Do not distribute. 1 Introduction

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames.

In the method section of our paper, we describe the general encoding-decoding paradigm. We provide a brief overview of our data preprocessing pipeline, which involves the following steps. We employ the method of Boussard et al. (2021) to estimate the location of Decentralized registration (Windolf et al., 2022) is applied to track and correct Figure 6: Motion drift in "good" and "bad" sorting recordings. "bad" sorting example, which is still affected by drift even after registration. To decode binary behaviors, such as the mouse's left or right choices, we utilize In this section, we provide visualizations to gain insights into the effectiveness of our proposed decoder.

A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.

Neuron interpretation offers valuable insights into how knowledge is structured within a deep neural network model.

The code is available in https://github.com/cathyhxh/DIFFER.

The code is available at Contrastive-Sampling .

Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning.

We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.

We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.