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Paper address: https://arxiv .org/abs/2312.12198
- On the RefCOCO, RefCOCO and G-Ref data sets, MagNet significantly surpassed all previous optimal algorithms, The core indicator of overall Interaction over Union (oIoU) increased significantly by 2.48 percentage points. The visualization results also confirm that MagNet has excellent performance in processing complex scenes and language expressions.
Method
1.Mask Grounding
##As shown in Figure 4, in order to further improve the model performance, the author also proposed a cross-modal Alignment Module Modality Alignment Module (CAM), which enhances language-image alignment by injecting global context priors into image features before performing language-image fusion. CAM first generates K feature maps of different pyramid scales using pooling operations with different window sizes. Then, each feature map is passed through a 3-layer MLP to better extract global information and performs a cross-attention operation with another modality. Next, all output features are upsampled to the original feature map size by bilinear interpolation and concatenated in the channel dimension. Subsequently, a 2-layer MLP is used to reduce the number of concatenated feature channels back to the original dimensions. To prevent multimodal signals from overwhelming the original signal, a gated unit with Tanh nonlinearity is used to modulate the final output. Finally, this gated feature is added back to the input features and passed to the next stage of the image or language encoder. In the authors' implementation, CAM is added at the end of each stage of the image and speech encoders.
Experiment

##In Figure 6, we can see that the visualization results of MagNet are also It stands out, outperforming the baseline LAVT in many difficult scenarios.
This article delves into the field of reference segmentation (RIS) challenges and current issues, especially the shortcomings in fine-grained language-image alignment. In response to these problems, researchers from Tsinghua University and Bosch Central Research Institute proposed a new method called MagNet, which comprehensively improves language by introducing the auxiliary task Mask Grounding, a cross-modal alignment module and a cross-modal alignment loss function. and the alignment effect between images. Experiments prove that MagNet achieves significantly better performance on the RefCOCO, RefCOCO and G-Ref data sets, surpassing the previous state-of-the-art algorithms and showing strong generalization capabilities. The visualization results also confirm the superiority of MagNet in processing complex scenes and language expressions. This research provides useful inspiration for the further development of the field of reference segmentation and is expected to promote greater breakthroughs in this field.
This paper comes from the Department of Automation, Tsinghua University (https:/ /www.au.tsinghua.edu.cn) and Bosch Central Research Institute (https://www.bosch.com/research/). One of the first authors of the paper, Zhuang Rongxian, is a doctoral student at Tsinghua University and is an intern at Bosch Academia Sinica; the project leader is Dr. Qiu Xuchong, a senior R&D scientist at Bosch Academia Sinica; the corresponding author is Professor Huang Gao from the Department of Automation, Tsinghua University.
The above is the detailed content of CVPR 2024 | Good at processing complex scenes and language expressions, Tsinghua & Bosch proposed a new instance segmentation network architecture MagNet. For more information, please follow other related articles on the PHP Chinese website!

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