Defying Imbalanced Forgetting in Class Incremental Learning

We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incremental Learning (CIL), highlights the imbalanced forgetting of learned classes, as their accuracy is similar before the occurrence of catastrophic forgetting. This discovery remains previously unidentified due to the reliance on average incremental accuracy as the measurement for CIL, which assumes that the accuracy of classes within the same task is similar. However, this assumption is invalid in the face of catastrophic forgetting. Further empirical studies indicate that this imbalanced forgetting is caused by conflicts in representation between semantically similar old and new classes. These conflicts are rooted in the data imbalance present in replay-based CIL methods. Building on these insights, we propose CLass-Aware Disentanglement (CLAD) to predict the old classes that are more likely to be forgotten and enhance their accuracy. Importantly, CLAD can be seamlessly integrated into existing CIL methods. Extensive experiments demonstrate that CLAD consistently improves current replay-based methods, resulting in performance gains of up to 2.56%.

Global Prototype Encoding for Incremental Video Highlights Detection

Video highlights detection has been long researched as a topic in computer vision tasks, digging the user-appealing clips out given unexposed raw video inputs. However, in most case, the mainstream methods in this line of research are built on the closed world assumption, where a fixed number of highlight categories is defined properly in advance and need all training data to be available at the same time, and as a result, leads to poor scalability with respect to both the highlight categories and the size of the dataset. To tackle the problem mentioned above, we propose a video highlights detector that is able to learn incrementally, namely \textbf{G}lobal \textbf{P}rototype \textbf{E}ncoding (GPE), capturing newly defined video highlights in the extended dataset via their corresponding prototypes. Alongside, we present a well annotated and costly dataset termed \emph{ByteFood}, including more than 5.1k gourmet videos belongs to four different domains which are \emph{cooking}, \emph{eating}, \emph{food material}, and \emph{presentation} respectively. To the best of our knowledge, this is the first time the incremental learning settings are introduced to video highlights detection, which in turn relieves the burden of training video inputs and promotes the scalability of conventional neural networks in proportion to both the size of the dataset and the quantity of domains. Moreover, the proposed GPE surpasses current incremental learning methods on \emph{ByteFood}, reporting an improvement of 1.57\% mAP at least. The code and dataset will be made available sooner.