SFAN: Selective Filter and Alignment Network for Cross-Modal Retrieval
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the Selective Filter and Alignment Network (SFAN) to improve cross-modal retrieval by filtering irrelevant features and aligning salient information between images and text. SFAN significantly enhances retrieval performance over state-of-the-art methods.
Area Of Science
- Computer Science
- Artificial Intelligence
- Machine Learning
Background
- Cross-modal retrieval faces challenges in effectively bridging visual and textual data.
- Fine-grained matching improves performance but struggles with filtering irrelevant multimodal features.
- Minimizing misalignment interference is crucial for accurate cross-modal retrieval.
Purpose Of The Study
- To propose a novel approach, the Selective Filter and Alignment Network (SFAN), for enhanced cross-modal retrieval.
- To address the challenge of filtering irrelevant features within and between modalities.
- To improve the alignment of salient cross-modal features and reduce misalignment interference.
Main Methods
- Developed modality-specific selective filter modules (SFMs) to implicitly filter redundant information within each modality.
- Introduced a state-space models (SSMs)-based selective alignment module (SAM) for capturing key correspondences.
- Utilized a fusion operation to combine SFM and SAM embeddings for final similarity computation.
Main Results
- The proposed SFAN effectively learns robust patterns for cross-modal retrieval.
- Experiments on Flickr30k, MS-COCO, and MSR-VTT datasets demonstrate significant performance improvements.
- SFAN outperforms existing state-of-the-art cross-modal retrieval methods.
Conclusions
- SFAN offers an effective solution for filtering irrelevant features and improving cross-modal alignment.
- The network architecture enhances the robustness and accuracy of cross-modal retrieval.
- This approach represents a significant advancement in the field of cross-modal retrieval.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Light is absorbed by the rod and cone...
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...