A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects

Video Scene Parsing (VSP) has emerged as a cornerstone in computer vision, facilitating the simultaneous segmentation, recognition, and tracking of diverse visual entities in dynamic scenes. In this survey, we present a holistic review of recent advances in VSP, covering a wide array of vision tasks, including Video Semantic Segmentation (VSS), Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), as well as Video Tracking and Segmentation (VTS), and Open-Vocabulary Video Segmentation (OVVS). We systematically analyze the evolution from traditional hand-crafted features to modern deep learning paradigms -- spanning from fully convolutional networks to the latest transformer-based architectures -- and assess their effectiveness in capturing both local and global temporal contexts. Furthermore, our review critically discusses the technical challenges, ranging from maintaining temporal consistency to handling complex scene dynamics, and offers a comprehensive comparative study of datasets and evaluation metrics that have shaped current benchmarking standards. By distilling the key contributions and shortcomings of state-of-the-art methodologies, this survey highlights emerging trends and prospective research directions that promise to further elevate the robustness and adaptability of VSP in real-world applications.

StgcDiff: Spatial-Temporal Graph Condition Diffusion for Sign Language Transition Generation

Sign language transition generation seeks to convert discrete sign language segments into continuous sign videos by synthesizing smooth transitions. However,most existing methods merely concatenate isolated signs, resulting in poor visual coherence and semantic accuracy in the generated videos. Unlike textual languages,sign language is inherently rich in spatial-temporal cues, making it more complex to model. To address this,we propose StgcDiff, a graph-based conditional diffusion framework that generates smooth transitions between discrete signs by capturing the unique spatial-temporal dependencies of sign language. Specifically, we first train an encoder-decoder architecture to learn a structure-aware representation of spatial-temporal skeleton sequences. Next, we optimize a diffusion denoiser conditioned on the representations learned by the pre-trained encoder, which is tasked with predicting transition frames from noise. Additionally, we design the Sign-GCN module as the key component in our framework, which effectively models the spatial-temporal features. Extensive experiments conducted on the PHOENIX14T, USTC-CSL100,and USTC-SLR500 datasets demonstrate the superior performance of our method.

MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos

We study multi-modal summarization for instructional videos, whose goal is to provide users an efficient way to learn skills in the form of text instructions and key video frames. We observe that existing benchmarks focus on generic semantic-level video summarization, and are not suitable for providing step-by-step executable instructions and illustrations, both of which are crucial for instructional videos. We propose a novel benchmark for user interface (UI) instructional video summarization to fill the gap. We collect a dataset of 2,413 UI instructional videos, which spans over 167 hours. These videos are manually annotated for video segmentation, text summarization, and video summarization, which enable the comprehensive evaluations for concise and executable video summarization. We conduct extensive experiments on our collected MS4UI dataset, which suggest that state-of-the-art multi-modal summarization methods struggle on UI video summarization, and highlight the importance of new methods for UI instructional video summarization.

Touch begins where vision ends: Generalizable policies for contact-rich manipulation

Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generalizable policies. We introduce VisuoTactile Local (ViTaL) policy learning, a framework that solves fine-grained manipulation tasks by decomposing them into two phases: a reaching phase, where a vision-language model (VLM) enables scene-level reasoning to localize the object of interest, and a local interaction phase, where a reusable, scene-agnostic ViTaL policy performs contact-rich manipulation using egocentric vision and tactile sensing. This approach is motivated by the observation that while scene context varies, the low-level interaction remains consistent across task instances. By training local policies once in a canonical setting, they can generalize via a localize-then-execute strategy. ViTaL achieves around 90% success on contact-rich tasks in unseen environments and is robust to distractors. ViTaL's effectiveness stems from three key insights: (1) foundation models for segmentation enable training robust visual encoders via behavior cloning; (2) these encoders improve the generalizability of policies learned using residual RL; and (3) tactile sensing significantly boosts performance in contact-rich tasks. Ablation studies validate each of these insights, and we demonstrate that ViTaL integrates well with high-level VLMs, enabling robust, reusable low-level skills. Results and videos are available at https://vitalprecise.github.io.

MAMMA: Markerless & Automatic Multi-Person Motion Action Capture

We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video of two-person interaction sequences. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person--person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. We will release our dataset, benchmark, method, training code, and pre-trained model weights for research purposes.

Can Sound Replace Vision in LLaVA With Token Substitution?

While multimodal systems have achieved impressive advances, they typically rely on text-aligned representations rather than directly integrating audio and visual inputs. This reliance can limit the use of acoustic information in tasks requiring nuanced audio understanding. In response, SoundCLIP explores direct audio-visual integration within multimodal large language models (MLLMs) by substituting CLIP's visual tokens with audio representations and selecting sound-relevant patch tokens in models such as LLaVA. We investigate two configurations: (1) projecting audio features into CLIP's visual manifold via a multilayer perceptron trained with InfoNCE on paired audio-video segments, and (2) preserving raw audio embeddings with minimal dimensional adjustments. Experiments with five state-of-the-art audio encoders reveal a fundamental trade-off. While audio-to-video retrieval performance increases dramatically (up to 44 percentage points in Top-1 accuracy) when audio is projected into CLIP's space, text generation quality declines. Encoders pre-trained with text supervision (CLAP, Whisper, ImageBind) maintain stronger generative capabilities than those focused primarily on audiovisual alignment (Wav2CLIP, AudioCLIP), highlighting the value of language exposure for generation tasks. We introduce WhisperCLIP, an architecture that fuses intermediate representations from Whisper, as well as AudioVisual Event Evaluation (AVE-2), a dataset of 580,147 three-second audiovisual clips with fine-grained alignment annotations. Our findings challenge the assumption that stronger cross-modal alignment necessarily benefits all multimodal tasks; instead, a Pareto frontier emerges wherein optimal performance depends on balancing retrieval accuracy with text generation quality. Codes and datasets: https://github.com/ali-vosoughi/SoundCLIP.

Prompts to Summaries: Zero-Shot Language-Guided Video Summarization

The explosive growth of video data intensified the need for flexible user-controllable summarization tools that can operate without domain-specific training data. Existing methods either rely on datasets, limiting generalization, or cannot incorporate user intent expressed in natural language. We introduce Prompts-to-Summaries: the first zero-shot, text-queryable video summarizer that converts off-the-shelf video-language models (VidLMs) captions into user-guided skims via large language models (LLMs) judging, without the use of training data at all, beating all unsupervised and matching supervised methods. Our pipeline (i) segments raw video footage into coherent scenes, (ii) generates rich scene-level descriptions through a memory-efficient, batch-style VidLM prompting scheme that scales to hours-long videos on a single GPU, (iii) leverages an LLM as a judge to assign scene-level importance scores under a carefully crafted prompt, and finally, (iv) propagates those scores to short segments level via two new metrics: consistency (temporal coherency) and uniqueness (novelty), yielding fine-grained frame importance. On SumMe and TVSum, our data-free approach surpasses all prior data-hungry unsupervised methods. It also performs competitively on the Query-Focused Video Summarization (QFVS) benchmark, despite using no training data and the competing methods requiring supervised frame-level importance. To spur further research, we release VidSum-Reason, a new query-driven dataset featuring long-tailed concepts and multi-step reasoning; our framework attains robust F1 scores and serves as the first challenging baseline. Overall, our results demonstrate that pretrained multimodal models, when orchestrated with principled prompting and score propagation, already provide a powerful foundation for universal, text-queryable video summarization.

Outside Knowledge Conversational Video (OKCV) Dataset -- Dialoguing over Videos

In outside knowledge visual question answering (OK-VQA), the model must identify relevant visual information within an image and incorporate external knowledge to accurately respond to a question. Extending this task to a visually grounded dialogue setting based on videos, a conversational model must both recognize pertinent visual details over time and answer questions where the required information is not necessarily present in the visual information. Moreover, the context of the overall conversation must be considered for the subsequent dialogue. To explore this task, we introduce a dataset comprised of $2,017$ videos with $5,986$ human-annotated dialogues consisting of $40,954$ interleaved dialogue turns. While the dialogue context is visually grounded in specific video segments, the questions further require external knowledge that is not visually present. Thus, the model not only has to identify relevant video parts but also leverage external knowledge to converse within the dialogue. We further provide several baselines evaluated on our dataset and show future challenges associated with this task. The dataset is made publicly available here: https://github.com/c-patsch/OKCV.

Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation

Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex temporal dynamics, and the current lack of domain-specific pre-trained models. Existing SSL approaches such as contrastive, masked modeling, and clustering-based methods struggle with high intersample similarity, sensitivity to low PSNR inputs common in ultrasound, or aggressive augmentations that distort clinically relevant features. We present DISCOVR (Distilled Image Supervision for Cross Modal Video Representation), a self-supervised dual branch framework for cardiac ultrasound video representation learning. DISCOVR combines a clustering-based video encoder that models temporal dynamics with an online image encoder that extracts fine-grained spatial semantics. These branches are connected through a semantic cluster distillation loss that transfers anatomical knowledge from the evolving image encoder to the video encoder, enabling temporally coherent representations enriched with fine-grained semantic understanding. Evaluated on six echocardiography datasets spanning fetal, pediatric, and adult populations, DISCOVR outperforms both specialized video anomaly detection methods and state-of-the-art video-SSL baselines in zero-shot and linear probing setups, and achieves superior segmentation transfer.

Q-SAM2: Accurate Quantization for Segment Anything Model 2

The Segment Anything Model 2 (SAM2) has gained significant attention as a foundational approach for promptable image and video segmentation. However, its expensive computational and memory consumption poses a severe challenge for its application in resource-constrained scenarios. In this paper, we propose an accurate low-bit quantization method for efficient SAM2, termed Q-SAM2. To address the performance degradation caused by the singularities in weight and activation distributions during quantization, Q-SAM2 introduces two novel technical contributions. We first introduce a linear layer calibration method for low-bit initialization of SAM2, which minimizes the Frobenius norm over a small image batch to reposition weight distributions for improved quantization. We then propose a Quantization-Aware Training (QAT) pipeline that applies clipping to suppress outliers and allows the network to adapt to quantization thresholds during training. Our comprehensive experiments demonstrate that Q-SAM2 allows for highly accurate inference while substantially improving efficiency. Both quantitative and visual results show that our Q-SAM2 surpasses existing state-of-the-art general quantization schemes, especially for ultra-low 2-bit quantization. While designed for quantization-aware training, our proposed calibration technique also proves effective in post-training quantization, achieving up to a 66% mIoU accuracy improvement over non-calibrated models.