Recent EEG-to-image retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such success does not reveal what visual information supports the match. A model may readily identify a cheetah among tools, plants, and vehicles, but can it still distinguish the viewed cheetah from the same scene with the cheetah replaced by a dog? Motivated by this question, we introduce EEG-EditBench, a diagnostic benchmark that examines this question through controlled edits of object identity, attributes, background, and object presence. Built from the 200 THINGS-EEG2 test images, EEG-EditBench contains 2,137 quality-controlled edits and evaluates eight representative EEG visual decoding models. Our results show that strong standard retrieval does not consistently transfer to edit-based evaluation, with fine-grained attribute changes presenting the greatest challenge. EEG-EditBench reveals model behavior hidden by aggregate retrieval accuracy and provides a controlled basis for studying what visual information EEG-image models preserve. The code and complete dataset are publicly available.

State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data. Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames. The proposed modular architecture is designed for seamless integration with existing VSR frameworks, ensuring strong adaptability and transferability across diverse applications. Experimental results demonstrate that our method achieves performance on par with, or surpassing, the current SOTA models, while significantly reducing inference time. By addressing key bottlenecks in CVSR, our work offers a practical and efficient pathway for advancing VSR technology. Our code will be publicly available at https://github.com/handsomewzy/FCA2.

Recognized as a fundamental task in computer vision, Image Quality Assessment (IQA) is pivotal for quantifying perceptual image quality and has consequently stimulated extensive research on assessment methodologies. Although previous methods have achieved certain progress, most models have focused solely on manipulating distorted images when extracting local features, without imposing constraints on the extraction range, thereby limiting the efficiency and accuracy of local feature extraction. Meanwhile, more advanced integration strategies need to be further investigated to improve existing fusion methods. To address these issues, motivated by the Human Visual System (HVS), which first perceives the global structure of an image and then captures local details for overall understanding, we propose an HVS-inspired blind image quality index with Prominent perception Learning and Multi-level Progressive Integration (PLMPI). First, prominence maps are derived from the distorted images. Then, the distorted images and the prominence maps are simultaneously fed into a dual-branch structure, where the global and local features are extracted separately. After that, local feature enhancement is performed. Finally, progressive integration is applied at each level of the dual-branch structure to enable feature fusion between the global and local representations, and a quality score is obtained through quality regression. A broad set of experiments conducted across eight challenging public datasets demonstrates that the proposed PLMPI can effectively handle both synthetic and authentic distortions, achieving competitive performance. Code and resources are available at https://github.com/CQUPT-HuBo90/PLMPI.

With the rapid development of text-to-image generation technology, accurately assessing the alignment between generated images and text prompts has become a critical challenge. Existing methods rely on Euclidean space metrics, neglecting the structured nature of semantic alignment, while lacking adaptive capabilities for different samples. To address these limitations, we propose HyperAlign, an adaptive text-to-image alignment assessment framework based on hyperbolic entailment geometry. First, we extract Euclidean features using CLIP and map them to hyperbolic space. Second, we design a dynamic-supervision entailment modeling mechanism that transforms discrete entailment logic into continuous geometric structure supervision. Finally, we propose an adaptive modulation regressor that utilizes hyperbolic geometric features to generate sample-level modulation parameters, adaptively calibrating Euclidean cosine similarity to predict the final score. HyperAlign achieves highly competitive performance on both single database evaluation and cross-database generalization tasks, fully validating the effectiveness of hyperbolic geometric modeling for image-text alignment assessment.

Visual stimuli reconstruction from EEG remains challenging due to fidelity loss and representation shift. We propose CognitionCapturerPro, an enhanced framework that integrates EEG with multi-modal priors (images, text, depth, and edges) via collaborative training. Our core contributions include an uncertainty-weighted similarity scoring mechanism to quantify modality-specific fidelity and a fusion encoder for integrating shared representations. By employing a simplified alignment module and a pre-trained diffusion model, our method significantly outperforms the original CognitionCapturer on the THINGS-EEG dataset, improving Top-1 and Top-5 retrieval accuracy by 25.9

Modeling visual perception in a manner consistent with human subjective evaluation has become a central direction in both video quality assessment (VQA) and broader visual understanding tasks. While free-energy-guided self-repair mechanisms-reflecting human observational experience-have proven effective in image quality assessment, extending them to VQA remains non-trivial. In addition, biologically inspired paradigms such as holistic perception, local analysis, and gaze-driven scanning have achieved notable success in high-level vision tasks, yet their potential within the VQA context remains largely underexplored. To address these issues, we propose EyeSimVQA, a novel VQA framework that incorporates free-energy-based self-repair. It adopts a dual-branch architecture, with an aesthetic branch for global perceptual evaluation and a technical branch for fine-grained structural and semantic analysis. Each branch integrates specialized enhancement modules tailored to distinct visual inputs-resized full-frame images and patch-based fragments-to simulate adaptive repair behaviors. We also explore a principled strategy for incorporating high-level visual features without disrupting the original backbone. In addition, we design a biologically inspired prediction head that models sweeping gaze dynamics to better fuse global and local representations for quality prediction. Experiments on five public VQA benchmarks demonstrate that EyeSimVQA achieves competitive or superior performance compared to state-of-the-art methods, while offering improved interpretability through its biologically grounded design. Our code will be publicly available at https://github.com/handsomewzy/EyeSim-VQA

Video Quality Assessment (VQA) technology is of significant importance for improving video transmission, storage, and processing. Although Convolutional Neural Networks (CNNs)-based and Transformer-based methods have achieved significant progress, they still suffer from some drawbacks. The previous methods treated video data as independent samples, thereby neglecting the close or distant relationships between different quality levels and consequently constraining the model’s discriminative ability; meanwhile, most existing fusion strategies utilize fixed architectures that lack the ability to adapt to the data for optimal integration, resulting in insufficient utilization of spatio-temporal information and limited expression capabilities of the fused features. To address the above issues, this article proposes a VQA method with In-Batch Contrastive Learning and Two-Phase Feature Fusion (IBCL-VQA). Firstly, the spatial features are extracted through two branches, which not only preserve the global semantics but also focus on the local regions. The temporal characteristics are obtained through a pre-trained video recognition model. Secondly, we propose an in-batch contrastive learning mechanism which, through the principles of maximizing intra-class similarity and minimizing inter-class similarity, combined with a dynamically adjusted penalty strategy, models the correlation between video quality levels. Thirdly, a two-phase feature fusion strategy, consisting of the Gated Spatio-temporal Attention Unit (GSTU) and the Adaptive Fusion Cell (AFC), is further proposed. The former achieves spatio-temporal feature fusion through dynamic weight allocation, and the latter adaptively integrates the features from the two branches based on data characteristics. Finally, a regression module outputs the quality score. Experimental results on five real-world VQA datasets demonstrate the superior performance of the IBCL-VQA. Furthermore, the strong generalizability is verified through cross-database testing. The code and pre-trained weights will be publicly available at: https://github.com/BoHu90/IBCL-VQA .

Existing object detection methods struggle to generalize across increasingly data domains while simultaneously adapting to the emergence of novel categories. To tackle this challenge, adaptive open-set object detection (AOOD) has been introduced, which employs supervised training on base categories within the source domain while enabling unsupervised adaptation to both base and novel categories in the target domain. However, existing AOOD approaches are still hindered by several limitations, including insufficient cross-domain feature representation, inter-category ambiguity in novel classes, and inherent feature bias toward the source domain. To overcome these issues, this paper proposes a category-level collaboration knowledge mining strategy designed to comprehensively exploit both inter-class and intra-class feature relationships across domains. Specifically, a clustering-based memory bank (CMB) is initially constructed to aggregate class prototype features, class auxiliary features, and intra-class disparity features, thereby embedding rich category-level knowledge into a unified memory structure. The CMB is iteratively updated through unsupervised clustering, which facilitates the modeling of intra-category relationships and enhances its capacity for cross-domain knowledge representation. Subsequently, a base-to-novel selection metric (BNSM) is designed to identify features corresponding to novel categories within the source domain by regulating the relationships between the novel categories and each base category. The selected features are then leveraged to initialize the object detector for the classification of novel categories. Finally, an adaptive feature assignment (AFA) strategy is introduced to transfer the learned category-level knowledge to the target domain, enabling the assignment of category labels to features. The memory bank is updated asynchronously with these assigned features to mitigate source domain bias. Extensive experiments conducted on diverse domain datasets demonstrate that the proposed method consistently outperforms state-of-the-art AOOD approaches, achieving performance gains of 1.1 to 5.5 mAP. Code is available at https://github.com/Jandsome/CCKM.

Efficiently and accurately recognizing interesting objects within the image and regressing bounding boxes to enclose them has been a persistent pursuit in object detection. However, existing detectors fail to achieve both aspects simultaneously due to insufficient task interaction and suboptimal classification behavior. To solve the problem, this paper proposes a novel detector with Efficient Asymmetric Progressive Semi-Decoupled Head (EAPSDH) and Harmonic Focal Loss (HFL). Specifically, we generalize the detection head into a progressive asymmetric paradigm that performs hierarchical and dynamically recalibrated interaction between classification and localization, enabling iterative mutual enhancement in an efficient manner beyond the prior designs. Meanwhile, HFL is proposed to improve classifier optimization by addressing the imbalance between positive and negative samples. HFL dynamically increases the loss weights of positive samples, amplifying their gradient contributions during classifier training, which significantly reduces classification error. By jointly improving task-specific feature representation and classification optimization, EAPSDH and HFL complement each other to alleviate the inconsistency between classification and localization performance, resulting in an efficient and accurate one-stage detector termed EADet. Experimental results on the MS COCO database demonstrate that EADet effectively mitigates the inconsistency between classification and localization performance. Furthermore, EADet achieves a strong trade-off between accuracy and speed, reaching 47.4 AP at 33.2 FPS on the MS COCO with ResNet-101 under the $2\times $ training schedule, demonstrating its effectiveness compared with recent state-of-the-art detectors. Code will be available at https://github.com/HB-X/EADet.

We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. We argue that this task represents a non-trivial challenge for the current generation of LMMs: it demands an unprecedented synergy between high-fidelity visual perception—to parse intricate spatial hierarchies and symbolic details—and precise generative expression—to synthesize syntactically sound and logically consistent code. Unlike traditional descriptive tasks, Omni-I2C requires a holistic understanding where any minor perceptual hallucination or coding error leads to a complete failure in visual reconstruction. Omni-I2C features 1130 meticulously curated samples, defined by its breadth across subjects, image modalities, and programming languages. By incorporating authentic user-sourced cases, the benchmark spans a vast spectrum of digital content—from scientific visualizations to complex symbolic notations—each paired with executable reference code. To complement this diversity, our evaluation framework provides necessary depth; by decoupling performance into perceptual fidelity and symbolic precision, it transcends surface-level accuracy to expose the granular structural failures and reasoning bottlenecks of current LMMs. Our evaluation reveals a substantial performance gap among leading LMMs; even state-of-the-art models struggle to preserve structural integrity in complex scenarios, underscoring that multimodal code generation remains a formidable challenge. Data and code are available at https://github.com/MiliLab/Omni-I2C.

With the rapid growth of large-scale AI, the demand for GPUs and other compute devices has surged, driving massive production of circuit-boards (e.g., PCBs). Consequently, accurate real-time defect detection has become more essential. Many recent methods adopt the vision foundation model DINO for defect detection due to its consistent feature representations across diverse environments and domains. However, image-level DINO features tend to emphasize semantics and global structure. In contrast, circuit-board defects are often visually similar across categories because they are small and texture-dominated. Meanwhile, many detection methods rely on global image-level feature aggregation. This weakens local instance-level defect cue modeling and degrades category discriminability. To enhance circuit-board defect detection, we propose DINO-PCB, which adopts a two-stage design comprising vision foundation model pretraining and dual-level knowledge distillation. In the pretraining stage, DINO is lightly fine-tuned together with a class projection head pretrained from scratch. Specifically, circuit-board images are fed into DINO to extract patch tokens. RoIAlign is then applied to obtain RoI features, which are passed through a class projection head to produce RoI embeddings. These RoI embeddings are optimized with an ArcFace loss to enhance category separability. In the distillation stage, the pretrained DINO and the class projection head are frozen as the teacher to supervise RT-DETR via dual-level knowledge distillation. The objective combines hierarchical image-level distillation between DINO patch tokens and RT-DETR encoder features, and contrastive instance-level distillation between RoI embeddings from the class projection head and RT-DETR decoder object queries. DINO-PCB enhances low-level texture and micro-structure modeling. Consequently, the RT-DETR detector achieves high accuracy across three circuit-board defect datasets without introducing additional inference latency.

Image dehazing, as a typical representative of low-level vision tasks, holds significant importance, prompting the proposal of numerous dehazing methods. Given that none of these methods can perfectly restore all hazy images, developing robust objective assessment methods remains a critical research priority in this field. Most existing objective assessment methods for dehazed images fail to fully leverage haze-related features and instead rely on basic dehazed images alone. Additionally, they often neglect effective feature fusion, which severely limits model performance. To address these issues, we propose a dark channel difference driven dehazed image quality assessment with progressive injection (DCPI-DQA). First, a dark channel difference map is generated using both the dehazed image and the hazy image. Then, a two-stream multi-scale feature extraction network is employed to extract haze density features and dehazing-aware features. During the feature extraction process, haze density features are progressively injected into the dehazing aware features using the proposed progressive injection mechanism. Finally, multi-level fusion is applied for feature integration, and the quality score is obtained through quality regression. Experiments conducted on four publicly available dehazed image datasets demonstrate that our method achieves state-of-the-art performance. Furthermore, cross-dataset experiments indicate that the proposed method exhibits strong generalization. Code and resources are available at https://github.com/BoHu90/DCPI-DQA https://github.com/BoHu90/DCPI-DQA.

User-generated content (UGC) videos often exhibit complex distortions and diverse content, posing significant challenges for traditional video quality assessment (VQA) methods. Approaches that directly merge distortion and semantic information risk feature conflicts and the loss of details. In addition, simple concatenation of spatiotemporal features fails to capture vital interactions, limiting predictive accuracy. Motivated by these challenges, this paper proposes a Multi-level Distortion Adaptation and Spatiotemporal Cross-Attention Fusion framework for VQA, named MACA-VQA. Specifically, a novel multi-level adaptive strategy progressively incorporates distortion information into each Transformer layer of the CLIP model, enabling layer-wise fusion of semantic and distortion features. Furthermore, a newly introduced cross-attention fusion mechanism dynamically integrates spatiotemporal features, capturing complex, multidimensional interactions. Extensive experiments demonstrate that MACA-VQA achieves state-of-the-art performance on multiple public datasets, validating its effectiveness and robustness in both intra-dataset and inter-dataset scenarios. The source code is available at https://github.com/BoHu90/MACA-VQA

Wide-angle video is favored for its wide viewing angle and ability to capture a large area of scenery, making it an ideal choice for sports and adventure recording. However, wide-angle video is prone to deformation, exposure and other distortions, resulting in poor video quality and affecting the perception and experience, which may seriously hinder its application in fields such as competitive sports. Up to now, few explorations focus on the quality assessment issue of wide-angle video. This deficiency primarily stems from the absence of a specialized dataset for wide-angle videos. To bridge this gap, we construct the first Multi-annotated and multi-modal Wide-angle Video quality assessment (MWV) dataset. Then, the performances of state-of-the-art video quality methods on the MWV dataset are investigated by inter-dataset testing and intra-dataset testing. Experimental results show that these methods impose significant limitations on their applicability.

In this letter, we propose a deep learning-based iterative residual encoder-decoder method (IRED), which provides an efficient deep learning framework for electromagnetic modeling over a broad frequency band. The method leverages the connection between traditional iterative method and iterative neural networks, making it more versatile by processing the imaginary and real parts of complex numbers in two distinct channels. In the neural network, each iteration layer consists of an encoder-decoder neural network designed to solve the residual at each iterative step. In a given region, the input of the IRED combines the initial guess of the total field at any frequency across a broadband with the contrast distribution of the scatterer, while the corresponding output is the total electromagnetic field. The effectiveness of this method is validated through the 2-D volume integral equation. Numerical results demonstrate that the method operates effectively over a broadband, achieving higher accuracy and faster efficiency compared to traditional algorithms.

Recently, AI-generated images (AIGIs), synthesized based on initial textual prompts, have attracted widespread attention. However, due to limitations in current generation techniques, these images often exhibit degraded perceptual quality and semantic misalignment with the guiding prompts. Therefore, evaluating both perceptual quality and text-to-image alignment is essential for optimizing the performance of generative models. Existing methods design textual prompts solely based on the initial prompt for both perceptual and alignment quality tasks, and compute only coarse-grained similarity between the designed prompt and the generated image. However, such task-agnostic prompts overlook the distinctions between the perceptual and alignment quality tasks, and coarse-level similarity fails to capture semantic details, leading to suboptimal evaluation performance. To address these challenges, we propose a novel AIGI quality assessment framework, termed TPMS, which incorporates task-specific prompt and multi-granularity similarity computation. The task-specific prompt constructs dedicated prompts for perceptual and alignment quality respectively, allowing the model to capture distinct quality cues tailored to each evaluation task. Multi-granularity similarity measures the coarse-level similarity between the generated image and task-specific prompts to capture global quality characteristics, and the fine-level similarity between the generated image and the initial prompt to enhance semantic detail awareness. By integrating these two complementary similarities, TPMS enables precise and robust quality prediction. Extensive experiments on four widely-used AIGI quality benchmarks validate the effectiveness and superiority of the proposed framework.

Affordance detection aims to jointly address the fundamental "what-where-how" challenge in embodied AI by understanding "what" an object is, "where" the object is located, and "how" it can be used. However, most affordance learning methods focus solely on "how" objects can be used while neglecting the "what" and "where" aspects. Other affordance detection methods treat object detection and affordance learning as two independent tasks, lacking effective interaction and real-time capability. To overcome these limitations, we introduce YOLO Affordance (YOLOA), a real-time affordance detection model that jointly handles these two tasks via a large language model (LLM) adapter. Specifically, YOLOA employs a lightweight detector consisting of object detection and affordance learning branches refined through the LLM Adapter. During training, the LLM Adapter interacts with object and affordance preliminary predictions to refine both branches by generating more accurate class priors, box offsets, and affordance gates. Experiments on our relabeled ADG-Det and IIT-Heat benchmarks demonstrate that YOLOA achieves state-of-the-art accuracy (52.8 / 73.1 mAP on ADG-Det / IIT-Heat) while maintaining real-time performance (up to 89.77 FPS, and up to 846.24 FPS for the lightweight variant). This indicates that YOLOA achieves an excellent trade-off between accuracy and efficiency.

Multi-modal image aesthetic assessment (MIAA) has gained significant progress, by predicting aesthetic based on both an image and its text comments. However, most MIAA methods are not applicable, when there are no text comments available. To combat this challenge, we propose a unified image aesthetic assessment (IAA) framework, termed AesFormer, by using mixtures of vision-language Transformers. Specially, AesFormer first learns aligned image-text representations through contrastive learning, and uses a vision-language head for MIAA prediction. Afterward, we propose a multi-level adaptation (MLA) method to adapt the learned MIAA model to the case without text comments, and use another vision head for vison-only IAA (VIAA) prediction. Extensive experimental results show that AesFormer significantly outperforms previous methods in both MIAA and VIAA tasks, on diverse benchmarking datasets. Our code has been released at: https://github.com/AiArt-Gao/AesFormer

Current object detectors often suffer performance degradation when applied to cross-domain scenarios, particularly under challenging visual conditions such as nighttime scenes. This is primarily due to the I3 problems: Inadequate sampling of instance-level features, Indistinguishable feature representation across domains and Inaccurate generation for identical category participation. To address these challenges, we propose a domain-adaptive detection framework that enables robust generalization across different visual domains without introducing any additional inference overhead. The framework comprises three key components. Specifically, the centerness-category consistency sampler alleviates inadequate sampling by selecting representative instance-level features, while the paired centerness consistency loss enforces alignment between classification and localization. Second, VLM-based orthogonality enhancement leverages frozen vision-language encoders with an orthogonal projection loss to improve cross-domain feature distinguishability. Third, hallucination feature generator synthesizes robust instance-level features for missing categories, ensuring balanced category participation across domains. Extensive experiments on multiple datasets covering various domain adaptation and generalization settings demonstrate that our method consistently outperforms state-of-the-art detectors, achieving up to 5.5 mAP improvement, with particularly strong gains in nighttime adaptation.

Weakly supervised temporal sentence grounding aims to identify semantically relevant video moments in an untrimmed video corresponding to a given sentence query without exact timestamps. Neuropsychology research indicates that the way the human brain handles information varies based on the grammatical categories of words, highlighting the importance of separately considering nouns and verbs. However, current methodologies primarily utilize pre-extracted video features to reconstruct randomly masked queries, neglecting the distinction between grammatical classes. This oversight could hinder forming meaningful connections between linguistic elements and the corresponding components in the video. To address this limitation, this paper introduces the dual semantic reconstruction network (DSRN) model. DSRN processes video features by distinctly correlating object features with nouns and motion features with verbs, thereby mimicking the human brain's parsing mechanism. It begins with a feature disentanglement module that separately extracts object-aware and motion-aware features from video content. Then, in a dual-branch structure, these disentangled features are used to generate separate proposals for objects and motions through two dedicated proposal generation modules. A consistency constraint is proposed to ensure a high level of agreement between the boundaries of object-related and motion-related proposals. Subsequently, the DSRN independently reconstructs masked nouns and verbs from the sentence queries using the generated proposals. Finally, an integration block is applied to synthesize the two types of proposals, distinguishing between positive and negative instances through contrastive learning. Experiments on the Charades-STA and ActivityNet Captions datasets demonstrate that the proposed method achieves state-of-the-art performance.