Asia
Supplementary Material IEBins: Iterative Elastic Bins for Monocular Depth Estimation
Table 2 shows a similar performance trend as in NYU-Depth-v2 dataset with increasing number of bins. We report results on keyframes (selected by the ORB-SLAM2) and on all frames of sequences 01-10. The A TE (m) metric is used.
Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents Zihao Wang
These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models. With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc .
Mode Connectivity in Auction Design Christoph Hertrich
Optimal auction design is a fundamental problem in algorithmic game theory.
85db52cc08c5e00cfb1d216b1c85ba35-Paper-Conference.pdf
Concretely, we prove that polynomial-sized message-passing GNN's can learn the
Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models Ziyi Yin 1 Muchao Y e
Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.
