R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO

In this work, we aim to incentivize the reasoning ability of Multimodal Large Language Models (MLLMs) via reinforcement learning (RL) and develop an effective approach that mitigates the sparse reward and advantage vanishing issues during RL. To this end, we propose Share-GRPO, a novel RL approach that tackle these issues by exploring and sharing diverse reasoning trajectories over expanded question space. Specifically, Share-GRPO first expands the question space for a given question via data transformation techniques, and then encourages MLLM to effectively explore diverse reasoning trajectories over the expanded question space and shares the discovered reasoning trajectories across the expanded questions during RL. In addition, Share-GRPO also shares reward information during advantage computation, which estimates solution advantages hierarchically across and within question variants, allowing more accurate estimation of relative advantages and improving the stability of policy training. Extensive evaluations over six widely-used reasoning benchmarks showcase the superior performance of our method. Code will be available at https://github.com/HJYao00/R1-ShareVL.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

This paper describes the speaker diarization system developed for the Multimodal Information-Based Speech Processing (MISP) 2025 Challenge. First, we utilize the Sequence-to-Sequence Neural Diarization (S2SND) framework to generate initial predictions using single-channel audio. Then, we extend the original S2SND framework to create a new version, Multi-Channel Sequence-to-Sequence Neural Diarization (MC-S2SND), which refines the initial results using multi-channel audio. The final system achieves a diarization error rate (DER) of 8.09% on the evaluation set of the competition database, ranking first place in the speaker diarization task of the MISP 2025 Challenge.

Beyond Time: Cross-Dimensional Frequency Supervision for Time Series Forecasting

Time series forecasting plays a crucial role in various fields, and the methods based on frequency domain analysis have become an important branch. However, most existing studies focus on the design of elaborate model architectures and are often tailored for limited datasets, still lacking universality. Besides, the assumption of independent and identically distributed (IID) data also contradicts the strong correlation of the time domain labels. To address these issues, abandoning time domain supervision, we propose a purely frequency domain supervision approach named cross-dimensional frequency (X-Freq) loss. Specifically, based on a statistical phenomenon, we first prove that the information entropy of the time series is higher than its spectral entropy, which implies higher certainty in frequency domain and thus can provide better supervision. Secondly, the Fourier Transform and the Wavelet Transform are applied to the time dimension and the channel dimension of the time series respectively, to capture the long-term and short-term frequency variations as well as the spatial configuration features. Thirdly, the loss between predictions and targets is uniformly computed in the frequency domain. Moreover, we plug-and-play incorporate X-Freq into multiple advanced forecasting models and compare on 14 real-world datasets. The experimental results demonstrate that, without making any modification to the original architectures or hyperparameters, X-Freq can improve the forecasting performance by an average of 3.3% on long-term forecasting datasets and 27.7% on short-term ones, showcasing superior generality and practicality. The code will be released publicly.

CPAny: Couple With Any Encoder to Refer Multi-Object Tracking

Referring Multi-Object Tracking (RMOT) aims to localize target trajectories specified by natural language expressions in videos. Existing RMOT methods mainly follow two paradigms, namely, one-stage strategies and two-stage ones. The former jointly trains tracking with referring but suffers from substantial computational overhead. Although the latter improves computational efficiency, its CLIP-inspired dual-tower architecture restricts compatibility with other visual/text backbones and is not future-proof. To overcome these limitations, we propose CPAny, a novel encoder-decoder framework for two-stage RMOT, which introduces two core components: (1) a Contextual Visual Semantic Abstractor (CVSA) performs context-aware aggregation on visual backbone features and projects them into a unified semantic space; (2) a Parallel Semantic Summarizer (PSS) decodes the visual and linguistic features at the semantic level in parallel and generates referring scores. By replacing the inherent feature alignment of encoders with a self-constructed unified semantic space, CPAny achieves flexible compatibility with arbitrary emerging visual / text encoders. Meanwhile, CPAny aggregates contextual information by encoding only once and processes multiple expressions in parallel, significantly reducing computational redundancy. Extensive experiments on the Refer-KITTI and Refer-KITTI-V2 datasets show that CPAny outperforms SOTA methods across diverse encoder combinations, with a particular 7.77\% HOTA improvement on Refer-KITTI-V2. Code will be available soon.

Data-Efficient Generalization for Zero-shot Composed Image Retrieval

Zero-shot Composed Image Retrieval (ZS-CIR) aims to retrieve the target image based on a reference image and a text description without requiring in-distribution triplets for training. One prevalent approach follows the vision-language pretraining paradigm that employs a mapping network to transfer the image embedding to a pseudo-word token in the text embedding space. However, this approach tends to impede network generalization due to modality discrepancy and distribution shift between training and inference. To this end, we propose a Data-efficient Generalization (DeG) framework, including two novel designs, namely, Textual Supplement (TS) module and Semantic-Set (S-Set). The TS module exploits compositional textual semantics during training, enhancing the pseudo-word token with more linguistic semantics and thus mitigating the modality discrepancy effectively. The S-Set exploits the zero-shot capability of pretrained Vision-Language Models (VLMs), alleviating the distribution shift and mitigating the overfitting issue from the redundancy of the large-scale image-text data. Extensive experiments over four ZS-CIR benchmarks show that DeG outperforms the state-of-the-art (SOTA) methods with much less training data, and saves substantial training and inference time for practical usage.

ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, current methods often rely on limited data modalities, resulting in suboptimal performance. In this paper, we propose an Integrated Cross-modal Fusion Network (ICFNet) that integrates histopathology whole slide images, genomic expression profiles, patient demographics, and treatment protocols. Specifically, three types of encoders, a residual orthogonal decomposition module and a unification fusion module are employed to merge multi-modal features to enhance prediction accuracy. Additionally, a balanced negative log-likelihood loss function is designed to ensure fair training across different patients. Extensive experiments demonstrate that our ICFNet outperforms state-of-the-art algorithms on five public TCGA datasets, including BLCA, BRCA, GBMLGG, LUAD, and UCEC, and shows its potential to support clinical decision-making and advance precision medicine. The codes are available at: https://github.com/binging512/ICFNet.

Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection

Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, and the distribution-invariant normality learning has been proposed based on the Reverse Distillation (RD) framework. However, we observe the misalignment issue between the teacher and the student network that causes detection failure, thereby propose FiCo, Filter or Compensate, to address the distribution shift issue in AD. FiCo firstly compensates the distribution-specific information to reduce the misalignment between the teacher and student network via the Distribution-Specific Compensation (DiSCo) module, and secondly filters all abnormal information to capture distribution-invariant normality with the Distribution-Invariant Filter (DiIFi) module. Extensive experiments on three different AD benchmarks demonstrate the effectiveness of FiCo, which outperforms all existing state-of-the-art (SOTA) methods, and even achieves better results on the ID scenario compared with RD-based methods. Our code is available at https://github.com/znchen666/FiCo.

UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer

Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organizer (UFO), which is compatible with any diffusion-based video generation model. The UFO comprises a series of adaptive adapters with adjustable intensities, which can significantly enhance the consistency between the foreground and background of videos and improve image quality without altering the original model parameters when integrated. The training for UFO is simple, efficient, requires minimal resources, and supports stylized training. Its modular design allows for the combination of multiple UFOs, enabling the customization of personalized video generation models. Furthermore, the UFO also supports direct transferability across different models of the same specification without the need for specific retraining. The experimental results indicate that UFO effectively enhances video generation quality and demonstrates its superiority in public video generation benchmarks. The code will be publicly available at https://github.com/Delong-liu-bupt/UFO.

MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval

Composed Image Retrieval (CIR) is a challenging vision-language task, utilizing bi-modal (image+text) queries to retrieve target images. Despite the impressive performance of supervised CIR, the dependence on costly, manually-labeled triplets limits its scalability and zero-shot capability. To address this issue, zero-shot composed image retrieval (ZS-CIR) is presented along with projection-based approaches. However, such methods face two major problems, i.e., task discrepancy between pre-training (image $\leftrightarrow$ text) and inference (image+text $\rightarrow$ image), and modality discrepancy. The latter pertains to approaches based on text-only projection training due to the necessity of feature extraction from the reference image during inference. In this paper, we propose a two-stage framework to tackle both discrepancies. First, to ensure efficiency and scalability, a textual inversion network is pre-trained on large-scale caption datasets. Subsequently, we put forward Modality-Task Dual Alignment (MoTaDual) as the second stage, where large-language models (LLMs) generate triplet data for fine-tuning, and additionally, prompt learning is introduced in a multi-modal context to effectively alleviate both modality and task discrepancies. The experimental results show that our MoTaDual achieves the state-of-the-art performance across four widely used ZS-CIR benchmarks, while maintaining low training time and computational cost. The code will be released soon.

Contactless Fingerprint Recognition Using 3D Graph Matching

Contactless fingerprint is a newly developed type of fingerprint, and has gained lots of attention in recent fingerprint studies. However, most existing contactless fingerprint algorithms treat contactless fingerprints as 2D plain fingerprints, and utilize similar recognition methods as traditional contact-based 2D fingerprints. This recognition approach does not consider the modality difference between contactless and contact fingerprints, especially the intrinsic 3D characteristic of contactless fingerprints. This paper proposes a novel contactless fingerprint recognition algorithm that captures the revealed 3D feature of contactless fingerprints rather than the plain 2D feature. The proposed method first recovers 3D features from the input contactless fingerprint, including the 3D shape model and 3D fingerprint feature (minutiae, orientation, etc.). Then, a novel 3D graph matching is conducted in 3D space according to the extracted 3D feature. Our method captures the real 3D nature of contactless fingerprints as the whole feature extraction and matching algorithms are completed in real 3D space. Experiments results on contactless fingerprint databases show that the proposed method successfully improves the matching accuracy of contactless fingerprints. Exceptionally, our method performs stably across multiple poses of contactless fingerprints due to 3D graph matching, which is a great advantage compared to previous contactless fingerprint recognition algorithms.