## 4 Experiments
### Visualization  
**Feature visualization.** To verify that our methods can learn discriminative audio-visual features, we visualize several classes of the features learned by AV-BT and TACMA with the high-dimensional data visualization tool, t-SNE [58], as shown in Figure 7. Specifically, we selected features from categories covering diverse targets, such as people, animals, vehicles, and environmental objects, to demonstrate the adaptability of our approach to a variety of situations. Despite its relatively small sample numbers for a certain class, such as index 23: \"Horse\", good within-class cohesion is still achieved in both audio and visual features. In addition, the discrimination between classes is also greatly enhanced after further information fusion by TACMA, and this is particularly remarkable for the visual stream.  
**Qualitative results.** We evaluated our whole method with several interesting samples from the AVE dataset to verify the design. Predictions made by our networks are presented in Figure 8. Through these representative samples, we can intuitively gain a better understanding of our method, especially how TACMA coordinates with AV-BT for superior performance.  
First, we observed that both of our proposed methods can detect various AVEs in the real world, including human speaking, vehicle sounds, environmental object activities, etc., as shown in in Figure 8b-d. Even if some samples are exceptionally challenging, for example, the visual quality of (e) in Figure 8 is very poor and the sound of the helicopter is almost completely distorted by extreme wind noise, our method still holds up to a certain extent. Generally, based on AV-BT, more accurate predictions can be made after fusion by TACMA.  
Second, our networks only represent the data from a semantic perspective. This conclusion can be obtained from a typical case in Figure 8a, where there is a ringing across the whole timeline while only the first appearing bell is emitting sound. When the camera's view is turned to the second bell, both AV-BT and TACMA still treat the sounding objects as visible (since a bell is still visible even though it's not the one ringing at this  
Figure 7: Illustration of the feature learning of our methods. The classes corresponding to the numbers in the legends are shown in the dashed boxes on the right. _Best viewed in color_.  
moment). We argue that adopting mono audio signals for feature encoding makes it difficult to solve this kind of problem because of the lack of information for tracking sounding targets spatially.  
Last, we designed TACMA to integrate more useful information from the audio-visual pairs that are of various time intervals, and Figure 8f proves that our design meets such expectations to a certain extent. In that example, a man is trimming branches on a tree with a chainsaw and the video is recorded from a first-person perspective. Since the visual content is very unstable and the chainsaw is only captured in very few frames, it is very difficult for AV-BT to deal with such rapidly changing visual content (resulting in predicting all segments as _background_). After further aggregating useful cues with TACMA, the network can grasp more event-related information.