@@ -113,11 +113,13 @@ Example output:
113113<div align =" center " >
114114<figure >
115115 <img src =" resources/v1_vs_v2.png " width =" 600 " >
116- <figcaption >SigLIP vs SigLIP2 Feature Space Comparison</figcaption >
116+ <div >
117+ <figcaption>SigLIP vs SigLIP2 Feature Space Comparison</figcaption>
118+ </div >
117119</figure >
118120</div >
119121
120- ## Orthogonal Transformation Learning
122+ ## Orthogonal Transformation Learning For R and B channels Swap
121123To study orthogonal transformations in feature space:
122124
1231251 . Generate dataset for ` google/siglip2-base-patch16-512 `
@@ -135,7 +137,57 @@ Example output:
135137<div align =" center " >
136138<figure >
137139 <img src =" resources/rb_swap.png " width =" 600 " >
138- <figcaption >RGB Channel Swap in Feature Space</figcaption >
140+ <div >
141+ <figcaption>RGB Channel Swap in Feature Space</figcaption>
142+ </div >
143+ </figure >
144+ </div >
145+
146+ ## Linear Transformation Learning For B Channel Suppression
147+ To study linear transformations in feature space:
148+
149+ 1 . Generate dataset for ` google/siglip2-base-patch16-512 `
150+ 2 . Train reconstructor or use precomputed [ weights] ( https://drive.google.com/file/d/1i-B-5yBpSwcZL3_Z2Dz53jfxiY9T-fkb/view?usp=drive_link )
151+ 3 . Place weights at:
152+ ``` bash
153+ metrics_calculation/precalculated_weights/models--google--siglip2-base-patch16-512.pt
154+ ```
155+ 4 . Run the analysis notebook:
156+ ```
157+ metrics_calculation/b_channel_suppression/understanding_b_suppression.ipynb
158+ ```
159+
160+ Example output:
161+ <div align =" center " >
162+ <figure >
163+ <img src =" resources/b_suppression_all_transformations.png " width =" 600 " >
164+ <div >
165+ <figcaption>B Channel Suppression in Feature Space</figcaption>
166+ </div >
167+ </figure >
168+ </div >
169+
170+ ## Linear Transformation Learning For Colorization
171+ To study linear transformations in feature space:
172+
173+ 1 . Generate dataset for ` google/siglip2-base-patch16-512 `
174+ 2 . Train reconstructor or use precomputed [ weights] ( https://drive.google.com/file/d/1i-B-5yBpSwcZL3_Z2Dz53jfxiY9T-fkb/view?usp=drive_link )
175+ 3 . Place weights at:
176+ ``` bash
177+ metrics_calculation/precalculated_weights/models--google--siglip2-base-patch16-512.pt
178+ ```
179+ 4 . Run the analysis notebook:
180+ ```
181+ metrics_calculation/colorization/understanding_colorization.ipynb
182+ ```
183+
184+ Example output:
185+ <div align =" center " >
186+ <figure >
187+ <img src =" resources/colorized_all_examples.png " width =" 600 " >
188+ <div >
189+ <figcaption>Colorization in Feature Space</figcaption>
190+ </div >
139191</figure >
140192</div >
141193
@@ -153,4 +205,4 @@ If you find this work useful, please cite it as follows:
153205 primaryClass={cs.CV},
154206 url={https://arxiv.org/abs/2506.07803},
155207}
156- ```
208+ ```
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