Video salient object detection using dual-stream spatiotemporal attention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video salient object detection plays an important role in many exciting applications in different areas. However, the existing deep learning-based video salient object detection methods still struggle in scenes of large salient object variabilities and great background scene diversity between and within frames. In this paper, we propose a dual-stream spatiotemporal attention network (DSSANet) for saliency detection in videos. It creatively introduces a multiplex attention mechanism to effectively extract and fuse spatiotemporal features of video salient object over frames in the video, thereby improving saliency detection performance. The DSSANet consists of: (1) A context feature path leverages a novel attention-augmented convolutional LSTM to effectively model the long-range dependency of the great temporal variation in the salient object over frames. (2) A content feature path creatively leverages an attention-based 1D dilated convolution to effectively model the local pixel correlation structure of each pixel in the salient object and the surrounding objects. (3) A refinement fusion module fuses these two features from their paths and further refines the fused feature by an attention-based feature selection. By integrating these three parts, DSSANet accurately detects the salient object from the video. The extensive experiments are performed on four public datasets and demonstrate the effectiveness of DSSANet and the superiority to five state-of-the-art video salient object detection methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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