arXiv subjects

Muhammad Munsif

Publications and source records attributed to Muhammad Munsif.

6 recordsLinked to original sources

arXiv2026

3rd Place Solution to Human Motion Challenges in Real-World and Clinical Settings (MoCha) @ECCV2026: Language-Aligned Motion Representations for Domain-Generalizable UPDRS-Gait Severity Estimation

In this work, we introduce language-aligned motion representations for domain-generalizable UPDRS-Gait severity estimation, aiming to learn semantically structured motion features that generalize across heterogeneous clinical domains. We first learn motion representations using a Bi-GRU backbone that captures the temporal dynamics of SMPL sequences. Prior to model training, motion captions are generated offline using Qwen2.5-7B-Instruct. The backbone is then trained with both classification and text-alignment objectives to learn discriminative and semantically structured motion representations while accounting for the class imbalance present in the training data. We subsequently adapt the learned backbone independently to each source domain so that the model can capture domain-specific motion characteristics. The resulting source-specific models are then merged at the parameter level to consolidate complementary knowledge across source domains into a single domain-generalized model. To further mitigate class imbalance, we perform GPT-5.5-based pseudo labeling, and our final merged models for each site do not use any class-prior correction during inference. The resulting model is evaluated under the unseen-site setting of the MoCha Challenge, using Macro F1 as the primary evaluation metric. Our method achieves a macro-F1 of 0.57 on the hidden test set with only 637K active parameters at inference, ranking 3rd among 58 leaderboard entries in the MoCha 2026 Challenge. The challenge attracted 1,669 submissions from 112 participants and offered monetary prizes sponsored by Machine Medicine Technologies.

cs.CV↗

arXiv2026

Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs

Video surveillance in public safety, healthcare, and smart environments has made continuous human monitoring routine, raising real risks to personal identity and appearance. Privacy-preserving action recognition (PPAR) tackles the tension between the utility of video understanding and this exposure, and has drawn fast-growing interest. However, existing surveys remain narrow. Most catalog a single mechanism family, predate recent adversarial and hybrid work, or barely address evaluation. The result is a fragmented literature with incompatible threat models, inconsistent metrics, and no shared evaluation standard. We address this with a PRISMA-guided review of 32 peer-reviewed papers (2018--2026) drawn from 885 screened records. Methods sort into five families, namely adversarial learning (52%), skeleton-based (20%), cryptographic (12%), differential privacy (8%), and hybrid (8%), each with distinct privacy, utility, and efficiency trade-offs. Evaluation is the weak point. Only 10% of papers adopt a formal privacy definition, 65% rely on ad-hoc metrics, and 40% report an inconsistently defined cMAP. The trade-offs are steep. Skeleton methods reach about 85% accuracy but drop appearance, adversarial methods hold near 80% utility at moderate privacy (cMAP 0.9 to 0.3--0.5), and differential privacy often falls below 70%. Harder conditions stay under-tested, with fewer than 15% of papers checking cross-dataset generalization, under 10% testing adaptive attackers, and real-time edge deployment nearly untouched. We contribute a two-dimensional privacy-space taxonomy, a formal threat model, a comparative trade-off analysis, the PPAR Unified Evaluation Protocol, and a roadmap centered on benchmark standardization. With this grounding, we argue PPAR can move from prototypes toward deployment, with lessons extending to face recognition and medical imaging.

cs.CV↗

arXiv2026

DREAM: Extending Vision-Language Models with Dual-Objective Encoding for Cross-Modal Retrieval

In today's media-driven world, the exponential growth of video content across domains such as surveillance, education, and entertainment has made retrieving semantically relevant videos via natural language queries increasingly critical. Early video retrieval systems relied on handcrafted features or shallow cross-modal mappings, limiting their ability to capture complex semantics and temporal dynamics. While large-scale vision-language models have improved cross-modal alignment, challenges remain in modeling fine-grained temporal dependencies and nuanced linguistic structures. In this paper, we introduce DREAM: Dual-path Representation Enhancement and Alignment Model, a novel multimodal framework that addresses these limitations through enhanced visual and textual encoding. DREAM incorporates a hybrid language modeling strategy that combines masked and permuted language modeling objectives to capture both local and global linguistic semantics. On the visual side, we design a hierarchical vision encoder with cascaded group attention, which integrates spatial and temporal information through multi-stage token interaction and coarse-to-fine attention refinement. We validate DREAM through comprehensive evaluations on the widely-used MSRVTT, MSVD and LSMDC benchmark datasets, where it achieves new state-of-the-art R1 scores of 49.4%, 49.7% and 27.3%, respectively. Qualitative analyses further show the model's ability to maintain coherent attention across frames and align complex queries with dynamic video content. These findings underscore the effectiveness of hierarchical attention and dual-objective textual modeling in enabling robust, context-aware video retrieval, and pave the way for future research in advancing cross-modal representation learning.

cs.CV↗

arXiv2025

AVAR-Net: A Lightweight Audio-Visual Anomaly Recognition Framework with a Benchmark Dataset

Anomaly recognition plays a vital role in surveillance, transportation, healthcare, and public safety. However, most existing approaches rely solely on visual data, making them unreliable under challenging conditions such as occlusion, low illumination, and adverse weather. Moreover, the absence of large-scale synchronized audio-visual datasets has hindered progress in multimodal anomaly recognition. To address these limitations, this study presents AVAR-Net, a lightweight and efficient audio-visual anomaly recognition framework designed for real-world environments. AVAR-Net consists of four main modules: an audio feature extractor, a video feature extractor, fusion strategy, and a sequential pattern learning network that models cross-modal relationships for anomaly recognition. Specifically, the Wav2Vec2 model extracts robust temporal features from raw audio, while MobileViT captures both local and global visual representations from video frames. An early fusion mechanism combines these modalities, and a Multi-Stage Temporal Convolutional Network (MTCN) model that learns long-range temporal dependencies within the fused representation, enabling robust spatiotemporal reasoning. A novel Visual-Audio Anomaly Recognition (VAAR) dataset, is also introduced, serving as a medium-scale benchmark containing 3,000 real-world videos with synchronized audio across ten diverse anomaly classes. Experimental evaluations demonstrate that AVAR-Net achieves 89.29% accuracy on VAAR and 88.56% Average Precision on the XD-Violence dataset, improving Average Precision by 2.8% over existing state-of-the-art methods. These results highlight the effectiveness, efficiency, and generalization capability of the proposed framework, as well as the utility of VAAR as a benchmark for advancing multimodal anomaly recognition research.

cs.CV↗

arXiv2025

Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey

Connected Vision Systems (CVS) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human-robot interaction. These systems harness multi-view multi-camera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identification (Re-ID), and action understanding (AU). However, deploying CVS in real-world, dynamic environments presents a number of challenges, particularly in addressing occlusions, diverse viewpoints, and environmental variability. Existing surveys have focused primarily on isolated tasks such as tracking, Re-ID, and AU, often neglecting their integration into a cohesive system. These reviews typically emphasize single-view setups, overlooking the complexities and opportunities provided by multi-camera collaboration and multi-view data analysis. To the best of our knowledge, this survey is the first to offer a comprehensive and integrated review of MVMC that unifies MVMC tracking, Re-ID, and AU into a single framework. We propose a unique taxonomy to better understand the critical components of CVS, dividing it into four key parts: MVMC tracking, Re-ID, AU, and combined methods. We systematically arrange and summarize the state-of-the-art datasets, methodologies, results, and evaluation metrics, providing a structured view of the field's progression. Furthermore, we identify and discuss the open research questions and challenges, along with emerging technologies such as lifelong learning, privacy, and federated learning, that need to be addressed for future advancements. The paper concludes by outlining key research directions for enhancing the robustness, efficiency, and adaptability of CVS in complex, real-world applications. We hope this survey will inspire innovative solutions and guide future research toward the next generation of intelligent and adaptive CVS.

cs.CV↗

arXiv2025

Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns

Benchmark object detection (OD) datasets play a pivotal role in advancing computer vision applications such as autonomous driving, and surveillance, as well as in training and evaluating deep learning-based state-of-the-art detection models. Among them, MS-COCO has become a standard benchmark due to its diverse object categories and complex scenes. However, despite its wide adoption, MS-COCO suffers from various annotation issues, including missing labels, incorrect class assignments, inaccurate bounding boxes, duplicate labels, and group labeling inconsistencies. These errors not only hinder model training but also degrade the reliability and generalization of OD models. To address these challenges, we propose a comprehensive refinement framework and present MJ-COCO, a newly re-annotated version of MS-COCO. Our approach begins with loss and gradient-based error detection to identify potentially mislabeled or hard-to-learn samples. Next, we apply a four-stage pseudo-labeling refinement process: (1) bounding box generation using invertible transformations, (2) IoU-based duplicate removal and confidence merging, (3) class consistency verification via expert objects recognizer, and (4) spatial adjustment based on object region activation map analysis. This integrated pipeline enables scalable and accurate correction of annotation errors without manual re-labeling. Extensive experiments were conducted across four validation datasets: MS-COCO, Sama COCO, Objects365, and PASCAL VOC. Models trained on MJ-COCO consistently outperformed those trained on MS-COCO, achieving improvements in Average Precision (AP) and APS metrics. MJ-COCO also demonstrated significant gains in annotation coverage: for example, the number of small object annotations increased by more than 200,000 compared to MS-COCO.

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