Source-free object detection (SFOD) faces persistent challenges due to class imbalance–driven context bias and instability in teacher–student training under noisy pseudo-labels. Existing techniques tend to ignore the context bias and class imbalance shift, especially in medical data. To tackle this, we propose Grounded Teacher (GT), a bias-aware source-free framework that grounds the teacher model through relational and semantic regularization.. To explicitly model directional confusions between classes, GT introduces a Relational Context Module (RCM), which maintains an exponential moving average (EMA) estimate of cross-domain contextual bias. Building upon this, a Semantic Augmentation (SA) strategy selectively augments minority and confusable classes through adaptive MixUp in both source-similar and source-dissimilar target regions, improving minority recall without overfitting dominant categories. To stabilize learning under biased pseudo-labels, we design a Semantic-Aware Loss (SAL) that applies diagonally normalized weights, preventing gradient explosion while emphasizing minority majority corrections. Additionally, a frozen Expert branch derived from large vision foundation models (LVFMs) serves as a supervisory reference during training, refining pseudo-label quality without adding inference overhead. GT’s behavior-driven bias quantification makes it broadly applicable across domains without relying on dataset priors. Evaluations on Cityscapes $$\rightarrow $$ Foggy (50.8 mAP) and medical transfers (+5.9 AP $$_{50}$$ on DDSM $$\rightarrow $$ INBreast) show consistent gains and improved minority-class detection, with less than 12% additional training cost. Code and model are available at this link .

We focus on the source-free domain adaptive object detection (SF-DAOD) problem when source data is unavailable during adaptation and the model must adapt to an unlabeled target domain. The majority of approaches for the problem employ a self-supervised approach using a student-teacher (ST) framework where pseudo-labels are generated via a source-pretrained model for further fine-tuning. We observe that the performance of a student model often degrades drastically, due to the collapse of the teacher model, primarily caused by high noise in pseudo-labels, resulting from domain bias, discrepancies, and a significant domain shift across domains. To obtain reliable pseudo-labels, we propose a Target-based Iterative Query-Token Adversarial Network (TITAN), which separates the target images into two subsets: those similar to the source (easy) and those dissimilar (hard). We propose a strategy to estimate variance to partition the target domain. This approach leverages the insight that higher detection variances correspond to higher recall and greater similarity to the source domain. Also, we incorporate query-token-based adversarial modules into a student-teacher baseline framework to reduce the domain gaps between two feature representations. Experiments conducted on four natural imaging datasets and two challenging medical datasets have substantiated the superior performance of TITAN compared to existing state-of-the-art (SOTA) methodologies. We report an mAP improvement of +22.7, +22.2, +21.1, and +3.7 percent over the current SOTA on C2F, C2B, S2C, and K2C benchmarks, respectively.

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Federated learning has emerged as a promising paradigm for collaborative machine learning, enabling multiple clients to train a model while preserving data privacy jointly. Tailored federated learning takes this concept further by accommodating client heterogeneity and facilitating the learning of personalized models. While the utilization of transformers within federated learning has attracted significant interest, there remains a need to investigate the effects of federated learning algorithms on the latest focal modulation-based transformers. In this paper, we investigate this relationship and uncover the detrimental effects of federated averaging (FedAvg) algorithms on Focal Modulation, particularly in scenarios with heterogeneous data. To address this challenge, we propose TransFed, a novel transformer-based federated learning framework that not only aggregates model parameters but also learns tailored Focal Modulation for each client. Instead of employing a conventional customization mechanism that maintains client-specific focal modulation layers locally, we introduce a learn-to-tailor approach that fosters client collaboration, enhancing scalability and adaptation in TransFed. Our method incorporates a hyper network on the server, responsible for learning personalized projection matrices for the focal modulation layers. This enables the generation of client-specific keys, values, and queries.Furthermore, we provide an analysis of adaptation bounds for TransFed using the learn-to-customize mechanism. Through intensive experiments on datasets related to pneumonia classification, we demonstrate that TransFed, in combination with the learn-to-tailor approach, achieves superior performance in scenarios with non-IID data distributions, surpassing existing methods. Overall, TransFed paves the way for leveraging focal Modulation in federated learning, advancing the capabilities of focal modulated transformer models in decentralized environments.

The area of Computer Vision has gone through exponential growth and advancement over the past decade. It is mainly due to the introduction of effective deep-learning methodologies and the availability of massive data. This has resulted in the incorporation of intelligent computer vision schemes to automate the different number of tasks. In this paper, we have worked on similar lines. We have proposed an integrated system for the development of robotic arms, considering the current situation in fruit identification, classification, counting, and generating their masks through semantic segmentation. The current method of manually doing these processes is time-consuming and is not feasible for large fields. Due to this, multiple works have been proposed to automate harvesting tasks to minimize the overall overhead. However, there is a lack of an integrated system that can automate all these processes together. As a result, we are proposing one such approach based on different machine learning techniques. For each process, we propose to use the most effective learning technique with computer vision capability. Thus, proposing an integrated intelligent end-to-end computer vision-based system to detect, classify, count, and identify the apples. In this system, we modified the YOLOv3 algorithm to detect and count the apples effectively. The proposed scheme works even under variable lighting conditions. The system was trained and tested using a standard benchmark i.e., MinneApple. Experimental results show an average accuracy of 91%.