Low-Light Salient Object Detection by Learning to Highlight the Foreground Objects
Why this work is in the frame
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Bibliographic record
Abstract
Previous methods in salient object detection (SOD) mainly focused on favorable illumination circumstances while neglecting the performance in low-light condition, which significantly impedes the development of related down-stream tasks. In this work, considering that it is impractical to annotate the large-scale labels for this task, we present a framework (HDNet) to detect the salient objects in low-light images with the synthetic images. Our HDNet consists of a foreground highlight sub-network (HNet) and an appearance-aware detection sub-network (DNet), both of which can be learned jointly in an end-to-end manner. Specifically, to highlight the foreground objects, we design the HNet to estimate the parameters to adjust the dynamic range for each pixel adaptively, which can be trained via the weak supervision signals of the salient object labels. In addition, we design a simple detection network (DNet) with a contextual feature fusion module and a multi-scale feature refine module for detailed feature fusion and refinement. Furthermore, we contribute the first annotated dataset for salient object detection in low-light images (SOD-LL), including 6,000 labeled synthetic images (SOD-LLS) and 2,000 labeled real images (SOD-LLR). Experimental results on SOD-LL and other low-light videos in the wild demonstrate the effectiveness and generalization ability of our method. Our dataset and code are available at https://github.com/Ylinyuan/HDNet.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it