To break through the limitations of pre-training models on fixed categories, Open-Set Object Detection (OSOD) and Open-Set Segmentation (OSS) have attracted a surge of interest from researchers. Inspired by large language models, mainstream OSOD and OSS methods generally utilize text as a prompt, achieving remarkable performance. Following SAM paradigm, some researchers use visual prompts, such as points, boxes, and masks that cover detection or segmentation targets. Despite these two prompt paradigms exhibit excellent performance, they also reveal inherent limitations. On the one hand, it is difficult to accurately describe characteristics of specialized category using textual description. On the other hand, existing visual prompt paradigms heavily rely on multi-round human interaction, which hinders them being applied to fully automated pipeline. To address the above issues, we propose a novel prompt paradigm in OSOD and OSS, that is, Image Prompt Paradigm. This brand new prompt paradigm enables to detect or segment specialized categories without multi-round human intervention. To achieve this goal, the proposed image prompt paradigm uses just a few image instances as prompts, and we propose a novel framework named MI Grounding for this new paradigm. In this framework, high-quality image prompts are automatically encoded, selected and fused, achieving the single-stage and non-interactive inference. We conduct extensive experiments on public datasets, showing that MI Grounding achieves competitive performance on OSOD and OSS benchmarks compared to text prompt paradigm methods and visual prompt paradigm methods. Moreover, MI Grounding can greatly outperform existing method on our constructed specialized ADR50K dataset.

Existing multimodal large language models (MLLMs) face significant challenges in Referring Video Object Segmentation(RVOS). We identify three critical challenges: (C1) insufficient quantitative representation of textual numerical data, (C2) repetitive and degraded response templates for spatiotemporal referencing, and (C3) loss of visual information in video sampling queries lacking textual guidance. To address these, we propose a novel framework, Dynamic Time Object Sensing (DTOS), specifically designed for RVOS. To tackle (C1) and (C2), we introduce specialized tokens to construct multi-answer response templates, enabling regression of event boundaries and target localization. This approach improves the accuracy of numerical regression while mitigating the issue of repetitive degradation. To address (C3), we propose a Text-guided Clip Sampler (TCS) that selects video clips aligned with user instructions, preventing visual information loss and ensuring consistent temporal resolution. TCS is also applicable to Moment Retrieval tasks, with enhanced multimodal input sequences preserving spatial details and maximizing temporal resolution. DTOS demonstrates exceptional capability in flexibly localizing multiple spatiotemporal targets based on user-provided textual instructions. Extensive experiments validate the effectiveness of our approach, with DTOS achieving state-of-the-art performance in $\mathcal{J}{{\& }}\mathcal{F}$ scores: an improvement of +4.36 on MeViS, +4.48 on Ref-DAVIS17, and +3.02 on Ref-YT-VOS. Additionally, our TCS demonstrates exceptional performance in Moment Retrieval. The code is available at https://github.com/Maulog/OPEN-DTOS-LMM.

Action segmentation in untrimmed videos is essential for comprehensive video understanding. Despite significant progress in unsupervised methods, capturing both long-range dependencies and short-duration actions simultaneously remains a challenging task. To address this challenge, this paper introduces the Cluster-Refined Optimal Transport (CROT) method, combining hierarchical clustering and optimal transport for unsupervised action segmentation. We first hierarchically cluster video frame representations to capture long-range dependencies and generate pseudo-boundaries. Initial pseudo-labels are then obtained via optimal transport, ensuring short-duration actions are recognized. Finally, these pseudo-labels are refined using the pseudo-boundaries, resulting in the final segmentation output. Extensive experiments on three public datasets, i.e., YouTube Instructions, Breakfast, and 50Salads, demonstrate that our method performs on par with or better than previous approaches.

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution. Based on CVSTA, we construct a multidimensional refinement GCN (MDR-GCN) that can improve the discrimination among channel-, joint-, and frame-level features for fine-grained actions. Furthermore, we propose a robust decouple loss (RDL) that significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse NTU-RGB + D 120 dataset and NTU-RGB + D X-view version. Our code is publicly available at https://github.com/dingyn-Reno/MDR-GCN.

The streaming temporal action segmentation (STAS) task, a supplementary task of temporal action segmentation (TAS), has not received adequate attention in the field of video understanding. Existing TAS methods are constrained to offline scenarios due to their heavy reliance on multimodal features and complete contextual information. The STAS task requires the model to classify each frame of the entire untrimmed video sequence clip by clip in time, thereby extending the applicability of TAS methods to online scenarios. However, directly applying existing TAS methods to SATS tasks results in significantly poor segmentation outcomes. In this article, we thoroughly analyze the fundamental differences between STAS tasks and TAS tasks, attributing the severe performance degradation when transferring models to model bias and optimization dilemmas. We introduce an end-to-end streaming video TAS model with reinforcement learning (SVTAS-RL). The end-to-end modeling method mitigates the modeling bias introduced by the change in task nature and enhances the feasibility of online solutions. Reinforcement learning (RL) is utilized to alleviate the optimization dilemma. Through extensive experiments, the SVTAS-RL model significantly outperforms existing STAS models and achieves competitive performance to the state-of-the-art (SOTA) TAS model on multiple datasets under the same evaluation criteria, demonstrating notable advantages on the ultralong video dataset EGTEA. Our code is publicly available at https://github.com/Thinksky5124/SVTAS.

Human Action Quality Assessment (AQA) is a prominent area of research in human action analysis. Current mainstream methods only consider the RGB modality which results in limited feature representation and insufficient performance due to the complexity of the AQA task. In this paper, we propose a simple and modular framework called the Two-Modality Assessment Framework (2M-AF), which comprises a skeleton stream, an RGB stream and a regression module. For the skeleton stream, we develop the Self-supervised Mask Encoder Graph Convolution Network (SME-GCN) to achieve representation learning, and further implement score assessment. Additionally, we propose a Preference Fusion Module (PFM) to fuse features, which can effectively avoid the disadvantages of different modalities. Our experimental results demonstrate the superiority of the proposed 2M-AF over current state-of-the-art methods on three publicly available datasets: AQA-7, UNLV-Diving, and MMFS-63.