Frequency-aware Feature Fusion for Dense Image Prediction (2408.12879v1)

Abstract: Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness. The code is made publicly available at https://github.com/Linwei-Chen/FreqFusion.

• The paper introduces Frequency-aware Feature Fusion (FreqFusion), a novel method that improves dense image prediction by addressing intra-category inconsistency and boundary displacement through frequency-aware processing.
• FreqFusion enhances feature consistency using an Adaptive Low-Pass Filter (ALPF) and refines boundaries and consistency with an Offset Generator and Adaptive High-Pass Filter (AHPF).
• Extensive evaluations demonstrate that FreqFusion substantially increases intra-category similarity and reduces boundary displacement across benchmarks like Cityscapes, ADE20K, and MS COCO.

Insightful Overview of "Frequency-aware Feature Fusion for Dense Image Prediction"

The paper, "Frequency-aware Feature Fusion for Dense Image Prediction," presents a thorough investigation into the challenges posed by intra-category inconsistency and boundary displacement in dense image prediction tasks. These tasks, encompassing object detection, semantic segmentation, instance segmentation, and panoptic segmentation, require models to balance semantic richness with spatial precision. The authors address these challenges by proposing a novel feature fusion method named Frequency-aware Feature Fusion (FreqFusion).

Challenges and Motivations

The primary challenge identified by the authors is the typical feature fusion process in hierarchical models, which often fails to maintain consistency within object categories and accurately define object boundaries. Traditional fusion techniques exacerbate these issues, causing intra-category inconsistency through rapid variations in feature values and resulting in blurred boundaries via affected high-frequency information.

Proposed Solution: FreqFusion

FreqFusion introduces a sophisticated feature fusion methodology designed to enhance feature consistency and sharpness at object boundaries. This is achieved through the integration of three primary components:

1. Adaptive Low-Pass Filter (ALPF) Generator: This component dynamically generates spatially-variant low-pass filters to smooth high-level features, reducing intra-class inconsistency during upsampling. By attenuating unnecessary high-frequency components, the ALPF generator ensures that features within the same category appear more consistent.
2. Offset Generator: This module refines features by predicting offsets that aid in resampling feature pixels. The offset generator utilizes local similarity to guide resampling in regions of inconsistency, ensuring that high intra-category similarity features replace inconsistent ones. This process greatly assists in maintaining accurate and clear boundaries.
3. Adaptive High-Pass Filter (AHPF) Generator: To restore high-frequency details inevitably lost during downsampling, the AHPF generator extracts pertinent boundary information to produce clearer object delineations. This enhancement supplements the sharpness of boundary features without reintroducing noise.

Empirical Results

The authors provide extensive quantitative analysis and visualization to substantiate the efficacy of FreqFusion. The proposed method substantially increases intra-category similarity while reducing boundary displacement across diverse benchmarks. This is validated through improvements in semantic metrics like mIoU and boundary-specific metrics like bIoU across notable datasets such as Cityscapes, ADE20K, and MS COCO.

Implications and Future Directions

The implications of this work are considerable for the field of computer vision, particularly in applications requiring fine-grained predictions, such as autonomous driving and medical imaging. By addressing the fundamental limitations of feature fusion in hierarchical vision models, FreqFusion sets a new standard for feature processing in dense prediction tasks.

Future research could extend these findings by exploring computational efficiency improvements, enabling deployment in real-time applications. Additionally, adaptations of FreqFusion for video inputs could address temporal inconsistencies, further broadening its applicability in dynamic scenes.

In summary, the authors present a rigorously evaluated, frequency-aware approach to feature fusion, significantly advancing the state of the art in dense image prediction by tailoring high-frequency content processing to align with object-level semantics and regional boundary details.