This report presents the overview of the runs related to Ad-hoc Video Search (AVS) and Activities in Extended Video (ActEV) tasks on behalf of the ITI-CERTH team. Our participation in the AVS task involves a collection of five cross-modal deep network architectures and numerous pretrained models, which are used to calculate the similarities between video shots and queries. These calculated similarities serve as input to a trainable neural network that effectively combines them. During the retrieval stage, we also introduce a normalization step that utilizes both the current and previous AVS queries for revising the combined video shot-query similarities. For the ActEV task, we adapt our framework to support a rule-based classification to overcome the challenges of detecting and recognizing activities in a multi-label manner while experimenting with two separate activity classifiers.