## 1 Introduction  
Surgical gestures, as fine-grained surgical actions intentionally carried out by surgeons during surgery, carry precise control over tiny details, and their actions can produce explicit, perceptible and meaningful results [1]. Recognizing surgical gestures not only aids in understanding the surgeon's intentions and monitoring the state of the operation but also effectively detects and alerts to errors [2], thereby enhancing the safety and efficiency of the surgical process. Currently, research on surgical gesture recognition has primarily focused on robotic surgery, while studies in the field of traditional surgery, particularly open surgery, are relatively scarce. This is partly because surgical robots can easily acquire digital video data and capture quantitative instrument motion trajectories that are difficult to obtain in traditional surgery. These data streams have facilitated the development of data-driven computational models in computer-assisted intervention (CAI) and surgical data science (SDS) [3]. On the other hand, there are many kinds of operations in open surgery, and the acquisition and annotation of video data also face great technical challenges and ethical restrictions. Despite the evident advantages of robotic surgery over traditional surgery, in certain surgical scenarios, traditional surgery, especially open surgery, remains the standard treatment method, with broader indications than robotic surgery. In extreme situations (e.g., battlefield injuries and disasters), open surgery is still indispensable [4]. Therefore, this paper is dedicated to investigating fine-grained surgical gesture recognition in open surgery to explore the application value of this technology in the field of open surgery. In open surgery, the research approach for surgical gesture recognition based on video data is similar to that of previous robotic surgery. Suppose a video of length \(T\) is given, containing a series of video frames \(v_{t}\), \(t\) = 1, \(\ldots\), \(T\). In the task of surgical gesture recognition, predictions need to be made for each time step \(t\) = 1, \(\ldots\), \(T\) to determine the surgical gesture category \(g(t)\in G\) at time \(t\), where \(G\) = {1, \(\ldots\), \(G\)} is the set of surgical gestures. The methods of surgical gesture recognition vary based on the amount of information provided and can be categorized as follows: (i) using only the current video frame, i.e., \(g(t)\) = \(g(v_{t})\) (frame recognition); (ii) using the sequence of frames from the current time step to previous ones, i.e., \(g(t)\) = \(g(v_{k},\ldots,v_{t})\), \(k\geq 1\) (online recognition); or (iii) using the complete sequence of frames of the video, i.e., \(g(t)\) = \(g(v_{1},\ldots,v_{T})\) (offline recognition) [5]. Previous research on surgical gesture recognition has mainly focused on robotic surgery, employing methods that include probabilistic graphical models and deep learning models. In the realm of probabilistic graphical models, hidden Markov models (HMMs) [6; 7] and conditional random fields (CRFs) [8] have been widely used. Although these methods use interpretable features, they typically focus on a few adjacent frames, neglecting long-term temporal dependencies, and require dense kinematic annotations, which are not always available in surgery [9]. However, in recent years, the field has gradually shifted towards deep learning methods. For instance, recurrent neural networks (RNNs) [10] have been widely applied, with long short-term memory (LSTM) networks frequently used for long sequence modeling. Nonetheless, LSTMs have inherent gradient vanishing problems, thus limiting their ability to capture long-term video context [11]. Consequently, researchers have started to seek other methods better suited for handling long sequence data. [PERSON] et al. proposed temporal convolutional networks (TCNs) [12], which use dilated convolutions to expand the receptive field of the convolution layers, capturing longer-range temporal dependencies.