13 papers
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
Disheng Liu, Tuo Liang, Chaoda Song +1
Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potentia…
DefenseSplat: Enhancing the Robustness of 3D Gaussian Splatting via Frequency-Aware Filtering
Yiran Qiao, Yiren Lu, Yunlai Zhou +4
3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time and high-fidelity 3D reconstruction from posed images. However, recent studies reveal its vulnerabilit…
Certified Causal Defense with Generalizable Robustness
Yiran Qiao, Yu Yin, Chen Chen +1
While machine learning models have proven effective across various scenarios, it is widely acknowledged that many models are vulnerable to adversarial attacks. Recently, there have…
Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion
Yiran Qiao, Yiren Lu, Yunlai Zhou +5
3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a single or multiple images. Bu…
When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?
Tuo Liang, Zhe Hu, Jing Li +8
Understanding humor-particularly when it involves complex, contradictory narratives that require comparative reasoning-remains a significant challenge for large vision-language mod…
AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Models
Yiran Qiao, Yiren Lu, Yunlai Zhou +4
3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits s…