26 accepted papers
Class-Incremental Learning (CIL) seeks to acquire new classes over time without erasing prior knowledge. While recent methods leverage pre-trained models (PTMs) to curb forgetting, they largely optimize the feature extractor and overlook the crucial classification head. In this work, we advance a si
Recent advances in Transformer-based Neural Operators have enabled significant progress in data-driven solvers for Partial Differential Equations (PDEs). Most current research has focused on reducing the quadratic complexity of attention to address the resulting low training and inference efficienc
B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Tr…
As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training process into sub-tasks and using parallel computation can effectively reduce training…
This paper addresses the challenge of discontinuity in goal-achievement capabilities observed in Goal-conditioned Reinforcement Learning (GCRL) algorithms. Through a theoretical analysis, we identify that the reuse of successful trajectories or policies during training can aid in achieving adjacent…
Class-Incremental Learning (CIL) enables models to continuously learn new classes while mitigating catastrophic forgetting. Recently, Pre-Trained Models (PTMs) have greatly enhanced CIL performance, even when fine-tuning is limited to the first task. This advantage is particularly beneficial for CIL…
Logit-based knowledge distillation (KD) is commonly used to mitigate catastrophic forgetting in class-incremental learning (CIL) caused by data distribution shifts. However, the strict match of logit values between student and teacher models conflicts with the cross-entropy (CE) loss objective of le…
Monocular endoscopic scene reconstruction is challenging due to limited viewpoints and interference from surgical instruments. While 3D Gaussian-based methods are popular for their strong reconstruction capabilities and efficiency, they often rely on sensors or stereo depth, resulting in blurred tis…
Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances in distinct representation spaces isolates the constraints of relations on instances…
Deep learning-based bioacoustic audio analysis holds immense potential across various applications. However, existing studies in bioacoustics often focus on a limited number of species, potentially hindering the transferability of models across different species. Furthermore, the manual annotation o…
Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on…
Many existing face forgery detection methods primarily revolve around learning general representations on predefined datasets and subsequently crossing these static representations to other datasets. However, these approaches could lead to catastrophic forgetting in real-world scenarios, especially…
Image deblurring is an ill-posed task, where exists infinite feasible solutions for blurry images. Modem deep learning approaches usually discard the learning of blur kernels and directly employ end-to-end supervised learning. However, supervised learning can’t handle ill-posed tasks appropriately.…
The explosion of rumors on social media has adversely affected cyber security and our lives, increasing the urgent demand for rumor detection. Existing detection methods focus on exploring signals of deception from textual contents and rumor propagation structures, without fully considering user’s l…
Real-world Temporal Knowledge Graphs keep growing with time and new entities and facts emerge continually, necessitating a model that can extrapolate to future timestamps and transfer knowledge for new components. Therefore, our work first dives into this more realistic issue, lifelong TKG reasoning…
Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context. However, the fully connected way of self-attention may scatter the attention distrib…
Heterogeneous behaviours are widespread in many multi-agent tasks, which have not been paid much attention in the community of multi-agent reinforcement learning. It would be a key factor for improving the learning performance to efficiently characterize and automatically find heterogeneous behaviou…
With the rapid development of automatic fake news detection technology, fact extraction and verification (FEVER) has been attracting more attention. The task aims to extract the most related fact evidences from millions of open-domain Wikipedia documents and then verify the credibility of correspond…
Ultrasound tongue imaging is an attractive way for speech production study as it provides an effective visualization for the vocal tract. Automatic classification of phonetic segments (tongue shapes) from raw ultrasound data is vital for further interpretation. Recently, deep learning-based approach…
Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extrac…
A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It…
The communication among agents is important for Multi-Agent Reinforcement Learning (MARL). In this work, we propose GraphComm, a method makes use of the relation-ships among agents for MARL communication. GraphComm takes the explicit relations (e.g., agent types), which can be provided through some…
Argumentation mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of argumentation components. Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level. Howe…
Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Attentional Fused Temporal Transformation Network (AttnTTN) for action recognition on top of popular Temporal Segment Network (T…
Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn r…
It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations. Most existing methods tend to solve this problem by using a two-phase learning procedure, i.e., multiple instance learning detec…