CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science, Peking University（北京大学计算机学院）
Incremental object detection is fundamentally challenged by catastrophic forgetting. A major factor contributing to this issue is background shift, where background categories in sequential tasks may overlap with either previously learned or future unseen classes. To address this, we propose a novel method called Class-Agnostic Shared Attribute Base (CASA) that encourages the model to learn category-agnostic attributes shared across incremental classes. Our approach leverages an LLM to generate candidate textual attributes, selects the most relevant ones based on the current training data, and records their importance in an assignment matrix. For subsequent tasks, the retained attributes are frozen, and new attributes are selected from the remaining candidates, ensuring both knowledge retention and adaptability. Extensive experiments on the COCO dataset demonstrate the state-of-the-art performance of our method.
University of Science and Technology of China（中国科学技术大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major co…
Task-Agnostic Guided Feature Expansion for Class-Incremental Learning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
Inception Institute of Artificial Intelligence（人工智能初始研究院）；University of Central Florida（中佛罗里达大学）
Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques,…
While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model must distinguish between learned and novel classes to properly include samples for training. We introduce a novelty detection method that leverages network confusion caused by training incoming data as a new class. We found that incorporating a class-imbalance during this detection method substantially enhances performance. The effectiveness of our approach is demonstrated acro…
We study the challenging incremental few-shot object detection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of continuous and finetune-free iFSD with limited success. We take a closer look at important design choices of such methods, leading to several key improvements and resulting in a more accurate and flexible framework, which we call Sylph. In particular, we demonstrate the effectiveness of decoupling object classification from localization by leveraging a base detector that is pretrained for class-agnostic localization on a large-scale dataset.…
A Unified Object Counting Network with Object Occupation Prior
Nanjing University of Information Science and Technology（南京信息工程大学）；Nanjing Forestry University（南京林业大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；the University of Adelaide（阿德莱德大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Nanjing University of Science and Technology（南京理工大学）；School of Automation Science and Electrical Engineering（自动化科学与电气工程学院）；Beihang University（北航）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；Shanghai Jiao Tong University（上海交通大学）
Tianjin University（天津大学）；Haihe Laboratory of Information Technology Application Innovation（海河实验室信息技术应用创新实验室）
Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally. Incremental learning has been proposed to retain the knowledge of old classes while learning to identify new classes. A typical approach is to use a few exemplars to avoid forgetting old knowledge. In such a scenario, data imbalance between old and new classes is a key issue that leads to performance degradation of the model. Several strategies have been designed to rectify the bias towards the new classes due to data imbalance. However, they heavily rely on the assumptions of the bias relation betw…
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
Institute of Science Tokyo（东京科学研究院）；University of California Berkeley（加州大学伯克利分校）；Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）；Greater Bay Area Institute for Innovation, Hunan University（湖南大学粤港澳大湾区创新研究院）
Huazhong University of Science and Technology（华中科技大学）
Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential. However, the well-performed models usually require large training data and can only detect defects that appeared in the training stage. When facing incremental few-shot data, defect detection models inevitably suffer from catastrophic forgetting and misclassification problem. To solve these problems, this paper proposes a new knowledge distillation network, called Dual Knowledge Align Network (DKAN). The proposed DKA…
Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness
School of Computer Science and Engineering（计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications（新一代人工智能技术及其交叉应用关键实验室）；Nanjing University of Science and Technology（南京理工大学）
Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）；MBZ University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning…
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
3D point cloud segmentation is an important function that helps robots understand the layout of their surrounding environment and perform tasks such as grasping objects, avoiding obstacles, and finding landmarks. Current segmentation methods are mostly class-specific, many of which are tuned to work with specific object categories and may not be generalizable to different types of scenes. This research proposes a learnable region growing method for class-agnostic point cloud segmentation, specifically for the task of instance label prediction. The proposed method is able to segment any class o…
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；S-Lab, College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院S-Lab）
SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation
University of Surrey, UK（英国萨里大学）；University of Wollongong, Australia（澳大利亚沃拉彭大学）；University of Copenhagen, Denmark（丹麦哥本哈根大学）；University of Exeter, UK（英国埃克塞特大学）；Singapore University of Technology and Design, Singapore（新加坡科技与设计大学）
