Class-Incremental Grouping Network for Continual Audio-Visual Learning

Continual learning is a challenging problem in which models need to be trained on non-stationary data across sequential tasks for class-incremental learning. While previous methods have focused on using either regularization or rehearsal-based frameworks to alleviate catastrophic forgetting in image classification, they are limited to a single modality and cannot learn compact class-aware cross-modal representations for continual audio-visual learning. To address this gap, we propose a novel class-incremental grouping network (CIGN) that can learn category-wise semantic features to achieve continual audio-visual learning. Our CIGN leverages learnable audio-visual class tokens and audio-visual grouping to continually aggregate class-aware features. Additionally, it utilizes class tokens distillation and continual grouping to prevent forgetting parameters learned from previous tasks, thereby improving the model's ability to capture discriminative audio-visual categories. We conduct extensive experiments on VGGSound-Instruments, VGGSound-100, and VGG-Sound Sources benchmarks. Our experimental results demonstrate that the CIGN achieves state-of-the-art audio-visual class-incremental learning performance. Code is available at https://github.com/stoneMo/CIGN.

Audio-Visual Class-Incremental Learning

In this paper, we introduce audio-visual class-incremental learning, a class-incremental learning scenario for audio-visual video recognition. We demonstrate that joint audio-visual modeling can improve class-incremental learning, but current methods fail to preserve semantic similarity between audio and visual features as incremental step grows. Furthermore, we observe that audio-visual correlations learned in previous tasks can be forgotten as incremental steps progress, leading to poor performance. To overcome these challenges, we propose AV-CIL, which incorporates Dual-Audio-Visual Similarity Constraint (D-AVSC) to maintain both instance-aware and class-aware semantic similarity between audio-visual modalities and Visual Attention Distillation (VAD) to retain previously learned audio-guided visual attentive ability. We create three audio-visual class-incremental datasets, AVE-Class-Incremental (AVE-CI), Kinetics-Sounds-Class-Incremental (K-S-CI), and VGGSound100-Class-Incremental (VS100-CI) based on the AVE, Kinetics-Sounds, and VGGSound datasets, respectively. Our experiments on AVE-CI, K-S-CI, and VS100-CI demonstrate that AV-CIL significantly outperforms existing class-incremental learning methods in audio-visual class-incremental learning. Code and data are available at: https://github.com/weiguoPian/AV-CIL_ICCV2023.

LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning

Transformer-based models, such as BERT, have revolutionized various language tasks, but still struggle with large file classification due to their input limit (e.g., 512 tokens). Despite several attempts to alleviate this limitation, no method consistently excels across all benchmark datasets, primarily because they can only extract partial essential information from the input file. Additionally, they fail to adapt to the varied properties of different types of large files. In this work, we tackle this problem from the perspective of correlated multiple instance learning. The proposed approach, LaFiCMIL, serves as a versatile framework applicable to various large file classification tasks covering binary, multi-class, and multi-label classification tasks, spanning various domains including Natural Language Processing, Programming Language Processing, and Android Analysis. To evaluate its effectiveness, we employ eight benchmark datasets pertaining to Long Document Classification, Code Defect Detection, and Android Malware Detection. Leveraging BERT-family models as feature extractors, our experimental results demonstrate that LaFiCMIL achieves new state-of-the-art performance across all benchmark datasets. This is largely attributable to its capability of scaling BERT up to nearly 20K tokens, running on a single Tesla V-100 GPU with 32G of memory.

MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning

Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation across different programming languages has been shown to be more effective than learning from single-language datasets, since more training data from multi-language datasets improves the model's ability to extract language-agnostic information from source code. However, existing multi-language models overlook the language-specific information which is crucial for downstream tasks that is training on multi-language datasets, while only focusing on learning shared parameters among the different languages. To address this problem, we propose MetaTPTrans, a meta learning approach for multilingual code representation learning. MetaTPTrans generates different parameters for the feature extractor according to the specific programming language of the input source code snippet, enabling the model to learn both language-agnostics and language-specific information. Experimental results show that MetaTPTrans improves the F1 score of state-of-the-art approaches significantly by up to 2.40 percentage points for code summarization, a language-agnostic task; and the prediction accuracy of Top-1 (Top-5) by up to 7.32 (13.15) percentage points for code completion, a language-specific task.

Checking Patch Behaviour against Test Specification

Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: similar failing test cases should require similar patches. We then propose BATS, an unsupervised learning-based system to predict patch correctness by checking patch Behaviour Against failing Test Specification. BATS exploits deep representation learning models for code and patches: for a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases in order to identify the associated applied patches, which are then used as a proxy for assessing generated patch correctness. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0.557 to 0.718 and a recall between 0.562 and 0.854 in identifying correct patches. Compared against previous work, we demonstrate that our approach outperforms state-of-the-art performance in patch correctness prediction, without the need for large labeled patch datasets in contrast with prior machine learning-based approaches. While BATS is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: used in conjunction with a recent approach implementing supervised learning, BATS improves the overall recall in detecting correct patches. We finally show that BATS can be complementary to the state-of-the-art PATCH-SIM dynamic approach of identifying the correct patches for APR tools.

Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

Ride-hailing demand prediction is an important prediction task in traffic prediction. An accurate prediction model can help the platform pre-allocate resources in advance to improve vehicle utilization and reduce the wait-time. This task is challenging due to the complicated spatial-temporal relationships among regions. Most existing methods mainly focus on Euclidean correlations among regions. Though there are some methods that use Graph Convolutional Networks (GCN) to capture the non-Euclidean pair-wise correlations, they only rely on the static topological structure among regions. Besides, they only consider fixed graph structures at different time intervals. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Graph Attention Network (STDGAT) to predict the taxi demand of multiple connected regions in the near future. The method uses Graph Attention Network (GAT), which achieves the adaptive allocation of weights for other regions, to capture the spatial information. Furthermore, we implement a Dynamic Graph Attention mode to capture the different spatial relationships at different time intervals based on the actual commuting relationships. Extensive experiments are conducted on a real-world large scale ride-hailing demand dataset, the results demonstrate the superiority of our method over existing methods.

STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction

As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities. An accurate prediction method can help BSS schedule resources in advance to meet the demands of users, and definitely improve operating efficiencies of it. However, most of the existing methods for similar tasks just utilize spatial or temporal information independently. Though there are some methods consider both, they only focus on demand prediction in a single location or between location pairs. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Interval Network (STDI-Net). The method predicts the number of renting and returning orders of multiple connected stations in the near future by modeling joint spatial-temporal information. Furthermore, we embed an additional module that generates dynamical learnable mappings for different time intervals, to include the factor that different time intervals have a strong influence on demand prediction in BSS. Extensive experiments are conducted on the NYC Bike dataset, the results demonstrate the superiority of our method over existing methods.