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Found 7 papers, 1 papers with code
Practical Region-level Attack against Segment Anything Models
Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i. e., user prompt).
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.
Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective
These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.
Revisiting Open World Object Detection
Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced.
