Bidirectional Zero-shot Composed Image Retrieval with Reverse Consistency
Authors: Yuchen Yang, Yu Wang, Yanfeng Wang


Reviewer paper summaries: The paper proposes a Bidirectional Zero-shot Composed Image Retrieval framework with Reverse Consistency (BiRC) to improve robustness and accuracy in CIR tasks without requiring annotated data. The framework operates in two directions: the forward direction uses image captioning and text integration to retrieve target images, while the reverse direction ensures consistency by retrieving the original reference image from the target images and reversed text. Leveraging large language models and a self-check mechanism, BiRC outperforms methods on three datasets (CIRR, FashionIQ, COCO object).
Summary of strengths: S1: The bidirectional zero-shot composed image retrieval framework with reverse consistency is a novel approach that addresses both forward and reverse retrieval processes, enhancing robustness and accuracy.

S2: The method outperforms existing zero-shot composed image retrieval methods on CIRR, FashionIQ, and COCO object datasets
Summary of weaknesses: W1: The reliance on GPT-4, a closed-source LLM, might be a critical weakness. The performance of the BiRC framework heavily depends on the high-quality semantic understanding and text generation capabilities of GPT-4. This limits the reproducibility and accessibility of the method. The reviewer is concerned if an open-source LLM like LLaMA is used, there might be a noticeable performance degradation, as these models may not match the capabilities of GPT-4 in handling complex text integration and modification tasks.

W2: The text integration module incorporates a self-check mechanism and a chain-of-thought process to ensure high-quality text generation. While this enhances performance, it adds considerable complexity to the implementation.
Comments, suggestions and typos: C1: Line 168, "Language Language Models" should be "Large Language Models"

C1: Line 698, "shoule" instead of "should"
Overall assessment scores: 3.5