[NS][Lab_Seminar_250616]FashionERN: Enhance-and-Refine Network for ...

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FashionERN: Enhance-and-
Refine Network for Composed
Fashion Image Retrieval
Tien-Bach-Thanh Do
Network Science Lab
Dept. ...

2
Introduction
Figure 1: Relatively weak results in (a) and reference dominance in (b).

3
Introduction
● Fashion image retrieval is a significant e-commerce application
● However, relying solely on fashion imag...

4
Introduction
The Problem: Reference Dominance Phenomenon
● Existing methods often use symmetric encoders (e.g., CLIP) pr...

5
Proposed Method
Figure 3: Proposed FashionERN. The Triple-branch Modifier Enhancement (TME) model employs a tri-branch s...

6
Proposed Method
Triple-branch Modifier Enhancement (TME)
● Purpose: Focuses on enriching semantic information of the mod...

7
Proposed Method
Triple-branch Modifier Enhancement (TME)
○ Target Alignment branch (TA):
■ Aligns the modified text moda...

8
Proposed Method
Dual-guided Vision Refinement (DVR)
● Retains critical visual information and preserves key semantic con...

9
Proposed Method
Dual-guided Vision Refinement (DVR)
○ Self-guided Refinement (SR):
■ Filters out non-relevant noise regi...

10
Proposed Method
Network Training
● The training process occurs in two steps:
○ Train the TME model first: Acquires the ...

11
Experiments
● Datasets:
○ FasionIQ: 77,684 images (Dress, Toptee, Shirt categories)
● Evaluation metrics: top-K recall ...

12
Experiments
Table 1: Results on FashionIQ dataset. Best scores are highlighted in bold and underlined formats.

13
Experiments
Table 2: Results on Fashion200k dataset. Best scores
are highlighted in bold and underlined formats.
Table ...

14
Experiments
Figure 4: Qualitative results on FashionIQ. We show reference images and modifiers with blue boxes on the l...

15
Experiments
Figure 5: Attention weights’ (W ) visualization for reference and target images, with their corresponding m...

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Conclusion
● A Triple-branch Modifier Enhancement (TME) model to enrich modified text semantics by injecting reference
...

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