Although saliency maps can highlight important regions to explain the reasoning behind image classification in artificial intelligence (AI), the meaning of these regions is left to the user's interpretation. In contrast, concept-based explanations decompose AI predictions into human-understandable concepts, clarifying their contributions. However, few methods can simultaneously reveal what concepts an image classifier learns, which regions are associated with them, and how they contribute to predictions.We propose a novel concept-based explanation method, Concept-based Explanation via Fusion of Activation Maps (CE-FAM). It employs a branched network that shares activation maps with an image classifier and learns to mimic the embeddings of a Vision and Language Model (VLM). The branch network predicts concepts in an image, and their corresponding regions are represented by a weighted sum of activation maps, with weights given by the gradients of the concept prediction scores. Their contributions are quantified based on their impact on the image classification score. Our method provides a general framework for identifying the concept regions and their contributions while leveraging VLM knowledge to handle arbitrary concepts without requiring an annotated dataset. Furthermore, we introduce a novel evaluation metric to assess the accuracy of the concept regions. Our qualitative and quantitative evaluations demonstrate our method outperforms existing approaches and excels in zero-shot inference for unseen concepts.
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Intelligent Document Processing (IDP) is critical for unlocking actionable insights from the vast volume of unstructured documents like invoices and medical reports, yet its promise is often unfulfilled as its implementation is typically hindered by significant technical barriers. Traditional IDP systems require deep expertise in programming, machine learning, and intricate model fine-tuning, creating a dependency on specialized data science teams. This effectively sidelines domain experts-the very individuals who possess the critical contextual understanding of the documents-thereby limiting the agility and accuracy of workflow automation. This paper introduces IDPFlow, a novel framework to unify a no-code, user-centric interface with a sophisticated, tool-augmented agentic architecture for end-to-end multimodal document processing, empowering experts such as business analysts and legal professionals to independently build and deploy sophisticated workflows without writing any code. IDPFlow is built upon a powerful agentic architecture, which intelligently utilize a versatile toolkit to execute a range of sophisticated IDP tasks. This toolkit enables a spectrum of high-precision IDP tasks such as multi-class document classification, Document visual question answering (Doc-VQA), key information extraction from text, tables, and checkboxes and long-document summarization. The core of IDPFlow is its dynamic agentic workflow, which redefines user interaction. Upon document upload, the agentic system instantly analyzes the content, classifying sub-documents and proactively suggesting a comprehensive data schema relevant to the use case, shifting the user's role from workflow builder to supervisor. This initial workflow is not static, it can be refined in real-time through simple, conversational instructions, enabling true business agility. Furthermore, the agentic intelligence extends to reusability, allowing existing workflows to be intelligently adapted for new, related tasks, dramatically reducing development time for subsequent use cases. For particularly complex tasks involving long or dense documents, the agentic system can leverage a specialized Multimodal Retrieval-Augmented Generation (MMRAG) pipeline to overcome the context window limitations of standard LLMs. This pipeline utilizes the ColPali model, which excels at generating unified multimodal embeddings, ensuring robust and accurate information retrieval from both textual content and embedded images or diagrams. To foster user trust and ensure verifiability, IDPFlow incorporates a grounded traceback citation mechanism that automatically highlights the precise document segments from which the agent derived its responses, making all outputs transparent and easily auditable. We highlight three key advantages of the framework: 1) Accessibility via an intuitive interface for domain experts; 2) Deep Adaptability and Reusability through dynamic agentic refinement and extensible tools; and 3) Trustworthiness rooted in a verifiable RAG pipeline and granular citation. The framework is projected to reduce end-to-end workflow creation time by 60-70% compared to traditional methods. Its unique combination of a no-code interface and a tool-augmented agentic architecture bridges the gap between technical complexity and domain expertise, accelerating the deployment of powerful, transparent, and scalable IDP solutions across industries.
Recent advances in video action recognition have achieved remarkable performance in coarse-grained macro-action classification by leveraging large-scale visual backbones and transformer architectures. However, extending these successes to fine-grained micro-action recognition remains a fundamental challenge due to the subtlety, brevity, and low motion intensity of micro-actions. In this paper, we propose a high-capacity framework for micro-action recognition, enhancing both representation learning and decision robustness. We scale to large-scale backbones using the VideoMAEv2 Giant model, enabling the extraction of finer spatial-temporal features. A Temporal-Spatial Connector (TSC) is introduced to dynamically highlight discriminative temporal frames and spatial regions, strengthening the model's focus on subtle motion cues critical for micro-action identification. To stabilize optimization and fully exploit the capacity of large models, we design a four-phase progressive training strategy, encompassing linear probing, full fine-tuning, connector-specific optimization, and classifier head refinement. Furthermore, we propose a novel ensemble decision mechanism that integrates Top-K predictions from diverse models via a Large Language Model (LLM), enhancing prediction consistency and robustness through multimodel consensus. Our method achieves an F1mean of 76.54% on the MA-52 dataset, ranking 3rd in the 2025 Micro-Action Analysis Grand Challenge and advancing the state of the art in fine-grained video understanding.
Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.
We present a unified vision-language framework tailored for ENT endoscopy image analysis that simultaneously tackles three clinically-relevant tasks: image classification, image-to-image retrieval, and text-to-image retrieval. Unlike conventional CNN-based pipelines that struggle to capture cross-modal semantics, our approach leverages the CLIP ViT-B/16 backbone and enhances it through Low-Rank Adaptation, multi-level CLS token aggregation, and spherical feature interpolation. These components collectively enable efficient fine-tuning on limited medical data while improving representation diversity and semantic alignment across modalities. To bridge the gap between visual inputs and textual diagnostic context, we introduce class-specific natural language prompts that guide the image encoder through a joint training objective combining supervised classification with contrastive learning. We validated our framework through participation in the ACM MM'25 ENTRep Grand Challenge, achieving 95% accuracy and F1-score in classification, Recall@1 of 0.93 and 0.92 for image-to-image and text-to-image retrieval respectively, and MRR scores of 0.97 and 0.96. Ablation studies demonstrated the incremental benefits of each architectural component, validating the effectiveness of our design for robust multimodal medical understanding in low-resource clinical settings.
In recent years, Multimodal Sentiment Analysis (MSA) has attracted growing attention for its ability to interpret human emotions by integrating information across multiple modalities. Multimodal Conversational Aspect-based Sentiment Analysis (MCABSA) extends this research frontier by incorporating multi-party conversational contexts and requiring comprehensive extraction of sentiment elements. MCABSA presents substantial challenges, including the need to understand complex conversational contexts, integrate heterogeneous multimodal signals, and identify causal reasoning at the cognitive level. To address these challenges, we propose a two-stage Full Fine-tuning and LLM Post-processing (FLP) framework. In the first stage, we develop a multimodal caption-enhanced full fine-tuning pipeline that performs structured extraction of sextuples and sentiment flip tuples. The second stage introduces paraphrase-based sextuple verification to identify and filter low-quality sextuples for Panoptic Sentiment Sextuple Extraction (Task-1), while implementing trigger classification with a distribution alignment mechanism to determine trigger types for sentiment flipping and enhance output consistency for Sentiment Flipping Analysis (Task-2). Comprehensive experiments on both MCABSA challenge subtasks demonstrate the effectiveness of our approach, achieving 1st place on Task-1 and 3rd place on Task-2.
This paper presents our solution for the Micro-Expression Visual Question Answering (ME-VQA) task in the 2025 Facial Micro-Expression Grand Challenge (MEGC). To address the limitations of traditional micro-expression recognition (MER) methods in dynamic modeling, semantic interpretation, and natural language interaction, we propose Emotion-Qwen-VL, a fully fine-tuned multimodal large language model tailored for micro-expression understanding. Specifically, we construct a structured, instruction-based QA dataset that reformulates emotion categories and action unit (AU) annotations into natural language QA pairs, covering classification, AU detection, and causal reasoning. We then adopt a full-parameter fine-tuning strategy to guide Qwen2.5-VL in learning fine-grained temporal facial dynamics and their emotional semantics. Experimental results on the MEGC 2025 test set demonstrate that Emotion-Qwen-VL outperforms strong baselines such as Qwen2.5-VL and QVQ across multiple dimensions, including coarse-grained macro-expression classification, fine-grained micro-expression classification, and language generation. Our results highlight the effectiveness, interpretability, and adaptation potential of large models in micro-expression understanding. The code is available at: https://github.com/2308623956/MEGC2025.
We present a Collaborative Agent-Based Framework for Multi-Image Reasoning. Our approach tackles the challenge of interleaved multimodal reasoning across diverse datasets and task formats by employing a dual-agent system: a language-based PromptEngineer, which generates context-aware, task-specific prompts, and a VisionReasoner, a large vision-language model (LVLM) responsible for final inference. The framework is fully automated, modular, and training-free, enabling generalization across classification, question answering, and free-form generation tasks involving one or multiple input images. We evaluate our method on 18 diverse datasets from the 2025 MIRAGE Challenge (Track A), covering a broad spectrum of visual reasoning tasks including document QA, visual comparison, dialogue-based understanding, and scene-level inference. Our results demonstrate that LVLMs can effectively reason over multiple images when guided by informative prompts. Notably, Claude 3.7 achieves near-ceiling performance on challenging tasks such as TQA (99.13% accuracy), DocVQA (96.87%), and MMCoQA (75.28 ROUGE-L). We also explore how design choices-such as model selection, shot count, and input length-influence the reasoning performance of different LVLMs.
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.
Recent advancements in computer vision and Natural Language Processing (NLP) have transformed how machines interpret visual and textual information. While traditional image processing techniques rely on pixel-based analysis and feature extraction, and NLP leverages self-attention mechanisms for contextual understanding, the potential of viewing images as a language remains largely unexplored. In this work as a proof-of-concept, we introduce a novel ''Vision Language'' framework, transforming image pixels into sequences of alphanumeric characters, effectively treating images as ''text.'' This unique representation enables the direct application of NLP techniques to computer vision tasks, such as classification and segmentation, by utilizing text-based models for image analysis. We demonstrate this approach by adapting text classification methods for image classification on benchmark datasets. Furthermore, we construct a domain-specific medical language from the MedMNIST dataset, spanning 11 medical imaging sub-datasets, illustrating how a unified language can effectively generalize across distinct datasets. We create a broad, generalized language from ImageNet, leveraging approximately 1.3 million images to show that a language derived from extensive image data can simplify and unify image processing across tasks and datasets. Our findings reveal a promising cross-disciplinary approach that bridges computer vision and NLP. Our code is publicly available at https://github.com/mustakinalam/The-Birth-of-Vision-Language.
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. However, these developments also raise significant concerns regarding data privacy and copyright. In response, training data detection has emerged as a critical task for identifying unauthorized data usage in model training. To better understand the vulnerability of autoregressive image generative models to such detection, we conduct the first study that applies membership inference to this domain. Our approach comprises two key components: implicit classification and an adaptive score aggregation strategy. First, we compute the implicit token-wise classification score within the query image. Then we propose an adaptive score aggregation strategy to acquire a final score, which places greater emphasis on the tokens with lower scores. A higher final score indicates that the sample is more likely to be involved in the training set. Extensive experiments demonstrate the superiority of our method over those designed for LLMs, in both class-conditional and text-to-image scenarios. Moreover, our approach exhibits strong robustness and generalization under various data transformations. Furthermore, sufficient experiments suggest two novel key findings: (1) A linear scaling law on membership inference, exposing the vulnerability of large foundation models. (2) Training data from scale-wise visual autoregressive models is easier to detect than other autoregressive paradigms. Our code is available at https://github.com/Chrisqcwx/ImageAR-MIA.
Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, underscoring different capabilities termed time series understanding in this paper. While GPT-style models have been positioned as foundation models for time series forecasting, the BERT-style architecture, which has made significant advances in natural language understanding, has not been fully unlocked for time series understanding, possibly attributed to the undesirable dropout of essential elements of BERT. In this paper, inspired by the shared multi-granularity structure between multivariate time series and multisentence documents, we design TimesBERT to learn generic representations of time series including temporal patterns and variate-centric characteristics. In addition to a natural adaptation of masked modeling, we propose a parallel task of functional token prediction to embody vital multi-granularity structures. Our model is pre-trained on 260 billion time points across diverse domains. Leveraging multi-granularity representations, TimesBERT achieves state-of-the-art performance across four typical downstream understanding tasks, outperforming task-specific models and language pre-trained backbones, positioning it as a versatile foundation model for time series understanding.
Accurate prediction of placental diseases via whole slide images (WSIs) is critical for preventing severe maternal and fetal complications. However, WSI analysis presents significant computational challenges due to the massive data volume. Existing WSI classification methods encounter critical limitations: (1) inadequate patch selection strategies that either compromise performance or fail to sufficiently reduce computational demands, and (2) the loss of global histological context resulting from patch-level processing approaches. To address these challenges, we propose an Efficient multimodal framework for Patient-level placental disease Diagnosis, named EmmPD. Our approach introduces a two-stage patch selection module that combines parameter-free and learnable compression strategies, optimally balancing computational efficiency with critical feature preservation. Additionally, we develop a hybrid multimodal fusion module that leverages adaptive graph learning to enhance pathological feature representation and incorporates textual medical reports to enrich global contextual understanding. Extensive experiments conducted on both a self-constructed patient-level Placental dataset and two public datasets demonstrating that our method achieves state-of-the-art diagnostic performance. The code is available at https://github.com/ECNU-MultiDimLab/EmmPD.
Aesthetic Image Cropping (AIC) aims to improve the visual appeal of images by removing redundant content while preserving attractive elements. Despite the encouraging progresses achieved in data-driven approaches, most existing models struggle to understand user intentions, particularly for diversified scenes with multiple subjects. Moreover, they can only provide cropping results without explanations, which further restricts their usability in real-world applications. Motivated by the above facts, we introduce InstructCrop : a multimodal large language model (MLLM)-based AIC framework, which can understand user instructions and provide explanatory reasons for cropping results. Specifically, we first build a multimodal Image Cropping Instruction Tuning (ICIT) dataset through a cost-effective paradigm by generating high-quality instruction tuning data based on the existing cropping datasets. Then, we embed dynamic domain knowledge into the cropping model by integrating cropping-aware experts of aesthetic assessment and composition classification. Finally, we adapt MLLMs to generate the cropping results and corresponding explanations. Quantitative and qualitative experiments on three benchmark datasets demonstrate that InstructCrop enables effective and interpretable image cropping, which aligns better with user intentions. Data and code are available at https://github.com/sxfly99/InstructCrop.
Due to data collection limitations and annotation reliability, the lack of multi-view data will weaken the comprehensive understanding of samples, and incomplete multi-view multi-label classification faces severe challenges. To address this problem, we propose a multi-view complementary learning framework MC-IVLC to explore the complementary information between views fully. Specifically, MC-IVLC proposes compensating for the collapse of reconstructed features and explicitly using fused features as supervisory signals to guide the completion of missing views. In addition, MC-IVLC fully utilizes the complementary relationship between views from both instance and semantic levels. Instance-level contrastive learning aims to promote the clustering of similar features in the same view to enhance the complementarity of cross-view features. Semantic-level contrastive learning utilizes pseudo-labels to infer missing labels in label embeddings. It combines pseudo-label semantic information with feature embeddings to guide the semantic relevance of cross-view features. Finally, MC-IVLC explicitly encodes view identity and introduces a view-label prediction loss term to enhance the perception of view information and align single views and multiple views, further exploring the intrinsic connection between views and labels. We conduct experiments on five widely used datasets. Experimental results show that MC-IVLC achieves excellent performance compared with state-of-the-art methods. Ablation studies further validate the effectiveness of each component.
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects. Current CLIP-based methods in CZSL, despite their advancements, often fail to effectively understand and link the attributes and objects due to inherent limitations in CLIP's pretraining mechanisms. To address these shortcomings, this paper introduces a novel framework, Understanding and Linking Attributes and Objects (ULAO) in CZSL, which comprises two innovative modules. The Understanding Attributes and Objects (UAO) module improves primitive understanding by sequential primitive prediction and leveraging recognized objects as contextual hints for attribute classification. Concurrently, the Linking Attributes and Objects (LAO) module improves the attribute-object linkage understanding through a new contrastive learning strategy that incorporates tailored hard negative generation and adaptive loss adjustments. We demonstrate our model's superiority by showcasing its state-of-the-art performance across three benchmark datasets in both Closed-World (CW) and Open-World (OW) scenarios.
Multi-label classification has recently demonstrated promising performance through CLIP-based unsupervised learning. However, existing CLIP-based approaches primarily focus on object-centric features, which limits their ability to capture rich contextual dependencies between objects and their surrounding scenes. In addition, the vision transformer architecture of CLIP exhibits a bias toward the most prominent object, often failing to recognize small or less conspicuous objects precisely. To address these limitations, we propose Background-Aware CLIP-GCN (BAC-GCN), a novel framework that explicitly models class-background interactions and is designed to capture fine-grained visual patterns of small objects effectively. BAC-GCN is composed of three key components: (i) a Similarity Kernel that extracts patch-level local features for each category (i.e., class and background), (ii) a CLIP-GCN that captures relational dependencies between local-global and class-background features, and (iii) a Re-Training for Small Objects (ReSO) strategy that enhances the representation of small and hard-to-learn objects by learning their distinctive visual characteristics. Therefore, our method facilitates a deeper understanding of complex visual contexts, enabling the model to make decisions by leveraging diverse visual cues and their contextual relationships. Extensive experiments demonstrate that BAC-GCN achieves state-of-the-art performance on three benchmark multi-label datasets: VOC07, COCO, and NUS, validating the effectiveness of our approach. The project page is available at: https://github.com/yonghyeonjo46/BAC-GCN.
Despite recent progress in decoding static images from brain activity, reconstructing dynamic visual experiences from EEG signals remains challenging due to the complex temporal dynamics involved. Current approaches primarily rely on pre-trained video generation models while failing to fully leverage the rich temporal-spatial information embedded in EEG signals for video synthesis. This paper proposes MINDEV Multi-modal Integrated Neural DEcoding and Visualization), a framework that places EEG signal processing at the core of video reconstruction. We introduce three key technical contributions: (1) a dual-branch feature extractor that captures both temporal dynamics and spatial relationships in EEG signals, (2) an EEG-driven semantic bridge that uses neural patterns to guide language model interpretation, and (3) a multi-modal video synthesis pipeline where EEG features lead the generation process while semantic guidance provides refinement. Our framework prioritizes the millisecond-level temporal resolution of EEG signals, using them to drive both visual content generation and semantic understanding. Evaluated on the SEED-DV dataset, MINDEV demonstrates superior performance with a semantic classification accuracy of 93.2% and a structural similarity index (SSIM) of 0.4777, establishing a new state-of-the-art for EEG-based video reconstruction. Our code is publicly available at https://github.com/HHarr1son/MINDEV.
Multi-label image classification requires simultaneously recognizing multiple objects with complex interdependencies. While existing attention-based methods are prominent, their performance is hampered by two forms of representation entanglement: 1) Spatial entanglement, where contextual interference from backgrounds and co-occurring objects confuses specific object representations; 2) Semantic entanglement, where models overfit label co-occurrence priors, thereby impairing a genuine semantic understanding of the image. To address these challenges, we propose an Object-Purified Representation Learning framework. Concretely, for spatial entanglement, we propose the Spatial-wise Representation Purification Module that employs Spatial-Purified Attention to eliminate object-irrelevant feature activations for contextual interference reduction, combined with Spatial-Aware Supervision to enhance object perception capability. For semantic entanglement, we develop the Semantic-wise Association Purification Module that synergistically integrates our proposed average message with the original co-occurrence-based message. This design effectively models co-occurrence relationships while preventing their overemphasis. Furthermore, we design the Bidirectional Representation Refinement Module to efficiently enhance representations, further boosting classification performance. Extensive experiments on multiple benchmark datasets with different configurations demonstrate that our proposed method achieves state-of-the-art performance.
Open vocabulary image segmentation tackles the challenge of recognizing dynamically adjustable, predefined novel categories at inference time by leveraging vision-language alignment. However, existing paradigms typically perform class-agnostic region segmentation followed by category matching, which deviates from the human visual system's process of recognizing objects based on semantic concepts, leading to poor alignment between region segmentation and object concepts. To bridge this gap, we propose a novel Cognition-Inspired Framework for open vocabulary image segmentation that emulates the human visual recognition process: first forming a conceptual understanding of an object, then perceiving its spatial extent. The framework consists of three core components: (1) A Generative Vision-Language Model (G-VLM) that mimics human cognition by generating object concepts to provide semantic guidance for region segmentation. (2) A Concept-Aware Visual Enhancer module that fuses textual concept features with global visual representations, enabling adaptive visual perception based on object concepts. (3) A Cognition-Inspired Mask Decoder that integrates local instance features with G-VLM-provided semantic cues, allowing selective classification over a subset of relevant categories. Extensive experiments demonstrate that our framework achieves significant improvements, reaching 27.2 PQ, 17.0 mAP, and 35.3 mIoU on A-150. It further attains 56.2, 28.2, 15.4, 59.2, 18.7, and 95.8 mIoU on Cityscapes, Mapillary Vistas, A-847, PC-59, PC-459, and PAS-20, respectively. In addition, our framework supports vocabulary-free image segmentation, offering enhanced flexibility in recognizing unseen categories.