X-CoT: Explainable Text-to-Video Retrieval via LLM-based
  Chain-of-Thought Reasoning
Authors: name: Prasanna Reddy Pulakurthi, name: Jiamian Wang, name: Majid Rabbani, name: Sohail Dianat, name: Raghuveer Rao, name: Zhiqiang Tao

Prevalent text-to-video retrieval systems mainly adopt embedding models for
feature extraction and compute cosine similarities for ranking. However, this
design presents two limitations. Low-quality text-video data pairs could
compromise the retrieval, yet are hard to identify and examine. Cosine
similarity alone provides no explanation for the ranking results, limiting the
interpretability. We ask that can we interpret the ranking results, so as to
assess the retrieval models and examine the text-video data? This work proposes
X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of
the embedding model-based similarity ranking. We first expand the existing
benchmarks with additional video annotations to support semantic understanding
and reduce data bias. We also devise a retrieval CoT consisting of pairwise
comparison steps, yielding detailed reasoning and complete ranking. X-CoT
empirically improves the retrieval performance and produces detailed
rationales. It also facilitates the model behavior and data quality analysis.
Code and data are available at: https://github.com/PrasannaPulakurthi/X-CoT.