18 accepted papers
Data-free continual model merging (DFCMM) aims to fuse independently fine-tuned models into a single backbone that evolves with incoming tasks without accessing task data. This paper formulate two fundamental desiderata for DFCMM: transparency, avoiding interference with earlier tasks, and fidelity,…
Efficient adaptation between Egocentric (Ego) and Exocentric (Exo) views is crucial for applications such as human-robot cooperation. However, the success of most existing Ego-Exo adaptation methods relies heavily on target-view data for training, thereby increasing computational and data collection
Unlike traditional Multimodal Class-Incremental Learning (MCIL) methods that focus only on vision and text, this paper explores MCIL across vision, audio and text modalities, addressing challenges in integrating complementary information and mitigating catastrophic forgetting. To tackle these issues…
Continual model merging integrates independently fine-tuned models sequentially without access to the original training data, offering a scalable and efficient solution for continual learning. However, existing methods face two critical challenges: parameter interference among tasks, which leads to…
Distillation strategies are currently the primary approaches for mitigating forgetting in class incremental learning (CIL). Existing methods generally inherit previous knowledge from a single teacher. However teachers with different mechanisms are talented at different tasks and inheriting diverse k…
Link prediction in directed graphs offers a solution for uncovering detailed and accurate relationships among distinct entities. Unlike conventional link prediction in undirected graphs, the task becomes more intricate in directed graphs as it involves predicting both associations and orientations.…
Non-exemplar class incremental learning (NECIL) aims to continuously assimilate new knowledge without forgetting previously acquired ones when historical data are unavailable. One of the generative NECIL methods is to invert the images of old classes for joint training. However these synthetic image…
Continual learning aims to equip deep neural networks (DNNs) with the capability to continuously learn new knowledge without catastrophic forgetting. Currently, there is significant attention on multimodal continual activity recognition from a egocentric perspective. However, the issue of modality i…
Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, we find a new type of bias appearing in continual learning, coined as task-induc…
Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting throu…
Recently, with the development of the Transformer, re-identification (ReID) has great success in various applications. Existing works prefer to utilize the Transformer’s highest-level information as its discriminative feature, which focuses on a few concentrated parts or areas. However, in ReID file…
Object detection aims to accurately locate and classify objects in an image, which requires precise object representations. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous a…
Instance segmentation has achieved siginificant progress in the presence of correctly annotated datasets. Yet, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. In addition, limited experience and knowledge of annotators can also lead to mislabeled object…
Object detection combines object classification and object localization problems. Most existing object detection methods usually locate objects by leveraging regression networks trained with Smooth L1 loss function to predict offsets between candidate boxes and objects. However, this loss function a…
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction…
Scene Parsing is a challenging task in computer vision, which can be formulated as a pixel-wise classification problem. Existing deep-learning-based methods usually use one general classifier to recognize all object categories. However, the general classifier easily makes some mistakes in dealing wi…
Referring expression image segmentation aims to segment out the object referred by a natural language query expression. Without considering the specific properties of visual and textual information, existing works usually deal with this task by directly feeding a foreground/background classifier wit…
Objective image quality assessment (IQA) models aim to automatically predict human visual perception of image quality and are of fundamental importance in the field of image processing and computer vision. With an increasing number of IQA models proposed, how to fairly compare their performance beco…