Updated ICASSP paper

docs/_layouts/home.html

@@ -93,7 +93,7 @@ <h2>Research Interests</h2>
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             <h2 id="news">News & Activities</h2>
             <ul>
-                <li>Feb 2024: Uploaded paper "Feature Denoising for Low-Light Instance Segmentation using Weighted Non-Local Blocks" to arXiv</li>
+                <li>Dec 2024: Paper "Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation" was accepted to ICASSP 2025</li>
             </ul>
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         <hr />
@@ -130,7 +130,9 @@ <h2 id="publications">Publications</h2>
             <h5>Selected papers:</h5>
             {% for post in site.posts %}
                 {% if post.featured %}
-                    <b>{{ post.title }}</b><p>{{ post.authors | jsonify }}</p>
+                    <!-- <b>{{ post.title }}</b><p>{{ post.authors | jsonify }}</p> -->
+                    <b><a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a></b>
+                    {% for author in post.authors}<a>{{ author }}, </a>{% endfor %}
                 {% endif %}
             {% endfor %}
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docs/_posts/2024-12-20-LoIS.md

@@ -0,0 +1,29 @@
+---
+title: Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation
+alt_title: Low-Light Instance Segmentation
+project: Segmentation and Tracking in Low-Light
+about: We propose a novel generic block for denoising the feature maps of low-light images, which can be easily integrated into existing instance segmentation frameworks.
+authors:
+    names: [J. Lin, N. Anatrasirichai, D. Bull]
+    affiliations: [University of Bristol, University of Bristol, University of Bristol]
+    urls: [https://joannelin168.github.io/, https://pui-nantheera.github.io/, https://david-bull.github.io/]
+funders: MyWorld Strength in Places
+# collaborators:
+# poster:
+# poster_thumbnail:
+paper: Currently unavailable
+arxiv: https://arxiv.org/abs/2402.18307
+supplementary:
+github:
+citation: |
+    @article{lin2024lowlightsegm,
+        title={Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation},
+        author={Lin, Joanne and Anatrasirichai, Nantheera and Bull, David},
+        year={2025},
+        publisher={ICASSP}}
+featured: true
+
+layout: post
+---
+
+Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast. In this paper, we propose an end-to-end solution to address this challenging task. Our proposed method implements weighted non-local blocks (wNLB) in the feature extractor. This integration enables an inherent denoising process at the feature level. As a result, our method eliminates the need for aligned ground truth images during training, thus supporting training on real-world low-light datasets. We introduce additional learnable weights at each layer in order to enhance the network's adaptability to real-world noise characteristics, which affect different feature scales in different ways. Experimental results on several object detectors show that the proposed method outperforms the pretrained networks with an Average Precision (AP) improvement of at least +7.6, with the introduction of wNLB further enhancing AP by upto +1.3.

docs/_site/2024/02/05/LoIS.html → docs/_site/2024/12/20/LoIS.html

@@ -106,23 +106,20 @@ <h2 id="downloads">Downloads</h2>
         <hr />
 
 
-        <div class="container-md">
+        <!-- <div class="container-md">
             <h2>Code (on Github)</h2>
             <p>Code will be available soon.</p>
         </div>
-        <hr />
+        <hr /> -->
 
 
         <div class="container-md">
             <h2>Citation</h2>
-            <pre class="citation">@article{hill2020habnet,
-    title={HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms},
-    author={Hill, Paul R and Kumar, Anurag and Temimi, Marouane and Bull, David R},
-    journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
-    volume={13},
-    pages={3229--3239},
-    year={2020},
-    publisher={IEEE}}
+            <pre class="citation">@article{lin2024lowlightsegm,
+title={Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation},
+author={Lin, Joanne and Anatrasirichai, Nantheera and Bull, David},
+year={2025},
+publisher={ICASSP}}
 </pre>
         </div>
     </div>