ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation This paper proposes two entropy-based methods for unsupervised domain adaptation in semantic segmentation. The first method minimizes the entropy of pixel-wise predictions on the target domain, while the second uses adversarial training to enforce consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks. Semantic segmentation is a key problem in computer vision, but models trained on one domain often perform poorly on another. This paper addresses the task of unsupervised domain adaptation in semantic segmentation using entropy-based losses. The authors propose two complementary methods: (1) an entropy loss that directly penalizes low-confidence predictions on the target domain, and (2) an adversarial loss that enforces consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks. The first method, direct entropy minimization, minimizes the entropy of the target predictions. This is done by minimizing the sum of weighted self-information maps. The second method, adversarial training, uses a discriminator network to enforce consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks.ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation This paper proposes two entropy-based methods for unsupervised domain adaptation in semantic segmentation. The first method minimizes the entropy of pixel-wise predictions on the target domain, while the second uses adversarial training to enforce consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks. Semantic segmentation is a key problem in computer vision, but models trained on one domain often perform poorly on another. This paper addresses the task of unsupervised domain adaptation in semantic segmentation using entropy-based losses. The authors propose two complementary methods: (1) an entropy loss that directly penalizes low-confidence predictions on the target domain, and (2) an adversarial loss that enforces consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks. The first method, direct entropy minimization, minimizes the entropy of the target predictions. This is done by minimizing the sum of weighted self-information maps. The second method, adversarial training, uses a discriminator network to enforce consistency in the weighted self-information between source and target domains. The methods are evaluated on two challenging "synthetic-2-real" benchmarks, GTA5→Cityscapes and SYNTHIA→Cityscapes, and show state-of-the-art performance. The proposed approaches are also applicable to object detection tasks.