Multi-object tracking (MOT) faces challenges in handling occlusions, feature degradation, and non-rigid motion. Existing methods relying on appearance-based re-identification (Re-ID) often struggle under occlusion, leading to frequent identity switches, while traditional motion models fail in dynamic scenarios. To address these issues, we propose an improved MOT framework integrating Score-based Gallery Management (SGM) to retain reliable Re-ID embeddings and Optical Flow-based Motion Compensation (OMC) to refine motion predictions. Our method achieves state-of-the-art performance on MOT20 and SportsMOT and exhibits competitive results on MOT17 and DanceTrack, demonstrating improved identity retention and tracking robustness in complex environments.
As video data is increasingly consumed by machines rather than solely by humans, there is a growing demand for new compression methods that efficiently accommodate this shift. Since object information extracted from videos is crucial for machine consumption, analyzing the similarity between adjacent pictures based on tracked object information can help identify motion-based redundancy which can then be utilized for video compression. In this paper, we perform object tracking on input video and analyze the similarity between adjacent pictures based on the movement of tracked objects. After classifying the pictures based on their similarity, highly redundant pictures within each group are aggressively resampled in the temporal domain to improve compression efficiency while maintaining machine performance. We propose a novel picture grouping method to cluster similar adjacent pictures and describe the process of similarity assessment. We evaluated the compression efficiency of the proposed object tracking-based adaptive temporal resampling through performance evaluation experiments, achieving BD-mAP improvements in object detection of 1.29%, 0.47%, and 2.44% for Random Access (RA), Low-Delay (LD), and All Intra (AI) modes, respectively, and achieving BD-MOTA improvements in object tracking of 0.02%, 0.61%, and 6.86% for RA, LD, and AI modes, respectively.
Diffusion models have recently shown great potential in object detection and instance segmentation, yet their application to scene text detection, with its unique challenges such as instance variability and subjective human annotations, remains unexplored. In this paper, we propose DRR (Diffusion ReRanking), a method that adapts diffusion-based instance segmentation for scene text detection. Traditional instance segmentation often relies on classification scores for ranking, potentially overlooking the accuracy of bounding boxes and mask quality. DRR addresses this by incorporating two networks: a diffusion network, trained with a combination of projection loss and pairwise loss in the mask branch to produce more precise and tightly-bound segmentations, and a reranking network, which refines the results by evaluating bounding box accuracy and mask quality. Extensive experiments demonstrate the effectiveness of DRR, achieving a precision of 86.7%, recall of 81.7%, and an F-measure of 84.1% on CTW1500, highlighting DRR’s potential to advance scene text detection.
Edge computing is evolving faster and has greater potential to bring a lot of transformation into the agricultural sector. It helps the farming industry to increase crop yield and production with its scientific methods and technological developments. When the farmer knows about the right amount of nutrients, soil requirements, weather updates, and water necessities at the right time, there will be a gradual increase in the crop yield. Edge infrastructure would process data and make faster decisions than other technologies. Edge infrastructure allows faster processing and decision-making. Our research aims to present an efficient edge-computing architecture for sustainable farming in smart agriculture. The objective of the proposed work is to provide a competent and cost-effective computing model for remote farmers with bandwidth limitations. The proposed edge computing-based Agro-economic model can transform conventional agriculture practices by improving speed, cost, efficiency, and latency with better data processing and decision-making. We have also reduced data risks by reducing infrastructure and operational expenditure and costs. This model provides an optimal solution for sustainable farming by addressing the challenges of current technologies and enabling a smarter approach.
Malignant melanoma is a lethal form of skin cancer, where early and precise diagnosis is essential for improving survival. While convolutional neural networks (CNNs) have shown promise in dermoscopic image analysis, they often neglect patient metadata that clinicians routinely consider. Vision-language models (VLMs) provide a unified architecture for multimodal understanding; however, their utility in clinical domains remains limited due to domain mismatch and lack of adaptation. To address these limitations, we propose a retrieval-augmented framework that integrates pre-trained VLMs with a multimodal vector database of dermoscopic images and patient metadata. For each input, the system retrieves semantically similar cases and inserts them into structured prompts, which enable few-shot classification without fine-tuning. Our method outperforms image-based, text-based, and embedding-fusion baselines by yielding higher F1-scores and reducing both false positives and false negatives. Our findings support retrieval-augmented prompting as a reliable and scalable approach to clinical decision support in melanoma diagnosis.
