Group re-identification (G-ReID) attempts to recognize human groups across multiple camera perspectives. It is a challenging task due to occlusion, perspective variation, and illumination change. While Person Attribute Recognition (PAR) methods have shown robustness under similar challenges, yet their potential in G-ReID remains unexplored. Though existing G-ReID datasets are well-crafted, however, they lack person-level attribute annotations. This restricts G-ReID methods to explore attribute-based matching, essentially limiting their capability of multi-modal analysis. In this work, we bridge this gap by utilizing person-level attributes for group-level re-identification. We introduce PAG-ReID (Person Attribute based Group Re-identification), a large-scale dataset constructed by combining three popular G-ReID datasets: CM-Group, Road Group, and CUHK-SYSU-Group. PAG-ReID includes 19K group images encompassing 2,148 groups and 5,504 unique person IDs. At the person level, it provides 25K individual images, each annotated with 19 diverse human attributes, resulting in nearly 475K fine-grained annotations. Next, we propose an effective baseline (CLIP-based) that transforms attribute information into natural language descriptions, enabling joint multi-modal (visual-textual) reasoning for PAR as well as G-ReID tasks. Experiments demonstrate the effectiveness of our approach, setting a new direction for person attribute-centric group re-identification. To our knowledge, this is the first work to unify G-ReID and PAR in a single multi-modal framework. PAG-ReID can be found at https://github.com/draxler1/PAG-ReID.
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The performance of current Vision-Language Tracking (VLT) models is constrained by the limited diversity and quantity of labeled data. Compared to constructing large-scale datasets, data augmentation offers a more cost-saving strategy for VLT by synthesizing new samples from existing data, rather than generating them from scratch. However, conventional techniques like rotation and flipping may disrupt scene composition, causing conflicts between visual layouts and textual annotations. Recent advances in generative models have inspired the use of synthetic videos for data augmentation. Yet, existing approaches fail to address the core concerns of data augmentation in VLT (shown in Fig. 1)-target location accuracy, text-video consistency, and video content coherency. To bridge the gap, we propose Gen4Track, a tuning-free data augmentation framework that leverages the self-correcting mechanism to dynamically generate high-quality video data with annotations. Our approach involves (1) optimizing the attention calculations in a frozen text-to-image diffusion model to synthesize coherent videos that satisfy specific conditions (e.g., spatial location, category, color, and style), and (2) implementing a self-correcting mechanism based on a Large Language Model (LLM) to improve text-video consistency. During video augmentation, we propose content-coherent self-attention and location-enhanced cross-attention mechanisms, ensuring that image-level editings are accurately and coherently propagated throughout the video. Then, with the goal of maximizing text-video consistency, we iteratively refine the augmentation instruction with our designed self-correcting mechanism for a more aligned video. Extensive experiments validate that Gen4Track significantly boosts the performance of SOTA VLT models (achieving improvements of up to 3.2% in SUC and 3.5% in PRE), opening a new chapter of training Vision-Language trackers with synthetic videos rather than manually annotated data.
We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets while providing compatibility with various input-output interfaces required across different task scenarios. Current systems cannot meet these requirements, as they typically utilize task-specific architecture trained on narrow data domains with limited semantic coverage. Our study addresses these limitations from two aspects: data and modeling. We first introduce an automatic data engine that enjoys significantly better scalability compared to previous human annotation or rule-based approaches. It has enabled us to create the largest dataset of its kind to date, comprising 270K image-text-mask triplets covering an unprecedented range of diverse semantic categories and attribute specifications. Based on this data foundation, we further propose a task unification paradigm that centers around referring expression segmentation. It effectively handles a wide range of vision-centric perception tasks, including classification, detection, segmentation, grounding, etc, using a single model without any task-specific heads. Combining these innovations on data and modeling, we present RemoteSAM, a foundation model that establishes new SoTA on several earth observation perception benchmarks, outperforming other foundation models such as Falcon, GeoChat, and LHRS-Bot with significantly higher efficiency. Models and data are publicly available at https://github.com/1e12Leon/RemoteSAM.
Federated Prompt Tuning (FPT) integrates large pre-trained Vision Transformers (ViT) into Federated Learning (FL) by leveraging Visual Prompt Tuning (VPT), achieving state-of-the-art performance with enhanced efficiency across various visual downstream tasks. However, data heterogeneity, such as feature shift and class imbalance, limits prompts' transferability and robustness in FPT. Existing methods primarily focus on generalized FPT (GFPT) or personalized FPT (PFPT), while only a few methods make initial attempts to integrate both approaches. In this paper, we propose a new FPT framework, dubbed DualFPT, which handles data heterogeneity from both generalized and personalized perspectives. Specifically, DualFPT divides the learnable prompts into global and local prompts to jointly capture general and client-specific information, achieving the harmonization of GFPT and PFPT. The generalization of DualFPT is realized by the Feature Sharing (FS) mechanism, which effectively narrows the distribution gap by allowing clients to securely share a portion of sensitive features. The key feature for improving personalization is the Prompt Composition Scheme (PCS), which weights local prompts with distribution similarity to generate composite prompts, thus achieving automatic distribution adaptation. Extensive experiments under feature shift and class imbalance scenarios demonstrate the superior performance of DualFPT. On DomainNet and CIFAR-100 (D (0.1)), DualFPT surpasses SGPT by 4.35% and 5.89% for generalization along with 7.89% and 9.09% for personalization. Ablation studies further validate the effectiveness, efficiency, and security of DualFPT.
Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that remain the same across all input instances. We observe that this strategy results in sub-optimal performance due to high variance in downstream datasets. To address this challenge, we propose Visual Instance-aware Prompt Tuning (ViaPT), which generates instance-aware prompts based on each individual input and fuses them with dataset-level prompts, leveraging Principal Component Analysis (PCA) to retain important prompting information. Moreover, we reveal that VPT-Deep and VPT-Shallow represent two corner cases based on a conceptual understanding, in which they fail to effectively capture instance-specific information, while random dimension reduction on prompts only yields performance between the two extremes. Instead, ViaPT overcomes these limitations by balancing dataset-level and instance-level knowledge, while reducing the amount of learnable parameters compared to VPT-Deep. Extensive experiments across 34 diverse datasets demonstrate that our method consistently outperforms state-of-the-art baselines, establishing a new paradigm for analyzing and optimizing visual prompts for vision transformers.
Human-Object Interaction (HOI) detection aims to identify humans and objects within images and interpret their interactions. Existing HOI methods rely heavily on large datasets with manual annotations to learn interactions from visual cues. These annotations are labor-intensive to create, prone to inconsistency, and limit scalability to new domains and rare interactions. We argue that recent advances in Vision-Language Models (VLMs) offer untapped potential, particularly in enhancing interaction representation. While prior work has injected such potential and even proposed training-free methods, there remain key gaps. Consequently, we propose a novel training-free HOI detection framework for Dynamic Scoring with enhanced semantics (dysco) that effectively utilizes textual and visual interaction representations within a multimodal registry, enabling robust and nuanced interaction understanding. This registry incorporates a small set of visual cues and uses innovative interaction signatures to improve the semantic alignment of verbs, facilitating effective generalization to rare interactions. Additionally, we propose a unique multi-head attention mechanism that adaptively weights the contributions of the visual and textual features. Experimental results demonstrate that our dysco surpasses training-free state-of-the-art models and is competitive with training-based approaches, particularly excelling in rare interactions. Code is available at https://github.com/francescotonini/dysco.
Learning from large-scale pre-trained models with strong generalization ability has shown remarkable success in a wide range of downstream tasks recently, but it is still underexplored in the challenging few-shot class-incremental learning (FSCIL) task. It aims to continually learn new concepts from limited training samples without forgetting the old ones at the same time. In this paper, we introduce DSS-Prompt, a simple yet effective approach that transforms the pre-trained Vision Transformer with minimal modifications in the way of prompts into a strong FSCIL classifier. Concretely, we synergistically utilize two complementary types of prompts in each Transformer block: static prompts to bridge the domain gap between the pre-training and downstream datasets, thus enabling better adaption; and dynamic prompts to capture instance-aware semantics, thus enabling easy transfer from base to novel classes. Specially, to generate dynamic prompts, we leverage a pre-trained multi-modal model to extract input-related diverse semantics, thereby generating complementary input-aware prompts, and then adaptively adjust their importance across different layers. In this way, on top of the prompted visual embeddings, a simple prototype classifier can beat state-of-the-arts without further training on the incremental tasks. We conduct extensive experiments on four benchmarks to validate the effectiveness of our DSS-Prompt and show that it consistently achieves better performance than existing approaches on all datasets and can alleviate the catastrophic forgetting issue as well.
Existing few-shot action recognition (FSAR) studies predominantly follow a metric learning framework, where prototypes are generated directly from features extracted by an encoder, and classification is performed via distance-based matching. However, due to the limited number of available samples, significant variations exist between different video features of the same class. As a result, the same query video may yield different classification results when matched against different sets of support videos. To address this issue, we propose a novel Hierarchical Meta-Prototypes Network (HMP-Net). The key innovation of our approach lies in the introduction of a category-agnostic and feature-agnostic meta-prototype module, which guides video feature mapping into a more suitable feature space. To optimize this meta-prototype, we design an alternating meta-prototype training strategy, where the model first learns to transform features under a fixed meta-prototype, and then the meta-prototype is refined to better guide feature mapping. Additionally, to adapt image-based metric learning models to video-based FSAR tasks, we introduce a series of lightweight adaptation modules. Specifically, we integrate an adapter into the encoder to improve video frame feature extraction, design a hierarchical prototype generation mechanism to enhance overall video understanding, and incorporate a task-specific perception module to extract unique features for each task. These adaptations make our model better suited for FSAR, significantly improving performance. We evaluate HMP-Net on five challenging benchmarks, and experimental results demonstrate that our model achieves new state-of-the-art performance on HMDB51, UCF101, Kinetics, and SthSthV2-Small. Extensive empirical evaluations further highlight the effectiveness and robustness of HMP-Net.
Traditional Mongolian script recognition poses unique challenges due to its vertical layout, complex morphology, and the coexistence of visually distinct writing styles, such as standard printed (White) and cursive calligraphic (Hawang) forms. Existing approaches typically rely on style-specific models, leading to limited generalization and increased computational cost. In this paper, we propose UniMTR, a unified and lightweight framework for dual-style Traditional Mongolian word recognition. UniMTR distills knowledge from two expert teacher networks into a compact student model through a novel contrastive distillation strategy. This strategy leverages cross-style positive pair construction, hard negative mining, and uncertainty-aware loss weighting to bridge the style gap. We also introduce a new glyph-code encoding scheme that captures context-dependent visual variants beyond Unicode representation. Experiments on the newly constructed benchmark MTR-Mix demonstrate that UniMTR outperforms state-of-the-art baselines, achieving 13.6% CER and 14.2% average style-wise CER, while reducing model size by over 75% and enabling real-time inference at 320 FPS. Our approach offers a practical and scalable solution for style-robust script recognition in resource-constrained scenarios.
This paper studies the challenging task of Referring Expression Comprehension (REC), which aims at detecting the text-referred target object in an input image. To achieve this, most recent works attempt to adapt powerful pretrained models through integrating additional structures (e.g., low-rank adaptation (LoRA) or adapter modules) to enable efficient parameter tuning. However, all these methods process pretrained features in a position-agnostic manner. This will limit their effectiveness in REC tasks, where the positional information is essential to correctly localize the target object. To this end, we propose a novel parameter-efficient tuning approach, named Multi-Modal Adaptive Positional Encoding (MAP), which addresses the above problem from a new perspective of positional encoding. More specifically, MAP first generates initial positional embeddings for different visual encoder layers from a set of learnable vectors, and then adjusts them adaptively based on spatial-wise visual-linguistic correlations of input data. In this way, the positional information of different image tokens can be appropriately modeled and utilized by MAP, thus making it more applicable to REC tasks. Extensive experiments on five widely-used datasets demonstrate that MAP achieves comparable results to full fine-tuning methods with much fewer extra parameters and outperforms other parameter-efficient tuning approaches. Our source code is available at: https://github.com/Mr-Bigworth/MAP.
Lifelong Person Re-identification (LReID) focuses on continuously adapting to new domains over time while preserving knowledge from previously seen domains, particularly under the domain incremental learning setting. The major challenge of LReID is catastrophic forgetting, typically caused by large domain shifts during training. To address this, we propose a novel Amplitude-aware Domain Style Replay (ADSR) framework, which introduces a Fourier-based Style Transfer (FST) mechanism to generate synthetic data that reflects the style of previously encountered domains. These proxy images help retain prior knowledge without the need to store actual past data. Our method transfers stylistic information-mainly encoded in the amplitude spectrum-from old domains to new ones, creating old-stylized images that preserve the content of new domain data while adopting the visual style of earlier domains. To further boost generalization, we design a Self-Stylization Normalization (SSN) module that adapts the current domain's style distribution, making the model more robust to stylistic variations. Additionally, we introduce a Multi-Granularity Transfer (MGT) module that uses K-Means clustering to extract multiple representative style features from each domain, enabling compact yet comprehensive storage and replay of domain-specific information. Extensive experiments on multiple LReID benchmarks show that ADSR achieves superior performance over existing approaches, effectively reducing forgetting and improving cross-domain generalization. Our code is available at https://github.com/cclong8/MM2025-ADSR.
Spatial transcriptomics technologies enable the integration of gene expression profiles with spatial context, facilitating a deeper understanding of tissue architecture through downstream tasks such as clustering. However, existing approaches predominantly focus on highly variable genes (HVGs), while the informative structural and contextual signals embedded in low variability genes (LVGs) remain largely underutilized. To bridge this gap, we propose SALVG (Spatial Augmentation via Latent Variable Genes), a novel and plug-and-play framework that leverages LVG-derived structural priors to enhance HVG representation learning for spatial clustering. Specifically, SALVG constructs spatial, feature, and combined graphs for both HVGs and LVGs, and introduces two graph-based augmentation strategies to inject LVG information into HVG graphs. The first strategy enhances the HVG combined graph directly using the LVG combined graph, while the other individually augments HVG spatial and feature graphs with their LVG counterparts before fusing them into a new combined representation. These enhanced graph structures are subsequently employed for downstream clustering. To the best of our knowledge, SALVG is the first framework to exploit LVG signals for assisting HVG-centric spatial transcriptomics clustering, effectively capturing complementary structural and contextual cues. Experiments on multiple benchmarks demonstrate its effectiveness, robustness, and transferability. Case studies further confirm that LVG-derived structure enhances biological interpretability by revealing coherent spatial and cellular patterns.
Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). Despite remarkable successes of numerous image processing methods that rely on appearance information, these methods often lack sensitivity to topological structures when used in general deep learning (DL) frameworks. In this paper, we introduce a new general approach, called TopoImages (for Topology Images), which computes a new representation of input images by encoding local topology of patches. In TopoImages, we leverage persistent homology (PH) to encode geometric and topological features inherent in image patches. Our main objective is to capture topological information in local patches of an input image into a vectorized form. Specifically, we first compute persistence diagrams (PDs) of the patches, and then vectorize and arrange these PDs into long vectors for pixels of the patches. The resulting multi-channel image-form representation is called a TopoImage. TopoImages offers a new perspective for data analysis. To garner diverse and significant topological features in image data and ensure a more comprehensive and enriched representation, we further generate multiple TopoImages of the input image using various filtration functions, which we call multi-view TopoImages. The multi-view TopoImages are fused with the input image for DL-based classification, with considerable improvement. Our TopoImages approach is highly versatile and can be seamlessly integrated into common DL frameworks. Experiments on three public medical image classification datasets demonstrate noticeably improved accuracy over state-of-the-art methods.
Class Incremental Learning (CIL) aims to continually learn new classes from a stream of data without forgetting previously learned ones. Recent approaches have leveraged pre-trained models (PTMs) to improve performance, especially vision-language models, which offer better generalization than models trained solely on visual data. Many of these methods rely on simple language templates to generate class representations, which then serve as classifiers. However, due to differences between the pre-training data and downstream tasks, these textual features can become too similar for certain classes, leading to prediction errors. To address this issue, we propose a method that optimizes the geometric structure of both visual and textual features across different classes. Inspired by neural collapse theory, we introduce a multi-modal alignment strategy: for each class, a reference vector is chosen from a simplex Equiangular Tight Frame, and both the visual and textual features of the class are aligned with this vector. To better capture intra-class variations, we also construct multiple visual prototypes for each class. A multi-prototype supervised contrastive loss is then employed to pull an image feature toward the closest matching prototype of its true class and push it away from prototypes of other classes. We evaluate our approach on five widely used CIL benchmarks. The results show that our method achieves state-of-the-art performance, demonstrating its effectiveness in addressing the challenges of class incremental learning. Our code is available at https://github.com/qcNPU/NCSCMP.
By clustering pixels with locally similar values, superpixel-based approaches have shown great potential in processing hyperspectral images (HSI) , thereby reducing the computational burden associated with large spatial dimensions. However, specific for spatial-spectral fusion (SSF), superpixel segmentation is inherently non-differentiable and irreversible; hence it is inapplicable. To address the issues, we propose a semantic transformer-based solver, namely SpecSolver, which is basically inspired by the benefits of superpixel-based approaches, yet with the inner mechanism completely improved. The core idea lies in learning the intrinsic semantic states of HSIs hidden behind discretized pixel representations. Specifically, we propose a new Semantic-Attention to adaptively split the image domain into a series of learnable slices of flexible shapes, where image pixels under similar semantic states will be ascribed to the same slice. By calculating attention to the Semantic-Superpixel tokens encoded from slices, SpecSolver can effectively capture intricate semantic correlations from the vast number of pixels, which also empowers the solver with an endogenous capacity for modeling different magnification scales and allows for efficient computation in linear complexity. On that basis, we elaborate a SpatialNet module, which extracts multiscale local spectral information, and a FreqNet module, which supplements global information, capturing subtle details and variations across different spectra. Experiments on two benchmark SSF datasets verify the state-of-the-art (SOTA) performance of the proposed method, both visually and quantitatively. Also, ablation studies validate the mentioned contributions.
Infrared and Visible Image Fusion (IVIF) under unregistered conditions has been of great interest in various visual tasks under challenging environments. While existing approaches often demonstrate promising results on specific benchmarks, they tend to exhibit performance drops in unseen scenarios and incur high computational overhead when retrained on new datasets. To address these challenges, we propose TRACE, a Training-free Reinforcement-based Alignment method for Cross-modality Enhancement, which incorporates Evaluator, a rewarding network, into an evaluation-driven Reinforcement Learning (RL) framework, enabling efficient and plug-and-play refinement of any existing registration approach. Specifically, TRACE constructs the Evaluator network to assess the alignment quality of the given registration model, generating confidence scores and adjustment masks via spatial and channel attention. Leveraging these cues as RL rewards, TRACE iteratively refines the registration network to mitigate misalignments until the accumulated improvement is satisfied. Due to its training-free and plug-and-play nature, TRACE notably enhances fusion results across diverse and unseen scenarios. TRACE achieves impressive improvements in different methods across diverse datasets with minimal computational cost. The project page is available at https://github.com/pubyLu/TRACE.
Generating accurate descriptions of human actions in videos remains a challenging task for video captioning models. Existing approaches often struggle to capture fine-grained motion details, resulting in vague or semantically inconsistent captions. In this work, we introduce the Motion-Augmented Caption Model (M-ACM), a novel generative framework that enhances caption quality by incorporating motion-aware decoding. At its core, M-ACM leverages motion representations derived from human mesh recovery to explicitly highlight human body dynamics, thereby reducing hallucinations and improving both semantic fidelity and spatial alignment in the generated captions. To support research in this area, we present the Human Motion Insight (HMI) Dataset, comprising 115K video-description pairs focused on human movement, along with HMI-Bench, a dedicated benchmark for evaluating motion-focused video captioning. Experimental results demonstrate that M-ACM significantly outperforms previous methods in accurately describing complex human motions and subtle temporal variations, setting a new standard for motion-centric video captioning.
Generalized category discovery (GCD) aims to group unlabeled samples from known and unknown classes when only part of the labeled data in the known classes is given. It allows the model to adapt to dynamic environments by discovering novel categories. However, when we applied the GCD approach to the decentralized open world, we still encountered the following challenges: (1) none of labeled data easily obtained in the open world, (2) heterogeneous label spaces across different environments, (3)representation degradation caused by fine-tuning models with limited data in specific environments. To address the above challenges, we introduce a new and practical task, namely Cloud-edge GCD (CE-GCD). Different from semi-supervised GCD, CE-GCD assumes that we only have a base model trained on common public categories, and aims to perform personalized unsupervised novel category discovery in multiple environments with heterogeneous label spaces. Data from different environments or clients cannot be shared, only model parameters can be transferred. To tackle this problem, we propose a novel GCD framework based on energy-guided known class discrimination and multi-level contrastive learning. In each client, we first use the classifier of the base model to distinguish between known and unknown classes, and then perform unsupervised learning on the unknown classes. Each client transfers category information through prototypes to assist learning. Extensive experiments on multiple datasets demonstrate the effectiveness of our approach.
Multiple Queries with Multiple Keys: A Precise Prompt Matching Paradigm for Prompt-based Continual Learning
PDF ↗Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existing approaches often suffer from low accuracy in prompt selection, which can result in the model receiving biased knowledge and making biased predictions. To address this issue, we propose the Multiple Queries with Multiple Keys (MQMK) prompt matching paradigm for precise prompt selection. The goal of MQMK is to select the prompts whose training data distribution most closely matches that of the test sample. Specifically, Multiple Queries enable precise breadth search by introducing task-specific knowledge, while Multiple Keys perform deep search by representing the feature distribution of training samples at a fine-grained level. Each query is designed to perform local matching with a designated task to reduce interference across queries. Experiments show that MQMK enhances the prompt matching rate by over 30% in challenging scenarios and achieves state-of-the-art performance on three widely adopted continual learning benchmarks. The code is available at https://github.com/DunweiTu/MQMK.
2D images and 3D point clouds are foundational data types for multimedia applications, including real-time video analysis, augmented reality (AR), and 3D scene understanding. Class-incremental semantic segmentation (CSS) requires incrementally learning new semantic categories while retaining prior knowledge. Existing methods typically rely on computationally expensive training based on stochastic gradient descent, employing complex regularization or exemplar replay. However, stochastic gradient descent-based approaches inevitably update the model's weights for past knowledge, leading to catastrophic forgetting, a problem exacerbated by pixel/point-level granularity. To address these challenges, we propose CFSSeg, a novel exemplar-free approach that leverages a closed-form solution, offering a practical and theoretically grounded solution for continual semantic segmentation tasks. This eliminates the need for iterative gradient-based optimization and storage of past data, requiring only a single pass through new samples per step. It not only enhances computational efficiency but also provides a practical solution for dynamic, privacy-sensitive multimedia environments. Extensive experiments on 2D and 3D benchmark datasets such as Pascal VOC2012, S3DIS, and ScanNet demonstrate CFSSeg's superior performance.