompressions and enable more reliable neural image compression codecs.
Detecting hate speech in videos remains challenging due to the complexity of multimodal content and the lack of fine-grained annotations in existing datasets. We present HateClipSeg, a large-scale multimodal dataset with both video-level and segment-level annotations, comprising over 11,714 segments labeled as Normal or across five Offensive categories: Hateful, Insulting, Sexual, Violence, Self-Harm, along with explicit target victim labels. Our three-stage annotation process yields high inter-annotator agreement (Krippendorff's alpha = 0.817). We propose three tasks to benchmark performance: (1) Trimmed Hateful Video Classification, (2) Temporal Hateful Video Localization, and (3) Online Hateful Video Classification. Results highlight substantial gaps in current models, emphasizing the need for more sophisticated multimodal and temporally aware approaches. The HateClipSeg dataset are publicly available at https://github.com/Social-AI-Studio/HateClipSeg.git.
Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content. Furthermore, we introduce HAI, a human-AI interactive data synthesis approach that leverages the efficient text-editing capabilities of LLMs alongside human expert knowledge to generate diverse and high-quality HQA data at a low cost. Using HAI, we construct Chart-HQA, a challenging benchmark synthesized from publicly available data sources. Evaluation results on 18 MLLMs of varying model sizes reveal that current models face significant generalization challenges and exhibit imbalanced reasoning performance on the HQA task. Our codebase and newly generated datasets are available at https://github.com/chenxn2020/Chart-HQA.
Online form filling is a common yet labor-intensive task involving extensive keyboard and mouse interactions. Despite the long-standing vision of automating this process with ''one click,'' existing tools remain largely rule-based and lack generalizable, generative capabilities. Recent advances in Multimodal Large Language Models (MLLMs) have enabled promising agents for GUI-related tasks in general-purpose scenarios. However, they struggle with the unique challenges of form filling, such as flexible layouts and the difficulty of aligning textual instructions with on-screen fields. To bridge this gap, we formally define the form-filling task and propose FormFactory-an interactive benchmarking suite comprising a web-based interface, backend evaluation module, and carefully constructed dataset. Our benchmark covers diverse real-world scenarios, incorporates various field formats, and simulates high-fidelity form interactions. We conduct a comprehensive evaluation of state-of-the-art MLLMs and observe that no model surpasses 5% accuracy, underscoring the inherent difficulty of the task. These findings also reveal significant limitations in current models' visual layout reasoning and field-value alignment abilities.
The rapid advancement of GenAI technology over the past few years has significantly contributed towards highly realistic deepfake content generation. Despite ongoing efforts, the research community still lacks a large-scale and reasoning capability driven deepfake benchmark dataset specifically tailored for person-centric object, context and scene manipulations. In this paper, we address this gap by introducing MultiFakeVerse, a large scale person-centric deepfake dataset, comprising 845,286 images generated through manipulation suggestions and image manipulations both derived from vision-language models (VLM). The VLM instructions were specifically targeted towards modifications to individuals or contextual elements of a scene that influence human perception of importance, intent, or narrative. This VLM-driven approach enables semantic, context-aware alterations such as modifying actions, scenes, and human-object interactions rather than synthetic or low-level identity swaps and region-specific edits that are common in existing datasets. Our experiments reveal that current state-of-the-art deepfake detection models and human observers struggle to detect these subtle yet meaningful manipulations. The code and dataset are available on https://github.com/Parul-Gupta/MultiFakeVerse GitHub.
Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.
Rapid advances in Artificial Intelligence Generated Content (AIGC) have enabled increasingly sophisticated face forgeries, posing a significant threat to social security. However, current Deepfake detection methods are limited by constraints in existing datasets, which lack the diversity necessary in real-world scenarios. Specifically, these data sets fall short in four key areas: unknown of advanced forgery techniques, variability of facial scenes, richness of real data, and degradation of real-world propagation. To address these challenges, we propose the Multi-dimensional Face Forgery Image (MFFI ) dataset, tailored for real-world scenarios. MFFI enhances realism based on four strategic dimensions: 1) Wider Forgery Methods; 2) Varied Facial Scenes; 3) Diversified Authentic Data; 4) Multi-level Degradation Operations. MFFI integrates 50 different forgery methods and contains 1024K image samples. Benchmark evaluations show that MFFI outperforms existing public datasets in terms of scene complexity, cross-domain generalization capability, and detection difficulty gradients. These results validate the technical advance and practical utility of MFFI in simulating real-world conditions. The dataset and additional details are publicly available at https://github.com/inclusionConf/MFFI.
Short video platforms are popular for sharing information, but they also spread harmful content quickly. Research on detecting harmful videos is limited due to unclear categories and a lack of good datasets. To solve this, we created CH-SV, the first Chinese Harmful Short Video dataset with 6,728 videos labeled into six categories: danger, offense, vulgarity, fakeness, violence, and normal content. We analyzed CH-SV in detail and proposed HAVE, a new detection framework that improves video understanding using AI-generated semantics. Experiments on CH-SV show that our dataset can help advance harmful video detection research, and HAVE outperforms existing methods. Resources including core code, dataset samples, supplementary material, and licensing details are available at https://github.com/DLUTSSL/CH-SV.
Food image classification models are crucial for dietary management applications because they reduce the burden of manual meal logging. However, most publicly available datasets for training such models rely on web-crawled images, which often differ from users' real-world meal photos. In this work, we present FoodLogAthl-218, a food image dataset constructed from real-world meal records collected through the dietary management application FoodLog Athl. The dataset contains 6,925 images across 218 food categories, with a total of 14,349 bounding boxes. Rich metadata, including meal date and time, anonymized user IDs, and meal-level context, accompany each image. Unlike conventional datasets-where a predefined class set guides web-based image collection-our data begins with user-submitted photos, and labels are applied afterward. This yields greater intra-class diversity, a natural frequency distribution of meal types, and casual, unfiltered images intended for personal use rather than public sharing. In addition to (1) a standard classification benchmark, we introduce two FoodLog-specific tasks: (2) an incremental fine-tuning protocol that follows the temporal stream of users' logs, and (3) a context-aware classification task where each image contains multiple dishes, and the model must classify each dish by leveraging the overall meal context. We evaluate these tasks using large multimodal models (LMMs). The dataset is publicly available at https://huggingface.co/datasets/FoodLog/FoodLogAthl-218.
As a fundamental task for indoor scene understanding, 3D object detection has been extensively studied, and the accuracy on indoor point cloud data has been substantially improved. However, existing researches have been conducted on limited datasets, where the training and testing sets share the same distribution. In this paper, we consider the task of adapting indoor 3D object detectors from one dataset to another, presenting a comprehensive benchmark with ScanNet, SUN RGB-D and 3D Front datasets, as well as our newly proposed large-scale datasets ProcTHOR-OD and ProcFront generated by a 3D simulator. Since indoor point cloud datasets are collected and constructed in different ways, the object detectors are likely to overfit to specific factors within each dataset, such as point cloud quality, bounding box layout and instance features. We conduct experiments across datasets on different adaptation scenarios including synthetic-to-real adaptation, point cloud quality adaptation, layout adaptation and instance adaptation, analyzing the impact of different domain gaps on 3D object detectors. We also introduce several approaches to improve adaptation performances, providing baselines for domain adaptive indoor 3D object detection, hoping that future works may propose detectors with stronger generalization ability across domains. The benchmark datasets and baseline code are available on our project homepage: https://jeremyzhao1998.github.io/DAVoteNet-release.