NEUCORE: Neural Concept Reasoning for Composed Image Retrieval
Shu Zhao Huijuan Xu
Composed image retrieval which combines a reference image and a text modifier
to identify the desired target image is a challenging task, and requires the
model to comprehend both vision and language modalities and their interactions.
Existing approaches focus on holistic multi-modal interaction modeling, and
ignore the composed and complimentary property between the reference image and
text modifier. In order to better utilize the complementarity of multi-modal
inputs for effective information fusion and retrieval, we move the multi-modal
understanding to fine-granularity at concept-level, and learn the multi-modal
concept alignment to identify the visual location in reference or target images
corresponding to text modifier. Toward the end, we propose a NEUral COncept
REasoning (NEUCORE) model which incorporates multi-modal concept alignment and
progressive multimodal fusion over aligned concepts. Specifically, considering
that text modifier may refer to semantic concepts not existing in the reference
image and requiring to be added into the target image, we learn the multi-modal
concept alignment between the text modifier and the concatenation of reference
and target images, under multiple-instance learning framework with image and
sentence level weak supervision. Furthermore, based on aligned concepts, to
form discriminative fusion features of the input modalities for accurate target
image retrieval, we propose a progressive fusion strategy with unified
execution architecture instantiated by the attended language semantic concepts.
Our proposed approach is evaluated on three datasets and achieves
state-of-the-art results.