Computer Vision
GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment
Kidus Zewde, Simiao Ren, Xingyu Shen et al. (+6 more)
The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter
Dataset, the first published
dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21, 2026 release. Leveraging the Twitter API v2 and a multi-stage curation pipeline spanning
multilingual text heuristics (English, Japanese, and Chinese), browser-automated Twitter "Made with AI" badge verification, and model name variant matching, we curate 10,217 confirmed GPT-image-2 images from 27,662 collected records over a six-day window. We characterize the
dataset across four analyses: CLIP-based zero-shot subject taxonomy, OCR text legibility (82.0% of images contain detectable text), face detection (59.2% of images, 22,583 total faces), and semantic clustering (137 CLIP ViT-L/14 clusters). A key negative result is that C2PA content credentials are systematically stripped by Twitter's CDN on upload, rendering cryptographic provenance verification infeasible for social-media-sourced AI images. The
dataset and all curation code are released publicly.
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All authors Kidus Zewde, Simiao Ren, Xingyu Shen, Jiaqi Wu, Yuchen Zhou, Tommy Duong, Zikang Zhang, Ethan Traister, Kewen Xie
Computer Vision
Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation
Jianyu Wen, Jun Xie, Feng Chen et al. (+5 more)
In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our previous Self-Reference Deep Adaptive Curve Estimation (Self-DACE). To better address the trade-off between computational efficiency and restoration quality, Self-DACE++ introduces enhanced Adaptive Adjustment Curves (AACs). These curves, governed by minimal trainable parameters, flexibly adjust the dynamic range while preserving the color fidelity, structural integrity, and naturalness of the enhanced images. To achieve an extremely lightweight architecture without sacrificing performance, we propose a randomized order training strategy coupled with a network fusion mechanism, which compresses the model into an efficient iterative inference structure. Furthermore, we formulate a physics-grounded objective function based on Retinex theory and incorporate a dedicated denoising module to effectively estimate and suppress latent noise in dark regions. Extensive qualitative and quantitative evaluations on multiple real-world
benchmark datasets demonstrate that Self-DACE++ outperforms existing state-of-the-art methods, delivering superior enhancement quality with real-time inference capability. The code is available at https://github.com/John-Wendell/Self-DACE.
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All authors Jianyu Wen, Jun Xie, Feng Chen, Zhepeng Wang, Chenhao Wu, Tong Zhang, Yixuan Yu, Piotr Swierczynski
Computer Vision
HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation
Bingzi Zhang, Kaisi Guan and Ruihua Song
Video generation models have developed rapidly in recent years, where generating natural human motion plays a pivotal role. However, accurately evaluating the quality of generated human motion
video remains a significant challenge. Existing evaluation metrics primarily focus on global scene statistics, often overlooking fine-grained human details and consequently failing to align with human subjective preference. To bridge this gap, we propose HuM-Eval, a novel human-centric evaluation framework that adopts a coarse-to-fine strategy. Specifically, our framework first utilizes a
Vision Language Model to perform a coarse assessment of global
video quality. It then proceeds to a fine-grained analysis, using 2D pose to verify anatomical correctness and 3D human motion to evaluate motion stability. Extensive experiments demonstrate that HuM-Eval achieves an average human correlation of 58.2%, outperforming state-of-the-art baselines. Furthermore, we introduce HuM-Bench, a comprehensive
benchmark comprising 1,000 diverse
prompts, and conduct a detailed evaluation of existing text-to-
video models, paving the way for next-generation human motion generation.
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Computer Vision
Benchmarking Layout-Guided Diffusion Models through Unified Semantic-Spatial Evaluation in Closed and Open Settings
Luca Parolari, Nicla Faccioli and Lamberto Ballan
Evaluating layout-guided text-to-image generative models requires assessing both semantic
alignment with textual
prompts and spatial fidelity to prescribed layouts. Assessing layout
alignment requires collecting fine-grained annotations, which is costly and labor-intensive. Consequently, current
benchmarks rarely provide comprehensive layout evaluation and often remain limited in scale or coverage, making model comparison, ranking, and interpretation difficult. In this work, we introduce a closed-set
benchmark (C-Bench) designed to isolate key generative capabilities while providing varying levels of complexity in both
prompt structure and layout. To complement this controlled setting, we propose an open-set
benchmark (O-Bench) that evaluates models using real-world
prompts and layouts, offering a measure of semantic and spatial
alignment in the wild. We further develop a unified evaluation protocol that combines semantic and spatial accuracy into a single score, ensuring consistent model ranking. Using our
benchmarks, we conduct a large-scale evaluation of six state-of-the-art layout-guided
diffusion models, totaling 319,086 generated and evaluated images. We establish a model ranking based on their overall performance and provide detailed breakdowns for text and layout
alignment to enhance interpretability. Fine-grained analyses across scenarios and
prompt complexities highlight the strengths and limitations of current models. Code is available at https://github.com/lparolari/cobench.
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Computer Vision
Simulation of temporomandibular joint space changes based on rigid-body transformation using virtual and semi-virtual models
Agnieszka Anna Tomaka, Krzysztof Domino, Michał Tarnawski et al. (+3 more)
The integration of
multimodal data within a common coordinate system is essential for spatial analysis of the stomatognathic system. This study aims to develop a
multimodal data integration methodology for digital modeling of spatial changes in the temporomandibular joint (TMJ) across various mandible positions. A transformation tree framework was proposed to integrate CBCT, facial, and dental representation images into a common coordinate system and to simulate seven mandible positions using rigid-body transformations. Two proprietary models were applied. Spatial changes in mandible positions were propagated to TMJ articular surface positions using both models, although the results were model-related. The model-based uncertainty was quantified using error metrics. The consistency error revealed variability that dominated in the translation component over the rotation component of the rigid-body transformation, and showed anisotropy in the latero-lateral direction. The simulation errors were not uniform across mandible positions and were higher (p<0.0001) at the TMJ level (1.46 mm) than at the dental level (0.47 mm). As even small discrepancies in mandible position can lead to significant changes in the TMJ joint space, the proposed methodology may be useful for evaluating geometrically related positions of the mandible and TMJ, particularly when repeated radiographic imaging of the TMJs is undesirable.
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All authors Agnieszka Anna Tomaka, Krzysztof Domino, Michał Tarnawski, Emil Kalinowski, Małgorzata Domino, Dariusz Pojda
Computer Vision
Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception
Yun Li and Xianju Li
The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth forces the use of extreme high-ratio compression, which irreversibly destroys high-frequency structural details essential for downstream machine perception tasks like object detection. While current super-resolution techniques attempt to recover these details, regression-based methods often yield over-smoothed textures, and generative
diffusion models frequently introduce structural
hallucinations that mislead detection systems. To address this trade-off, we propose the Structure-Aware Latent
Diffusion (SALD) framework, an asymmetric edge-cloud collaborative SR system. At the resource-constrained edge, the system decouples imagery into a highly compressed low-frequency payload and a lightweight soft structural prior. Transmitting this decoupled representation minimizes bandwidth consumption. On the powerful cloud side, we introduce a Structure-Gated Large Kernel (SGLK) module and a Semantic-Guidance Engine (SGE) within the
diffusion backbone. These modules leverage the transmitted structural priors to gate large-kernel convolutions, effectively capturing long-range dependencies inherent in aerial scenes while actively suppressing generative
hallucinations. Extensive experiments on both the MSCM and UCMerced
datasets demonstrate that, even under extreme bandwidth constraints, SALD achieves superior perceptual quality (LPIPS) and significantly enhances downstream performance in both scene classification and small-target detection.
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Computer Vision
Towards Robust Deep Learning-based Rumex Obtusifolius Detection from Drone Images
Fabian Dionys Schrag, Mehmet Ozgur Turkoglu, Konrad Schindler et al. (+1 more)
Domain adaptation (DA) addresses the challenge of transferring a machine learning model trained on a source domain to a target domain with a different data distribution. In this work, we study DA for the task of Rumex obtusifolius (Rumex) image classification. We train models on a published, ground vehicle-based
dataset (source) and evaluate their performance on a custom target
dataset acquired by unmanned aerial vehicles (
UAVs). We find that Convolutional Neural Network (CNN) models, specifically ResNets, generalize poorly to the target domain, even after fine-tuning on the source data. Applying moment-matching and maximum classifier discrepancy, two established DA techniques, substantially improves target-domain performance. However,
Vision Transformer (ViT) models pretrained with self-supervised objectives (DINOv2, DINOv3) handle domain shifts intrinsically well, surpassing even moment-matching-trained ResNets, likely due to the rich, general-purpose representations acquired during large-scale pretraining. Using ViTs fine-tuned on the source
dataset, we demonstrate high classification performances in the range of F1=0.8 on our target
dataset. To support further research on DA for weed detection in grassland systems, we publicly release our
UAV-based target
dataset AGSMultiRumex, comprising data from 15 flights over Swiss meadows.
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All authors Fabian Dionys Schrag, Mehmet Ozgur Turkoglu, Konrad Schindler, Ralph Lukas Stoop
Computer Vision
SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation
Ali Karkehabadi, Jamshid Hassanpour, Houman Homayoun et al. (+1 more)
Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly align with semantically meaningful input features. We argue that a fundamental cause of this behavior lies in the geometry of learned representations: correlated feature dimensions diffuse attribution gradients across redundant directions, resulting in blurred and unreliable saliency maps. To address this issue, we identify feature correlation as a structural limitation of gradient-based interpretability and propose SaliencyDecor, a training framework that enforces feature decorrelation to improve attribution fidelity without modifying saliency methods or model architectures by reshaping the feature space toward orthogonality, our approach promotes more concentrated gradient flow and improves the fidelity of saliency-based explanations. SaliencyDecor jointly optimizes classification, prediction consistency under feature masking, and a decorrelation regularizer, requiring no architectural changes or inference-time overhead. Extensive experiments across multiple
benchmarks and architectures demonstrate that our method produces substantially sharper and more object-focused saliency maps while simultaneously improving predictive performance, achieving accuracy gains across the
datasets. These results establish our method as a principled mechanism for enhancing both interpretability and accuracy, challenging the conventional trade-off between explanation quality and model performance.
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All authors Ali Karkehabadi, Jamshid Hassanpour, Houman Homayoun, Avesta Sasan
Computer Vision
Golden RPG: Confidence-Adaptive Region-Aware Noise for Compositional Text-to-Image Generation
Hao Li
Compositional text-to-image (T2I) generation requires a model to honour multiple sub-
prompts that describe distinct image regions. Recent work shows that the starting noise of a
diffusion model carries significant semantic information: “golden” noise predicted from text can substantially raise
prompt fidelity. We observe that this noise prediction is, however, fundamentally global: the same network is asked to summarise a long, multi-region
prompt with a single text
embedding, which becomes the bottleneck whenever the
prompt describes scenes with spatially-separated entities. We introduce Golden RPG, a region-aware noise predictor that extends a frozen NPNet with two trainable additions: (i) a per-region FiLM adapter that reshapes the predicted noise according to each sub-
prompt; and (ii) a Region Cross-
Attention layer injected between two stages of the Swin backbone, allowing different spatial locations to attend to different sub-
prompt tokens. To prevent the regional conditioning from degrading samples whose
prompts are already easy, we further propose a Confidence-Adaptive Blending head that dynamically predicts, per sample, how strongly the regional signal should override the global signal. We evaluate on the original RPG
benchmark (20
prompts, 100 samples) and on four multi-region categories of T2I-CompBench (1{,}200 images, six competing methods). Golden RPG achieves the highest Cross-Region-Coherence score on every category, while matching the strongest baselines on absolute CLIP-Score and CLIP-IQA. A paired user study further shows a $\boldsymbol{\sim}$67% preference over the strongest baseline. The adapter contains $\sim$2M trainable parameters and adds only $0.6$\,s of inference overhead on top of SDXL.
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Computer Vision
Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis
Yuqing Cao, Shuo Zhu, Rongzhou Chen et al. (+3 more)
This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.
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All authors Yuqing Cao, Shuo Zhu, Rongzhou Chen, Jingyan Chen, Ni Chen, Edmund Y. Lam
Computer Vision
DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms
Zhouzhi Xiong, Zimo Zeng, Yi Chen et al. (+3 more)
Deploying high-resolution tiny-object perception on edge platforms requires not only accurate localization, but also selecting a small set of informative patches under compute, transport, and latency constraints. We study budgeted tiny-object selection, where a frontend ranks patch centers from a lightweight proxy and a downstream detector processes only the selected regions. DenseScout is a 1.01M-parameter deployment-oriented dense-response selector that removes detector-style box regression and directly optimizes ranked patch-center prioritization. Its contribution lies in the task-specific selector formulation, the
alignment among output representation, supervision, and decoding, and its joint design with transport-aware execution and QoS-oriented evaluation. Under unified protocols on VisDrone and DOTA, DenseScout provides stronger low-budget recall than the evaluated detector-derived selectors; controlled fixed-K inspection experiments further demonstrate advantages over selection-style proxy baselines. Cross-platform profiling on Jetson Orin NX and RK3588 shows that deployable utility depends jointly on selector quality,
memory movement, and heterogeneous runtime realization. These results support treating edge tiny-object perception as a selection-and-deployment co-design problem rather than evaluating model accuracy and runtime in isolation.
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All authors Zhouzhi Xiong, Zimo Zeng, Yi Chen, Shuqi Xu, Yunfeng Yan, Donglian Qi
Computer Vision
ReMoA: Recursive Modular Adaptation via Sparse Latent Refinement in Multimodal Diffusion Models
Yuwei Sun, Yuxuan Yao, Hui Li et al. (+2 more)
Diffusion models have achieved success in high-fidelity data synthesis, yet their capacity for more complex, structured
reasoning such as text following remains constrained. While advances in
language models have leveraged strategies such as latent
reasoning and recursion to enhance text understanding capabilities, extending these to
multimodal text-to-image generation tasks is challenging due to the continuous and non-discrete nature of visual
tokens. To tackle this problem, we draw inspiration from modular human cognition and propose a Recursive Modular Adaptation (ReMoA) framework integrated into joint
attention layers of conventional
diffusion models, iteratively refining visual
tokens over multiple recursion steps while efficiently sharing parameters via sparse selection of neural modules. Comprehensive evaluations on both class-conditioned ImageNet generation and text-to-image generation using the GenEval and DPG
benchmarks demonstrate that recursive visual latent refinement contributes to richer object details and better-aligned outputs. Additionally, visual latent trajectory and routing analyses reveal that neural modules learn to specialize across
diffusion timesteps, with each module's trajectories diverging at later, low-noise steps to support context-adaptive refinement. ReMoA increases recall for DiT-XL from 0.57 to 0.62 on ImageNet, and improves the overall GenEval score for SD3-medium from 68.33% to 71.18%, without introducing additional parameters through recursion.
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All authors Yuwei Sun, Yuxuan Yao, Hui Li, Weihao Yuan, Siyu Zhu
Computer Vision · Cross-listed from cs.LG
Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds
Liuzhuozheng Li, Zhiyuan Zhan, Shuhong Liu et al. (+5 more)
Practically, training
diffusion models typically requires explicit time conditioning to guide the network through the denoising sampling process. Especially in deterministic methods like DDIM, the absence of time conditioning leads to significant performance degradation. However, other deterministic sampling approaches, such as flow matching, can generate high-quality content without this conditioning, raising the question of its necessity. In this work, we revisit the role of time conditioning from a
geometric perspective. We analyze the
evolution of noisy data distributions under the forward
diffusion process and demonstrate that, in high-dimensional spaces, these distributions concentrate on low-dimensional hyper-cylinder-like
manifolds embedded within the input space. Successful generation, we argue, stems from the disentanglement of these
manifolds in high-dimensional space. Based on this insight, we modify the forward process of DDIM to align the noisy data
manifold with the flow-matching approach, proving that DDIM can generate high-quality content without time conditioning, provided the noisy
manifold evolves according to the flow-matching method. Additionally, we extend our framework to class-conditioned generation by decoupling classes into distinct time spaces, enabling class-conditioned synthesis with a class-unconditional denoising model. Extensive experiments validate our theoretical analysis and show that high-quality generation is achievable without explicit conditional
embeddings.
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All authors Liuzhuozheng Li, Zhiyuan Zhan, Shuhong Liu, Dengyang Jiang, Zanyi Wang, Guang Dai, Jingdong Wang, Mengmeng Wang
Computer Vision
OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding
Minghang Zheng, Zihao Yin, Yi Yang et al. (+2 more)
Video Temporal Grounding (VTG), the task of localizing
video segments from text queries, struggles in open-world settings due to limited
dataset scale and semantic diversity, causing performance gaps between common and rare concepts. To overcome these limitations, we introduce OmniVTG, a new large-scale
dataset for open-world VTG, coupled with a Self-Correction Chain-of-Thought (CoT) training paradigm designed to enhance the grounding capabilities of
Multimodal Large
Language Models (MLLMs). Our OmniVTG is constructed via a novel Semantic Coverage Iterative Expansion pipeline, which first identifies gaps in the vocabulary of existing
datasets and collects
videos that are highly likely to contain these target concepts. For high-quality annotation, we leverage the insight that modern MLLMs excel at dense captioning more than direct grounding and design a caption-centric data engine to
prompt MLLMs to generate dense, timestamped descriptions. Beyond the
dataset, we observe that simple supervised finetuning (SFT) is insufficient, as a performance gap between rare and common concepts still persists. We find that MLLMs'
video understanding ability significantly surpasses their direct grounding ability. Based on this, we propose a Self-Correction Chain-of-Thought (CoT) training paradigm. We train the MLLM to first predict, then use its understanding capabilities to reflect on and refine its own predictions. This capability is instilled via a three-stage pipeline of SFT, CoT finetuning, and reinforcement learning. Extensive experiments show our approach not only excels at open-world grounding in our OmniVTG
dataset but also achieves state-of-the-art zero-shot performance on four existing VTG
benchmarks. Code is available at https://github.com/oceanflowlab/OmniVTG.
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All authors Minghang Zheng, Zihao Yin, Yi Yang, Yuxin Peng, Yang Liu
Computer Vision
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval
Guosheng Zhang, Linkai Liu, Keyao Wang et al. (+3 more)
Despite significant progress in Unified
Multimodal Retrieval (UMR) powered by Large
Multimodal Models (LMMs), existing
embedding methods primarily focus on sample-level objectives via contrastive learning while overlooking the crucial subject-level semantics. This limitation hinders the model's ability to group semantically coherent subjects in complex
multimodal queries, manifesting as semantic
alignment deviation–where models fail to accurately localize salient text-referred regions in visual content. Moreover, without explicit guidance to model salient visual subjects, LMMs tend to over-rely on textual cues, resulting in visual modality neglect and suboptimal utilization of visual knowledge. To this end, we propose Salient Subject-Aware
Multimodal Embedding (SSA-ME), a novel framework designed to enhance fine-grained representation learning through saliency-aware modeling. SSA-ME leverages LMMs and visual experts to identify and emphasize salient visual concepts in image-text pairs, and introduces a saliency-guided objective to better align cross-modal
attention with semantically meaningful regions. Additionally, a feature regeneration module recalibrates visual features based on the derived saliency maps, ensuring a balanced and semantically coherent integration across modalities. Extensive experiments show that our method achieves state-of-the-art performance on the MMEB
benchmark, demonstrating that incorporating subject-level modeling substantially improves
multimodal retrieval. Comprehensive qualitative analyses further illustrate the interpretability and effectiveness of our approach.
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All authors Guosheng Zhang, Linkai Liu, Keyao Wang, Haixiao Yue, Zhiwen Tan, Xiao Tan
Computer Vision
Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation
Tianshui Chen, Yujie Zhu, Jianman Lin et al. (+4 more)
Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original
speech. A primary challenge in this domain is the scarcity of paired data, namely aligned frames of the same individual with identical
speech but different expressions, which impedes direct supervision for emotional manipulation. While current Visual-
Language Models (VLMs) can extract aligned visual and semantic features, making them a promising source of supervision, their direct application is limited. To this end, we propose a Personalized Cross-Modal Emotional Correlation Learning (PCMECL)
algorithm that refines VLM-based supervision through two major improvements. First, standard VLMs rely on a single generic
prompt for each
emotion, failing to capture expressive variations among individuals. PCMECL addresses this limitation by conditioning on individual visual information to learn personalized
prompts, thereby establishing more fine-grained visual-semantic correlations. Second, even with personalization, inherent discrepancies persist between the visual and semantic feature distributions. To bridge this modality gap, PCMECL employs feature differencing to correlate the modalities, providing more precisely aligned supervision by matching the change in visual features to the change in semantic features. As a plug-and-play module, PCMECL can be seamlessly integrated into existing SPFEM models. Extensive experiments across various
datasets demonstrate the superior efficacy of our
algorithm.
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All authors Tianshui Chen, Yujie Zhu, Jianman Lin, Zhijing Yang, Chunmei Qing, Feng Gao, Liang Lin
Computer Vision · Cross-listed from cs.LG
VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation
Divake Kumar, Sina Tayebati, Devashri Naik et al. (+2 more)
Vision-
language models (VLMs) are increasingly used as automated judges for
multimodal systems, yet their scores provide no indication of reliability. We study this problem through conformal prediction, a distribution-free framework that converts a judge's point score into a calibrated prediction interval using only score-
token log-probabilities, with no retraining. We present the first systematic analysis of conformal prediction for VLM-as-a-Judge across 3 judges and 14 visual task categories. Our results show that evaluation uncertainty is strongly task-dependent: intervals cover ~40% of the score range for aesthetics and natural images but expand to ~70% for chart and mathematical
reasoning, yielding a quantitative reliability map for
multimodal evaluation. We further identify a failure mode not captured by standard evaluation metrics, ranking-scoring decoupling, where judges achieve high ranking correlation while producing wide, uninformative intervals, correctly ordering responses but failing to assign reliable absolute scores. Finally, we show that interval width is driven primarily by task difficulty and annotation quality, i.e., the same judge and method yield 4.5x narrower intervals on a clean, multi-annotator captioning
benchmark. Code: https://github.com/divake/VLM-Judge-Uncertainty
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All authors Divake Kumar, Sina Tayebati, Devashri Naik, Ranganath Krishnan, Amit Ranjan Trivedi
Computer Vision
DRAGON: A Benchmark for Evidence-Grounded Visual Reasoning over Diagrams
Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Gaurav Najpande et al. (+4 more)
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics,
circuit schematics, and scientific diagrams. Recent
vision-
language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their
reasoning in the diagram regions that support the prediction. Models may instead rely on textual correlations or
dataset artifacts without identifying the visual evidence required to verify the answer. This limitation prevents reliable evaluation of diagram
reasoning and reduces interpretability. We introduce DRAGON, a
benchmark for evaluating evidence-grounded visual
reasoning in diagrams. Given a diagram, a question, and the correct answer, a model must predict bounding boxes that correspond to the visual elements required to justify the answer. These evidence regions may include answer-bearing components, textual labels, legends, axes, connectors, and other supporting structures involved in the
reasoning process. The DRAGON
dataset contains 11,664 annotated question instances from six diagram QA
datasets: ChartQA,
Circuit-VQA, InfographicsVQA, MapIQ, MapWise, and AI2D, with a 2,445-instance test set carrying human-verified evidence annotations and a standardized evaluation framework. Our evaluation on recent VLMs reveals that even Claude Opus 4.6 achieves a Grounding IoU of only 23.8% on AI2D, showing that faithful visual grounding remains an open challenge. DRAGON supports future research on models that ground their predictions in visual evidence.
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All authors Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Gaurav Najpande, Manan Suri, Dinesh Manocha, Puneet Mathur, Vivek Gupta
Computer Vision
When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents
Jiaqi Wu, Yuchen Zhou, Dennis Tsang Ng et al. (+5 more)
OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can be replaced in under a second for a few cents. We release AIForge-Doc v2, a paired
dataset of 3,066 GPT-Image-2 document forgeries with pixel-precise masks in DocTamper-compatible format, and
benchmark four lines of defence: human inspectors (N=120, n=365 pair-votes via the public 2AFC site CanUSpotAI.com), TruFor (generic forensic), DocTamper (qcf-568, document-specific), and the same GPT-Image-2 model as a zero-shot self-judge – asked, to avoid the trivial "image is mostly real" reading, whether any region was generated or edited by an AI image model. Human 2AFC accuracy is 0.501, indistinguishable from chance: even side-by-side, inspectors cannot tell GPT-Image-2 receipt forgeries from authentic counterparts. The three computational judges sit only modestly above (TruFor 0.599, DocTamper 0.585, self-judge 0.532). The self-judge fails consistently, not by chance: across five
prompt strategies and four policies for handling ambiguous responses, AUC never rises above 0.59. To rule out the possibility that the two forensic detectors are broken on our source domain rather than blind to AI inpainting, we calibrate each on a same-domain traditional-tampering set built for its training distribution: TruFor reaches AUC 0.962 on cross-camera splicing of our
dataset, DocTamper reaches 0.852 on cross-document OCR-
token splicing with two-pass JPEG re-encoding. Both retain near-published performance on traditional tampering; switching to GPT-Image-2 inpainting drops AUC by 0.27-0.36 (0.962->0.599 TruFor; 0.852->0.585 DocTamper), isolating a detection gap specific to GPT-Image-2 inpainting. We release the
dataset, pipeline, four-judge protocol, and calibration sets.
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All authors Jiaqi Wu, Yuchen Zhou, Dennis Tsang Ng, Xingyu Shen, Kidus Zewde, Ankit Raj, Tommy Duong, Simiao Ren
Computer Vision
Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data
Pratincha Singh, Jai Gopal Singla, Prashant Hemrajani et al. (+3 more)
Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illumination geometry, sensor characteristics, and acquisition conditions. This paper presents a deep learning-based radiometric normalization framework for multi-mission lunar mosaics constructed primarily from ISRO's Chandrayaan-2 Terrain Mapping Camera (TMC) data, supplemented with auxiliary imagery from the SELENE (Kaguya) mission. The proposed approach employs a conditional generative
adversarial network (cGAN) comprising a U-Net-based generator and a PatchGAN discriminator to learn a nonlinear radiometric mapping from conventionally mosaicked lunar imagery to a photometrically consistent reference derived from LROC Wide Angle Camera (WAC) data. A patch-based training strategy with overlap-aware inference is adopted to enable scalable processing of large-area mosaics while preserving structural continuity across tile boundaries. Quantitative evaluation using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) demonstrates consistent improvements over traditional histogram-based normalization techniques. The proposed framework achieves enhanced tonal uniformity, reduced seam artifacts, and improved structural coherence across multi-source lunar
datasets. These results highlight the effectiveness of learning-based radiometric normalization for large-scale planetary mosaicking and demonstrate its potential for generating high-fidelity lunar surface maps from heterogeneous orbital imagery.
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All authors Pratincha Singh, Jai Gopal Singla, Prashant Hemrajani, Nitant Dube, Amithabh, Hinal Patel
Computer Vision
Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation
Wentao Jiang, Yuanchan Xu and Heng Yuan
Image classification remains a fundamental yet challenging task in computer
vision, particularly when fine-grained feature extraction and background noise suppression are required simultaneously. Conventional convolutional neural networks, despite their remarkable success in hierarchical feature learning, often struggle with capturing multi-scale contextual information and are susceptible to overfitting when confronted with noisy or irrelevant image regions. In this paper, we propose RDCNet (Image Classification Network with Random Dilated Convolution), a novel architecture built upon ResNet-34 that integrates three synergistic innovations to address these limitations: (1) a Multi-Branch Random Dilated Convolution (MRDC) module that employs parallel branches with varying dilation rates combined with a
stochastic masking mechanism to capture fine-grained features across multiple scales while enhancing robustness against noise and overfitting; (2) a Fine-Grained Feature Enhancement (FGFE) module embedded within MRDC that bridges global contextual information with local feature representations through adaptive pooling and bilinear interpolation, thereby amplifying sensitivity to subtle visual patterns; and (3) a Context Excitation (CE) module that leverages softmax-based spatial
attention and channel recalibration to dynamically emphasize task-relevant features while suppressing background interference. Extensive experiments conducted on five
benchmark datasets – CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof – demonstrate that RDCNet consistently achieves state-of-the-art classification accuracy, outperforming the second-best competing methods by margins of 0.02%, 1.12%, 0.18%, 4.73%, and 3.56%, respectively, thereby validating the effectiveness and generalizability of the proposed approach across diverse visual recognition scenarios.
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Computer Vision
FCMBench-Video: Benchmarking Document Video Intelligence
Runze Cui, Fangxin Shang, Yehui Yang et al. (+3 more)
Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and evidence traceability matter. Compared with static document images, document
videos present a temporally redundant and sequentially unfolding evidence stream, require evidence integration across frames, and preserve acquisition-process cues relevant to authenticity-sensitive and anti-fraud review. We introduce FCMBench-
Video, a
benchmark for document-
video intelligence that evaluates document perception, temporal grounding, and evidence-grounded
reasoning under realistic capture conditions. For
privacy-compliant yet realistic data at scale, we organize construction as an atomic-acquisition and composition workflow that records reusable single-document clips, applies controlled degradations, and assembles long-form multi-document
videos with prescribed temporal spans. FCMBench-
Video is built from 495 atomic
videos composed into 1,200 long-form
videos paired with 11,322 expert-annotated question–answer instances, covering 28 document types over 20s–60s duration tiers and 5,960 Chinese / 5,362 English instances. Evaluations on nine recent
Video-MLLMs show that FCMBench-
Video provides meaningful separation across systems and capabilities: counting is the most duration-sensitive task, Cross-Document Validation and Evidence-Grounded Selection probe higher-level evidence integration, and Visual
Prompt Injection provides a complementary robustness dimension. The overall score distribution is broad and approximately bell-shaped, indicating a
benchmark that is neither saturated nor dominated by trivial cases. Together, these results position FCMBench-
Video as a reproducible
benchmark for tracking
Video-MLLM progress on document-
video understanding and probing capability boundaries in authenticity-sensitive credit-domain applications.
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All authors Runze Cui, Fangxin Shang, Yehui Yang, Qing Yang, Yanwu Xu, Tao Chen
Computer Vision
Lightweight Real-Time Rendering Parameter Optimization via XGBoost-Driven Lookup Tables
Baijun Tan and Francesco Moretti
Achieving a desirable balance between rendering quality and real-time performance is a long-standing challenge in modern game and rendering engines, particularly on resource-constrained mobile devices such as laptops, tablets, and smartphones. Existing approaches to automatic rendering parameter optimization either depend on exhaustive per-scene pre-computation that spans several days, suffer from the prohibitive inference overhead of neural networks that prevents per-frame adaptation, or lack generalizability across heterogeneous hardware and diverse scenes. In this paper, we propose LUT-Opt, a lightweight, general-purpose framework for adaptive per-frame rendering parameter optimization. Our method decomposes the joint optimization of rendering time and image quality into a tractable two-stage pipeline. In the offline stage, we train a pair of XGBoost regressors to predict rendering time and image quality from rendering parameters, hardware state, and scene complexity descriptors. The trained ensemble models are then distilled into compact lookup tables (LUTs) through systematic discretization and a two-phase linear search that first constrains rendering time and subsequently maximizes structural similarity (SSIM). During runtime, the pre-computed LUT is queried every frame in sub-millisecond time, enabling truly adaptive parameter selection with negligible computational overhead. We validate LUT-Opt on two representative rendering techniques – subsurface scattering (SSS) and hybrid-pipeline ambient occlusion (AO) – implemented within Unreal Engine 5. Extensive experiments across multiple scenes and GPU configurations demonstrate that LUT-Opt reduces subsurface scattering rendering time by approximately 40% and ambient occlusion rendering time by roughly 70%, while incurring only about 2% increase in image quality error, with per-frame inference latency below 0.1\ ms.
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Computer Vision
Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization
Vijaysinh Gaikwad
The digitization of multi-domain retail billing documents remains a challenging task due to variability in scan quality, layout heterogeneity, and domain diversity across commercial sectors. This paper proposes and
benchmarks an intelligent, quality-aware adaptive Optical Character Recognition (OCR) pipeline for retail bill digitization spanning five domains: grocery stores, restaurants, hardware shops, footwear outlets, and clothing retailers. The proposed system integrates a Convolutional Neural Network (CNN)-based image enhancement module trained via self-supervised denoising, a
Laplacian variance-based image quality analyzer with three-tier routing, a confidence-driven adaptive feedback loop with iterative retry, and an NLP-based post-OCR correction layer. Experiments were conducted on a real-world
dataset of 360 heterogeneous retail bill images. Ground truth for quantitative evaluation was generated using an OCR ensemble majority voting strategy, a validated approach for scenarios without manual annotation. The proposed pipeline achieves a Character Error Rate (CER) of 18.4% and Word Error Rate (WER) of 27.6%, representing improvements of 26.4% and 31.2% respectively over the Raw Tesseract baseline. The pipeline additionally achieves a text density of 108.3 words per image, a noise ratio of 2.3%, and a processing time of 3.64 seconds per image - a 6.4x speed advantage over EasyOCR. Image quality PSNR analysis on enhanced MEDIUM and LOW quality images yields an average of 28.7 dB, confirming meaningful enhancement. These results establish a reproducible
benchmark for multi-domain retail bill OCR research.
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Computer Vision
IAM: Identity-Aware Human Motion and Shape Joint Generation
Wenqi Jia, Zekun Li, Abhay Mittal et al. (+6 more)
Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural
language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attributes such as body proportions, mass distribution, and age significantly affect how actions are performed, and neglecting this coupling often leads to physically inconsistent motions. We propose an identity-aware motion generation framework that explicitly models the relationship between body morphology and motion dynamics. Instead of relying on explicit
geometric measurements, identity is represented using
multimodal signals, including natural
language descriptions and visual cues. We further introduce a joint motion-shape generation paradigm that simultaneously synthesizes motion sequences and body shape parameters, allowing identity cues to directly modulate motion dynamics. Extensive experiments on motion capture
datasets and large-scale in-the-wild
videos demonstrate improved motion realism and motion-identity consistency while maintaining high motion quality. Project page: https://vjwq.github.io/IAM
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All authors Wenqi Jia, Zekun Li, Abhay Mittal, Chengcheng Tang, Chuan Guo, Lezi Wang, James Matthew Rehg, Lingling Tao, Size An
Computer Vision · Cross-listed from cs.GR
8DNA: 8D Neural Asset Light Transport by Distribution Learning
Liwen Wu, Haolin Lu, Bing Xu et al. (+2 more)
High-fidelity 3D assets exhibit intriguing global illumination effects like subsurface scattering, glossy interreflections, and fine-scale fiber scatterings, which often involve long scattering paths that are expensive to simulate. We introduce 8D neural assets (8DNA) to pre-bake these light transport effects into neural representations. Unlike prior methods that assume far-field lighting and precompute light transport into 6D functions, 8DNA learns the full 8D light transport, enabling accurate rendering under near-field illumination. Our training leverages a distribution-learning formulation that learns light transport from forward path-traced samples, which produces less optimization variance with lower training budget than the prior regression-based approaches. Experiments show our 8DNA rendering closely matches path-traced results under various scene configurations, yet it achieves improved variance reduction and fast inference speeds on challenging assets.
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All authors Liwen Wu, Haolin Lu, Bing Xu, Miloš Hašan, Ravi Ramamoorthi
Computer Vision
ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent
Hanyi Wang, Han Fang, Zheng Wang et al. (+2 more)
Recent advances in
diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies local regions while preserving global structure. Achieving such flexible and precise editing requires a high-quality starting point, a latent representation that provides both the freedom needed for diverse modifications and the precision required for fine-grained, region-specific control. However, existing inversion-based approaches such as DDIM inversion often yield unsatisfactory starting latents, resulting in degraded edit fidelity and structural inconsistency. Ideally, the most suitable editing anchor should be the original latent used during the generation process, as it inherently captures the scene's structure and semantics. Yet, storing this latent for every generated image is impractical due to massive storage and
retrieval costs. To address this challenge, we propose ResetEdit, a proactive
diffusion editing framework that embeds recoverable latent information directly into the generation process. By injecting the discrepancy between the clean and diffused latents into the
diffusion trajectory and extracting it during inversion, ResetEdit reconstructs a resettable latent that closely approximates the true starting state. Additionally, a lightweight latent optimization module compensates for reconstruction bias caused by VAE asymmetry. Built upon Stable
Diffusion, ResetEdit integrates seamlessly with existing tuning-free editing methods and consistently outperforms state-of-the-art baselines in both controllability and visual fidelity.
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All authors Hanyi Wang, Han Fang, Zheng Wang, Shilin Wang, Ee-Chien Chang
Computer Vision
M$^3$-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering
Jiatong Ma, Longteng Guo, Yuchen Liu et al. (+4 more)
We present M$^3$-VQA, a novel knowledge-based Visual Question Answering (VQA)
benchmark, to enhance the evaluation of
multimodal large
language models (MLLMs) in fine-grained
multimodal entity understanding and complex multi-hop
reasoning. Unlike existing VQA
datasets that focus on coarse-grained categories and simple
reasoning over single entities, M$^3$-VQA introduces diverse multi-entity questions involving multiple distinct entities from both visual and textual sources. It requires models to perform both sequential and parallel multi-hop
reasoning across multiple documents, supported by traceable, detailed evidence and a curated
multimodal knowledge base. We evaluate 16 leading MLLMs under three settings: without external knowledge, with gold evidence, and with
retrieval-augmented input. The poor results reveal significant challenges for MLLMs in knowledge acquisition and
reasoning. Models perform poorly without external information but improve markedly when provided with precise evidence. Furthermore,
reasoning-aware agentic
retrieval surpasses heuristic methods, highlighting the importance of structured
reasoning for complex
multimodal understanding. M$^3$-VQA presents a more challenging evaluation for advancing the
multimodal reasoning capabilities of MLLMs. Our code and
dataset are available at https://github.com/CASIA-IVA-Lab/M3VQA.
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All authors Jiatong Ma, Longteng Guo, Yuchen Liu, Zijia Zhao, Dongze Hao, Xuanxu Lin, Jing Liu
Computer Vision
One Perturbation, Two Failure Modes: Probing VLM Safety via Embedding-Guided Typographic Perturbations
Ravikumar Balakrishnan and Sanket Mendapara
Typographic
prompt injection exploits
vision language models' (VLMs) ability to read text rendered in images, posing a growing threat as VLMs power autonomous
agents. Prior work typically focus on maximizing attack success rate (ASR) but does not explain why certain renderings bypass safety
alignment. We make two contributions. First, an empirical study across four VLMs including GPT-4o and Claude, twelve font sizes, and ten transformations reveals that
multimodal embedding distance strongly predicts ASR ($r{=}{-}0.71$ to ${-}0.93$, $p{<}0.01$), providing an interpretable, model agnostic proxy. Since
embedding distance predicts ASR, reducing it should improve attack success, but the relationship is mediated by two factors: perceptual readability (whether the VLM can parse the text) and safety
alignment (whether it refuses to comply). Second, we use this as a red teaming tool: we directly maximize image text
embedding similarity under bounded $\ell_\infty$ perturbations via CWA-SSA across four surrogate
embedding models, stress testing both factors without access to the target model. Experiments across five degradation settings on GPT-4o, Claude Sonnet 4.5, Mistral-Large-3, and Qwen3-VL confirm that optimization recovers readability and reduces safety aligned refusals as two co-occurring effects, with the dominant mechanism depending on the model's safety filter strength and the degree of visual degradation.
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