SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 4: MAP scores on Amazon  $\rightarrow$  Dslr task with code lengths varying from 16 to 128 for single-domain retrieval.
Bit	16	32	48	64	96	128
SH [62]	30.54	35.66	40.84	42.50	44.01	45.64
ITQ [17]	40.83	49.27	53.92	56.16	59.31	59.41
DSH [34]	22.45	33.38	37.01	40.09	43.64	46.31
LSH [16]	16.04	26.18	34.36	39.68	45.60	49.04
SGH [24]	38.67	45.59	50.77	53.57	57.70	57.37
OCH [35]	33.30	41.65	48.18	50.78	50.06	53.74
ITQ+ [79]	35.03	42.62	41.85	43.12	39.82	39.12
LapITQ+ [79]	37.60	42.91	43.24	44.55	42.01	38.87
GTH-g [74]	37.11	45.69	50.67	50.22	54.37	55.81
DAPH [21]	46.74	49.43	56.10	58.63	59.82	60.41
PWCF [22]	49.94	53.05	58.26	59.08	60.11	62.35
DHLing [64]	52.08	56.43	59.92	60.17	61.19	63.44
PEACE [58]	55.43	57.89	60.23	61.21	62.30	64.14
Ours	61.25	62.65	65.14	67.06	68.20	70.04
Table 5: MAP scores on Product  $\rightarrow$  Real task with code lengths varying from 16 to 128 for single-domain retrieval.
Bit	16	32	48	64	96	128
SH [62]	13.15	18.71	22.51	22.57	21.27	20.66
ITQ [17]	20.07	29.64	31.62	33.15	33.95	34.81
DSH [34]	6.10	11.44	12.21	16.61	16.46	14.45
LSH [16]	5.84	10.62	15.46	17.57	21.62	24.92
SGH [24]	18.97	26.18	30.61	32.61	33.82	34.97
OCH [35]	13.45	21.14	23.65	25.34	25.99	28.02
ITQ+ [79]	15.60	20.60	24.95	24.96	24.92	24.05
LapITQ+ [79]	16.78	22.26	22.76	22.29	22.33	21.85
GTH-g [74]	15.05	21.20	25.64	27.67	29.05	28.40
DAPH [21]	20.77	29.01	31.60	33.35	34.22	34.92
PWCF [22]	24.80	34.03	36.86	37.98	38.43	39.14
DHLing [64]	27.81	36.05	40.82	40.91	43.45	44.07
PEACE [58]	28.99	37.93	41.42	42.97	46.51	47.29
Ours	34.88	44.83	48.38	49.91	53.55	54.40
- Office-Home dataset [57] consists of image samples from four different domains, i.e., Art (A), Clipart (C), Product (P) and Real-World (R). There are 65 categories in the dataset. We select two domains as the source and target, respectively, which can produce six transferable image retrieval tasks (P2R, C2R, R2A, R2P, R2C, A2R).
- Office-31 dataset [42] is comprised of 4110 image samples from three different domains, i.e., Amazon (A), Webcam (W) and Dslr (D). Each domain corresponds to a platform where the images are collected. Similarly, we randomly select two domains as the source and target, which can result in six transferable image retrieval tasks (A2D, A2W, W2D, D2A, W2A, D2W).
- Digits consists of two datasets, MNIST [26] and USPS [23] and each contains handwritten digits from ten classes. The digits in both datasets are captured under different conditions. Here we consider two transferable image retrieval tasks (MNIST2USPS and USPS2MNIST).
D.1 Performance of Single-domain Retrieval
We first explore the results on single-domain retrieval, Table 4 compares the average mean average precision (MAP) of different hashing methods under different bit lengths for the single-domain retrieval task, i.e., Amazon  $\rightarrow$  Dslr. The table is divided into two parts: the first part shows the performance of unsupervised hashing methods, including SH, ITQ, DSH, LSH, SGH, and OCH, while the second part shows the performance of transfer learning hashing methods, including ITQ+, LapITQ+, GTH-g, DAPH, PWCF, DHLing, and PEACE. Additionally, the results of IDEA method are highlighted with bold font and pink background, indicating that it achieves the highest MAP values across all hash code lengths. The table shows that IDEA performs better than other hashing methods for simplifying image descriptions and improving image retrieval efficiency. As the hash
Table 6: MAP scores on MNIST  $\rightarrow$  USPS task with code lengths varying from 16 to 128 for single-domain retrieval.
Bit	16	32	48	64	96	128
SH [62]	46.30	47.82	48.58	49.12	49.27	47.81
ITQ [17]	13.39	22.58	33.12	39.67	40.80	40.16
DSH [34]	41.42	45.30	48.72	47.85	48.50	50.76
LSH [16]	26.69	33.49	33.21	35.64	38.92	38.53
SGH [24]	15.60	30.78	35.61	35.55	39.31	41.78
OCH [35]	24.23	32.90	36.10	36.34	39.95	44.36
ITQ+ [79]	50.22	49.66	46.64	44.38	44.53	43.21
LapITQ+ [79]	54.19	55.24	54.57	55.77	54.58	54.08
GTH-g [74]	45.41	39.72	36.66	34.34	32.11	34.73
DAPH [21]	47.53	54.86	57.36	60.15	60.65	60.39
PWCF [22]	50.21	49.41	57.27	60.06	64.71	64.00
DHLing [64]	51.25	50.48	58.44	63.13	65.09	67.02
PEACE [58]	52.77	56.25	61.03	65.27	67.71	69.99
Ours	60.81	63.32	68.05	72.11	74.47	76.73
Figure 5: We use 128-bit Precision-Recall curves to evaluate the performance of our method on the Office-31 and Office-Home datasets.
code length increases, the MAP value of IDEA also increases, ultimately surpassing the performance of all other hashing methods at various hash code lengths. This indicates that IDEA has stronger feature extraction and hashing encoding capabilities and has significant practical value for image retrieval.
Table 5 compares the average MAP of different hashing methods under different bit lengths for the single-domain retrieval task, i.e., Product  $\rightarrow$  Real. We can see that IDEA performs better than other hashing methods at all hash code lengths, especially at higher bit lengths such as 96, 128. Furthermore, the table highlights the results of IDEA in bold font and pink background, indicating that it achieves the highest MAP score at all bit lengths, namely 34.88, 44.83, 48.38, 49.91, 53.55, and 54.40. This further highlights the strong performances and advantages of IDEA, especially in learning compact hash codes and improving retrieval efficiency for single-domain retrieval tasks.
Table 6 compares the average MAP of different hashing methods under different bit lengths for the single-domain retrieval task MNIST  $\rightarrow$  USPS. We can see that IDEA achieves the highest MAP values compared to other methods at all bit lengths, especially at higher bit lengths such as 96 and 128 bits, with MAP values of 74.47 and 76.73 respectively, far exceeding the MAP values of other methods. Additionally, the table highlights the results of IDEA in bold font and pink background, indicating that it achieves the highest MAP values at all bit lengths. Therefore, combining the information from the table, we can conclude that IDEA demonstrates outstanding performance in the
Figure 6: Top-N precision curves with 128 bits on Office-31 and Office-Home datasets.
single-domain retrieval task from MNIST  $\rightarrow$  USPS, significantly enhancing retrieval efficiency and accuracy. This result suggests that IDEA can be a promising approach for improving image retrieval in similar domains.
D.2 Performance of Precision-Recall Curves
Additional Precision-Recall curves for six image retrieval tasks, namely A2R, A2D, A2W, W2D, D2A, and W2A, can be found in this section. These tasks involve retrieving images from one domain and matching them to images from another domain. The Precision-Recall curves offer a visual representation of the balance between precision and recall for each task. As illustrated in Figure 5, these curves give insight into the effectiveness of our approach in dealing with different cross-domain retrieval scenarios. In particular, the curves highlight the strengths of our method in achieving high precision and recall values for each task. The supplementary material provides a detailed analysis of the experimental results, which further demonstrates the robustness and generalized ability of our approach. Overall, these additional results reinforce the effectiveness of our method in improving cross-domain image retrieval performance.
D.3 Performance of Top-N Precision Curve and Top-N Recall Curve
The Top-N precision curve and Top-N recall curve depicted in Figure 6 and Figure 7, respectively, provide valuable insights into the performance of our IDEA algorithm compared to other methods in
Figure 7: Top-N recall curves with 128 bits on Office-31 and Office-Home datasets.
image retrieval tasks. Based on the analysis of these curves, we can conclude that our IDEA algorithm outperforms other comparison methods. This is attributed to the fact that the calculation of the Top-N precision and Top-N recall curves is based on the Hamming distance ranking, and the IDEA algorithm achieves better performance under this metric. Therefore, from the perspective of evaluation metrics, the IDEA algorithm is superior to other comparison methods, as it offers superior precision and recall values for various cross-domain retrieval tasks. Our extensive experimental results further validate the effectiveness of our approach in improving the performance of cross-domain image retrieval.
D.4 Ablation Study
We conduct more ablation studies on more datasets and the results are shown in Table 7. The results can validate our superiority again.
Table 7: Ablation Studies on three benchmark cross-domain retrieval tasks.
Office-Home	Office31
Product2Real	Real2Product	Webcam2Dslr	Dslr2Webcam
IDEA w/o L_v	58.23	60.92	83.88	87.22
IDEA w/o L_MI	57.87	60.46	83.71	87.32
Ours	59.18	61.84	84.97	88.69
Figure 8: Top 10 Images and Precision@10 Examples on the Office-31 Dataset.
D.5 Performance of Different Architectures
We compare the performance of both PEACE and IDEA with different network backbones, i.e., VGG-F and ViT. The compared results are shown in Table 8. From the results, we can have the following observations. Firstly, our IDEA can still achieve better performance under different architectures. Secondly, with strong networks, the performance of our IDEA can improve a lot.
Table 8: MAP scores with respect to different architectures.
Dataset	Office-Home	Office31	Digits
Clipart2Real	Real2Art	Amazon2Dslr	Amazon2Webcam	USPS2MNIST	MNIST2USPS
PEACE (VGG-F)	38.72	42.68	46.69	48.89	60.91	62.84
Ours (VGG-F)	45.71	49.64	48.70	54.43	67.97	67.48
PEACE (ViT)	53.83	56.11	56.05	60.25	79.28	78.37
Ours (ViT)	58.56	64.39	63.27	66.30	83.39	82.96
D.6 Case Study
Figure 8 demonstrates the top 10 retrieval results of the proposed IDEA and the best baseline method PEACE for three query samples on the Office-31 dataset. The blue box indicates the query, the green box indicates the correctly retrieved result, and the red box indicates the incorrectly retrieved result. The results demonstrate that IDEA performs better than PEACE in providing more relevant and user-desired examples, with the latter typically used for cross-domain retrieval. For instance, compared to PEACE, our IDEA returns more examples of the query "Phone," further validating its success in image retrieval tasks. Overall, these findings emphasize the effectiveness of our IDEA method in improving cross-domain image retrieval performance.