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- 1,836
- A Comprehensive Survey of Continual Learning: Theory, Method and Application1,672
This work presents a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications, and summarizes the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency.
- 1,075
- Cross-domain collaboration recommendation351
The Cross-domain Topic Learning (CTL) model is proposed, which consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue and outperforms baselines significantly on multiple recommendation metrics.
- 289
- Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving228
Several important findings are drawn, including: 1) motion-level corruptions are the most threatening ones that lead to significant performance drop of all models; 2) LiDAR-camerafusion models demonstrate better robustness; 3) camera-only models are extremely vulnerable to image corruptions, showing the indispensability of LiDar point clouds.
- Pruning from Scratch225
This work finds that pre-training an over-parameterized model is not necessary for obtaining the target pruned structure, and empirically shows that more diverse pruned structures can be directly pruned from randomly initialized weights, including potential models with better performance.
- 215
- Toward the third generation artificial intelligence209
This paper looks toward developing a third generation artificial intelligence by combining the current paradigms, and considers symbolism as the first generation of AI and connectionism as the second generation.
- A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking187
A comprehensive robustness benchmark called ARES-Bench is established on the image classification task and several important findings are drawn, including that there exists an intrinsic trade-off between the adversarial and natural robustness of specific noise types for the same model architecture.
- Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning149
This work performs a systematic investigation of backdoor attack on text-to-image diffusion models and proposes BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels.
- Improving Interpretability of Deep Neural Networks with Semantic Information134
This work proposes a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions by extracting a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts.
- Learning to Write Stylized Chinese Characters by Reading a Handful of Examples114
A novel framework of Style-Aware Variational Auto-Encoder (SA-VAE), which disentangles the content-relevant and style-relevant components of a Chinese character feature with a novel intercross pair-wise optimization method and has a powerful one-shot/few-shot generalization ability.
- 111
- Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition75
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems, and designs adversarial textured 3D meshes with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses.
- Detection Transformer with Stable Matching73
This paper proposes two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost, and shows consistent improvements over baselines.
- Multi-task learning for segmentation and classification of breast tumors from ultrasound images70
A novel multi-task learning network designed to optimize tumor segmentation and classification in BUS images, which not only outperforms mainstream multi-task learning methods for both breast tumor segmentation and classification tasks, but also achieves state-of-the-art results for BUS tumor segmentation.
- 69
- Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification67
An automatic ECG heartbeat classification method based on ensemble learning and multi-kernel learning is proposed, which achieves a superior classification performance compared to the state-of-the-art methods.
- Triple-Memory Networks: A Brain-Inspired Method for Continual Learning66
A novel approach named triple memory networks (TMNs) for continual learning that model the interplay of hippocampus, prefrontal cortex and sensory cortex as a triple-network architecture of generative adversarial networks (GAN) and achieves new state-of-the-art performance on a variety of class-incremental learning benchmarks.
- Exploring the Robustness of Decision-Level Through Adversarial Attacks on LLM-Based Embodied Models63
The Embodied Intelligent Robot Attack Dataset (EIRAD) is constructed, tailored specifically for robustness evaluation, and experimental results demonstrate that the method exhibits a superior attack success rate when targeting LLM-based embodied models, indicating a lower level of decision-level robustness in these models.
- Towards Transferable Targeted 3D Adversarial Attack in the Physical World48
A novel framework named TT3D is designed that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes and exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks.
- Unsupervised Part Segmentation through Disentangling Appearance and Shape42
A bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance, and combined with a self-supervised part classification loss and an improved geometry concentration constraint can segment more consistent parts with semantic meanings.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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