We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose $TA^{2}Net$, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. $TA^{2}Net$ also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, $Audible623$, derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on $Audible623$ and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at https://github.com/WenlongWan/Audible623.

Although pre-trained large-scale generative models StyleGAN series have proven to be effective in various editing and translation tasks, they are limited to pre-defined fixed aspect ratio. To overcome this limitation, we propose StyleGAN-$\infty$∞, a model that enables pre-trained StyleGAN to perform arbitrary-ratio conditional synthesis. Our key insight is to distill the expressive StyleGAN features into a StyleBook, such that an arbitrary-ratio condition can be translated to other forms by properly assembling pre-defined StyleBook vectors. To learn and leverage the StyleBook, we employ a network with three distinct stages, each corresponding to StyleBook extraction, StyleBook correspondence learning, and arbitrary-ratio synthesis. Extensive experiments on various conditional synthesis tasks, like super-resolution, sketch synthesis, and semantic synthesis, demonstrate superior performances over state-of-the-art image-to-image translation methods. Moreover, our model can easily generate megapixel images in diverse modalities by taking advantage of different pre-trained StyleGAN models.

Image-level weakly supervised semantic segmentation (WSSS) reduces the cost of semantic segmentation significantly as only category labels are required. In the WSSS pipeline, the initially obtained seed areas are imprecise and should be refined. AffinityNet is a widely used seed area refinement method which learns pixel-level affinity between coordinates to adjust the object regions. However, the capabilities of AffinityNet are limited due to the complex simultaneous exploration of the affinities of all object classes, as the classes have different characteristics. This paper proposes a class-specific AffinityNet (CSANet) in which the affinities of each class are learnt separately by the class-specific affinity layers. Moreover, as the learning task is simple because the affinity of only one class is focused, the information contained by the uncertain neutral regions is extracted to provide additional information in the training of the class-specific affinity layers. The empirical results demonstrate the effectiveness of our method, which captures more precise boundaries and significantly improves segmentation results on the PASCAL VOC 2012 segmentation benchmark.