E3M: Zero-Shot Spatio-Temporal Video Grounding with Expectation-Maximization Multimodal Modulation

Peijun Bao*, Zihao Shao, Wenhan Yang, Boon Poh Ng, Alex Kot ;

"Spatio-temporal video grounding aims to localize the spatio-temporal tube in a video according to the given language query. To eliminate the annotation costs, we make a first exploration to tackle spatio-temporal video grounding in a zero-shot manner. Our method dispenses with the need for any training videos or annotations; instead, it localizes the target object by leveraging pre-trained vision-language models and optimizing within the video and text query during the test time. To enable spatio-temporal comprehension, we introduce a multimodal modulation that integrates the spatio-temporal context into both visual and textual representation. On the visual side, we devise a context-based visual modulation that enhances the visual representation by propagation and aggregation of the contextual semantics. Concurrently, on the textual front, we propose a prototype-based textual modulation to refine the textual representations using visual prototypes, effectively mitigating the cross-modal discrepancy. In addition, to overcome the interleaved spatio-temporal dilemma, we propose an expectation maximization (EM) framework to optimize the process of temporal relevance estimation and spatial region identification in an alternating way. Comprehensive experiments validate that our zero-shot approach achieves superior performance in comparison to several state-of-the-art methods with stronger supervision. The code is available at https://github.com/baopj/E3M."