Due to the absence of fine structure and texture information, existing
fusion-based few-shot image generation methods suffer from unsatisfactory
generation quality and diversity. To address this problem, we propose a novel
feature Equalization fusion Generative Adversarial Network (EqGAN) for few-shot
image generation. Unlike existing fusion strategies that rely on either deep
features or local representations, we design two separate branches to fuse
structures and textures by disentangling encoded features into shallow and deep
contents. To refine image contents at all feature levels, we equalize the fused
structure and texture semantics at different scales and supplement the decoder
with richer information by skip connections. Since the fused structures and
textures may be inconsistent with each other, we devise a consistent
equalization loss between the equalized features and the intermediate output of
the decoder to further align the semantics. Comprehensive experiments on three
public datasets demonstrate that, EqGAN not only significantly improves
generation performance with FID score (by up to 32.7%) and LPIPS score (by up
to 4.19%), but also outperforms the state-of-the-arts in terms of accuracy (by
up to 1.97%) for downstream classification tasks.

为了解决现有融合式少样本图像生成方法在生成质量和多样性上的不足，我们提出了一种新颖的特征均衡融合生成对抗网络（EqGAN）来进行少样本图像生成。通过将编码特征解耦为浅层内容和深层内容，并使用两个独立分支分别融合结构和纹理，我们能够在各个特征层次上细化图像内容，通过跳跃连接为解码器提供更丰富的信息。通过等化不同尺度的融合结构和纹理语义，并通过一致性等化损失将等化特征与解码器的中间输出进一步对齐，以解决融合结构和纹理可能不一致的问题。在三个公开数据集上的综合实验证明，EqGAN 不仅在 FID 分数（最高提升 32.7%）和 LPIPS 分数（最高提升 4.19%）方面显著提高了生成性能，还在下游分类任务的准确性方面超过了现有最优方法（最高提升 1.97%）。

EqGAN: 特征均衡融合用于少样本图像生成

EqGAN: Feature Equalization Fusion for Few-shot Image Generation

The goal of the audio-visual segmentation (AVS) task is to segment the
sounding objects in the video frames using audio cues. However, current
fusion-based methods have the performance limitations due to the small
receptive field of convolution and inadequate fusion of audio-visual features.
To overcome these issues, we propose a novel \textbf{Au}dio-aware
query-enhanced \textbf{TR}ansformer (AuTR) to tackle the task. Unlike existing
methods, our approach introduces a multimodal transformer architecture that
enables deep fusion and aggregation of audio-visual features. Furthermore, we
devise an audio-aware query-enhanced transformer decoder that explicitly helps
the model focus on the segmentation of the pinpointed sounding objects based on
audio signals, while disregarding silent yet salient objects. Experimental
results show that our method outperforms previous methods and demonstrates
better generalization ability in multi-sound and open-set scenarios.

通过引入多模式变压器架构来深度融合和聚合音频 - 视觉特征，我们提出了一种新颖的音频感知查询增强转换器 (AuTR)，用于解决音频 - 视觉分割任务。实验结果表明，我们的方法在多声音和开放场景中具有更好的普适性和性能优势。