ProjLens shows that backdoor parameters in MLLMs are encoded in low-rank subspaces of the projector and that embeddings shift toward the target direction with magnitude linear in input norm, activating only on poisoned samples.
Flamingo: a visual language model for few-shot learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Video Prediction Policy conditions robot action learning on future-frame predictions inside fine-tuned video diffusion models, yielding 18.6% relative gains on Calvin ABC-D and 31.6% higher real-world success rates.
Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.
citing papers explorer
-
ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety
ProjLens shows that backdoor parameters in MLLMs are encoded in low-rank subspaces of the projector and that embeddings shift toward the target direction with magnitude linear in input norm, activating only on poisoned samples.
-
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Video Prediction Policy conditions robot action learning on future-frame predictions inside fine-tuned video diffusion models, yielding 18.6% relative gains on Calvin ABC-D and 31.6% higher real-world success rates.
-
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models
Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.