FreqFusion

TPAMI 2024：Frequency-aware Feature Fusion for Dense Image Prediction

The preliminary official implementation of our TPAMI 2024 paper "Frequency-aware Feature Fusion for Dense Image Prediction", which is also available at https://github.com/ying-fu/FreqFusion.

Interested readers are also referred to an insightful Note about this work in Zhihu (TODO).

[TOC]

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.

Highlight✨

• We identify two significant issues present in widely-used standard feature fusion techniques: intra-category inconsistency and boundary displacement. We also introduce feature similarity analysis to quantitatively measure these issues, which not only contributes to the development of new feature fusion methods but also has the potential to inspire advancements in related areas and beyond.
• We propose FreqFusion, which addresses category inconsistency and boundary displacement by adaptively smoothing the high-level feature with spatial-variant low-pass filters, resampling nearby category-consistent features to replace inconsistent features in the high-level feature, and enhancing the high frequency of lower-level features.
• Qualitative and quantitative results demonstrate that FreqFusion increases intra-category similarity and similarity margin, leading to a consistent and considerable improvement across various tasks, including semantic segmentation, object detection, instance segmentation, and panoptic segmentation.

News📰

• 2024-10-24 Updated: We have added a self-implemented CARAFE operator (here), which means you no longer need to rely on the mmcv library. This update allows for greater flexibility in environments where external dependencies are limited or restricted. Please note that if you were previously using the Pretrained model that utilized mmcv's CARAFE implementation, it is recommended to retrain your model for optimal performance with this new operator.

Method Overview💡

Visualization👀

FreqFusion Usage🧠

The clean code for FreqFusion is available here. By utilizing their frequency properties, FreqFusion is capable of enhancing the quality of both low and high-resolution features (referred to as lr_feat and hr_feat, respectively, with the assumption that the size of hr_feat is twice that of lr_feat). The usage is very simple.

ff = FreqFusion(hr_channels=64, lr_channels=64)
hr_feat = torch.rand(1, 64, 32, 32)
lr_feat = torch.rand(1, 64, 16, 16)
_, hr_feat, lr_feat = ff(hr_feat=hr_feat, lr_feat=lr_feat) # lr_feat [1, 64, 32, 32]

Where should I integrate FreqFusion?

You should integrate FreqFusion wherever you need to perform upsampling. FreqFusion is capable of fully utilizing both low and high-resolution features, it can very effectively recover high-resolution, semantically accurate features from low-resolution high-level features, while enhancing the details of high-resolution low-level features.

Example of the concat version for feature fusion (SegNeXt, SegFormer):

You can refer to ham_head.py.

x1, x2, x3, x4 = backbone(img) #x1, x2, x3, x4 in 1/4, 1/8, 1/16, 1/32
x1, x2, x3, x4 = conv1x1(x1), conv1x1(x2), conv1x1(x3), conv1x1(x4) # channel=c
ff1 = FreqFusion(hr_channels=c, lr_channels=c)
ff2 = FreqFusion(hr_channels=c, lr_channels=2 * c)
ff3 = FreqFusion(hr_channels=c, lr_channels=3 * c)
_, x3, x4_up = ff1(hr_feat=x3, lr_feat=x4)
_, x2, x34_up = ff2(hr_feat=x2, lr_feat=torch.cat([x3, x4_up]))
_, x1, x234_up = ff3(hr_feat=x1, lr_feat=torch.cat([x2, x34_up]))
x1234 = torch.cat([x1, x234_up] # channel=4c, 1/4 img size

Another example of the concat version for feature fusion (You may try for UNet):

x1, x2, x3, x4 = backbone(img) #x1, x2, x3, x4 in 1/4, 1/8, 1/16, 1/32
x1, x2, x3, x4 = conv1x1(x1), conv1x1(x2), conv1x1(x3), conv1x1(x4) # conv1x1s in original FPN to align channel=c
ff1 = FreqFusion(hr_channels=c, lr_channels=c)
ff2 = FreqFusion(hr_channels=c, lr_channels=c)
ff3 = FreqFusion(hr_channels=c, lr_channels=c)
y4 = x4 # channel=c
_, x3, y4_up = ff1(hr_feat=x3, lr_feat=y4)
y3 = conv(torch.cat([x3 + y4_up])) # channel=c
_, x2, y3_up = ff2(hr_feat=x2, lr_feat=y3)
y2 = conv(torch.cat([x2 + y3_up])) # channel=c
_, x2, y2_up = ff3(hr_feat=x1, lr_feat=y2)
y1 = conv(torch.cat([x1 + y2_up])) # channel=c

Example of the add version for feature fusion (FPN-based methods):

You can refer to FPN.py.

x1, x2, x3, x4 = backbone(img) #x1, x2, x3, x4 in 1/4, 1/8, 1/16, 1/32
x1, x2, x3, x4 = conv1x1(x1), conv1x1(x2), conv1x1(x3), conv1x1(x4) # conv1x1s in original FPN to align channel=c
ff1 = FreqFusion(hr_channels=c, lr_channels=c)
ff2 = FreqFusion(hr_channels=c, lr_channels=c)
ff3 = FreqFusion(hr_channels=c, lr_channels=c)
y4 = x4
_, x3, y4_up = ff1(hr_feat=x3, lr_feat=y4)
y3 = x3 + y4_up
_, x2, y3_up = ff2(hr_feat=x2, lr_feat=y3)
y2 = x2 + y3_up
_, x2, y2_up = ff3(hr_feat=x1, lr_feat=y2)
y1 = x1 + y2_up

The FreqFusion relies on mmcv libarary, you can install mmcv-full by:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11/index.html

You can refer to https://mmcv.readthedocs.io/en/v1.7.0/get_started/installation.html and select the appropriate installation command depending on the type of system, CUDA version, PyTorch version, and MMCV version.

Tips:

MMData installation may be annoying, and although the adaptive low/high-filter in FreqFusion can use torch.nn.functional.unfold as a replacement (you can try), it may consume a large amount of GPU memory. Therefore, I suggest using MMData for efficiency.

Semantic Segmentation

SegNeXt

Code of SegNeXt

Core modification:

FreqFusion.py

ham_head.py

Method	Backbone	Crop Size	Lr Schd	mIoU
SegNeXt	MSCAN-T	512x512	160k	41.1
SegNeXt + FreqFusion	MSCAN-T	512x512	160k	43.5

Checkpoint:

Method	Backbone	mIoU	Configs	Links
SegNeXt + FreqFusion	MSCAN-T	43.7 (43.5 in paper)	config	ckpt (code: PAMI)

Note:

The original SegNeXt code can be found here.

Our code is based on MMSegmentation. You can install mmseg by:

pip install mmsegmentation==0.24.1

Please refer to get_started.md for more details on installation, and dataset_prepare.md for information on dataset preparation. For further details on code usage, you can refer to this.

You can install mmcv-full by:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11/index.html

For more details on installing and using SegNeXt, please refer to the README file.

Mask2Former

Code of Mask2Former

Core modification:

FreqFusion.py

msdeformattn.py

Mask2Former	Backbone	mIoU
Bilinear	Swin-B	53.9
FreqFusion (Ours)	Swin-B	55.3 (+1.4)
Bilinear	Swin-L	56.1
FreqFusion (Ours)	Swin-L	56.8 (+0.7)

Checkpoint:

Mask2Former	Backbone	mIoU	Configs	Links
FreqFusion	Swin-B	55.7 (55.3 in paper)	config	ckpt (code: PAMI)
FreqFusion	Swin-L	57.0 (56.8 in paper)	config	ckpt (code: PAMI)

Note：

Install Mask2Former.

See Preparing Datasets for Mask2Former.

See Getting Started with Mask2Former.

See installation instructions.

For more details on installing and using Mask2Former, please refer to the README file.

Object Detection

Code for Faster R-CNN, Mask R-CNN, Panoptic FPN: Here (mmdet==2.28.1)

Faster R-CNN（Detection）	Backbone	AP
Nearest	R50	37.5
Deconv	R50	37.3
PixelShuffle	R50	37.5
CARAFE	R50	38.6
IndexNet	R50	37.6
A2U	R50	37.3
FADE	R50	38.5
SAPA-B	R50	37.8
DySample-S+	R50	38.6
DySample+	R50	38.7
FreqFusion (Ours)	R50	39.4
Nearest	R101	39.4
DySample+	R101	40.5
FreqFusion (Ours)	R101	41.0

Checkpoint:

Faster R-CNN	Backbone	Box AP	Configs	Links
FreqFusion	ResNet-50	39.5 (39.4 in paper)	config	ckpt (code: PAMI)
FreqFusion	ResNet-101	41.1 (41.0 in paper)	config	ckpt (code: PAMI)

Instance Segmentation

Model	Backbone	Box AP	Mask AP
Nearest	R50	38.3	34.7
Deconv	R50	37.9	34.5
PixelShuffle	R50	38.5	34.8
CARAFE	R50	39.2	35.4
IndexNet	R50	38.4	34.7
A2U	R50	38.2	34.6
FADE	R50	39.1	35.1
SAPA-B	R50	38.7	35.1
DySample-S+	R50	39.3	35.5
DySample+	R50	39.6	35.7
FreqFusion (Ours)	R50	40.0	36.0
Nearest	R101	40.0	36.0
DySample+	R101	41.0	36.8
FreqFusion (Ours)	R101	41.6	37.4

Checkpoint:

Mask R-CNN	Backbone	Mask AP	Configs	Links
FreqFusion	ResNet-50	36.0	config	ckpt (code: PAMI)
FreqFusion	ResNet-101	37.3	config	ckpt (code: PAMI)

Panoptic Segmentation

Panoptic FPN	Backbone	Params (M)	PQ	PQth	PQst	SQ	RQ
Nearest	R50	46.0	40.2	47.8	28.9	77.8	49.3
Deconv	R50	+1.8	39.6	47.0	28.4	77.1	48.5
PixelShuffle	R50	+7.1	40.0	47.4	28.8	77.1	49.1
CARAFE	R50	+0.2	40.8	47.7	30.4	78.2	50.0
IndexNet	R50	+6.3	40.2	47.6	28.9	77.1	49.3
A2U	R50	+29.2K	40.1	47.6	28.7	77.3	48.0
FADE	R50	+0.1	40.9	48.0	30.3	78.1	50.1
SAPA-B	R50	+0.1	40.6	47.7	29.8	78.0	49.6
DySample-S+	R50	+6.2K	41.1	48.1	30.5	78.2	50.2
DySample+	R50	+49.2K	41.5	48.5	30.8	78.3	50.7
FreqFusion (Ours)	R50	+0.3	42.7	49.3	32.7	79.0	51.9
Nearest	R101	65.0	42.2	50.1	30.3	78.3	51.4
DySample+	R101	+49.2K	43.0	50.2	32.1	78.6	52.4
FreqFusion (Ours)	R101	+0.3	44.0	50.8	33.7	79.4	53.4

Checkpoint:

Panoptic FPN	Backbone	PQ	Configs	Links
FreqFusion	ResNet-50	42.7	config	ckpt (code: PAMI)
FreqFusion	ResNet-101	44.1 (44.0 in paper)	config	ckpt (code: PAMI)

Note:

Original code of mmdet can be found here.

For more details on installing and using mmdetection, please refer to the README file.

Citation

If you use our dataset or code for research, please cite this paper (early access now):

@ARTICLE{2024freqfusion,
  author={Chen, Linwei and Fu, Ying and Gu, Lin and Yan, Chenggang and Harada, Tatsuya and Huang, Gao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Frequency-aware Feature Fusion for Dense Image Prediction}, 
  year={2024},
  volume={1},
  number={1},
  pages={1-18},
  doi={10.1109/TPAMI.2024.3449959}}

Acknowledgment

This code is built using mmsegmentation, Mask2Former, mmdetection libraries.

Contact

If you encounter any problems or bugs, please don't hesitate to contact me at [email protected]. To ensure effective assistance, please provide a brief self-introduction, including your name, affiliation, and position. If you would like more in-depth help, feel free to provide additional information such as your personal website link. I would be happy to discuss with you and offer support.