This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in
Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD). Our
solution consists of a strong LiDAR-based Bird's Eye View (BEV) encoder with
temporal fusion and a two-stage decoder, which combines a DETR head and a UNet
decoder. The solution was tested on the Argoverse 2 sensor dataset to evaluate
the occupancy state 3 seconds in the future. Our solution achieved 18% lower L1
Error (3.57) than the baseline and got the 1 place on the 4D Occupancy
Forecasting task in Argoverse Challenges at CVPR 2023.

我们的 Le3DE2E_Occ 解决方案在 CVPR 2023 自动驾驶（WAD）研讨会的 Argoverse 挑战中呈现了 4D 占用预测。我们的解决方案包括一个强大的基于 LiDAR 的鸟瞰图编码器，具有时间融合和两阶段解码器，结合了 DETR 头部和 UNet 解码器。该解决方案在 Argoverse 2 传感器数据集上进行了测试，以评估未来 3 秒的占用状态。我们的解决方案的 L1 误差（3.57）比基线低 18％，并在 CVPR 2023 的 4D 占用预测任务中获得第一名。

Argoverse 四维占用预测挑战技术报告

Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

In this report, we present the 4th place solution for CVPR 2023 3D occupancy
prediction challenge. We propose a simple method called Multi-Scale Occ for
occupancy prediction based on lift-splat-shoot framework, which introduces
multi-scale image features for generating better multi-scale 3D voxel features
with temporal fusion of multiple past frames. Post-processing including model
ensemble, test-time augmentation, and class-wise thresh are adopted to further
boost the final performance. As shown on the leaderboard, our proposed
occupancy prediction method ranks the 4th place with 49.36 mIoU.

本报告介绍了通过采用 Multi-Scale Occ 方法进行 3D 占用预测的方式，在 CVPR 2023 挑战赛中获得了第四名，并通过模型混合、测试时增强和类别敏感的阈值进一步提高了最终性能。

CVPR 2023 3D 占据预测挑战赛 4th Place 多尺度 Occ 解决方案

Multi-Scale Occ: 4th Place Solution for CVPR 2023 3D Occupancy  Prediction Challenge

With the development of multimedia technology, Video Copy Detection has been
a crucial problem for social media platforms. Meta AI hold Video Similarity
Challenge on CVPR 2023 to push the technology forward. In this paper, we share
our winner solutions on both tracks to help progress in this area. For
Descriptor Track, we propose a dual-level detection method with Video Editing
Detection (VED) and Frame Scenes Detection (FSD) to tackle the core challenges
on Video Copy Detection. Experimental results demonstrate the effectiveness and
efficiency of our proposed method. Code is available at
this https URL

本文介绍 Meta AI 在 CVPR 2023 Video Similarity Challenge 中的优胜方案，提出了一种利用视频剪辑检测和帧场景检测结合的双层检测方法来解决视频复制检测的核心挑战，实验结果表明了该方法的有效性和高效性。

视频抄袭检测的双层检测方法

A Dual-level Detection Method for Video Copy Detection

This paper introduces our method for the Emotional Reaction Intensity (ERI)
Estimation Challenge, in CVPR 2023: 5th Workshop and Competition on Affective
Behavior Analysis in-the-wild (ABAW). Based on the multimodal data provided by
the originazers, we extract acoustic and visual features with different
pretrained models. The multimodal features are mixed together by Transformer
Encoders with cross-modal attention mechnism. In this paper, 1. better features
are extracted with the SOTA pretrained models. 2. Compared with the baseline,
we improve the Pearson's Correlations Coefficient a lot. 3. We process the data
with some special skills to enhance performance ability of our model.

本文介绍了我们在 CVPR 2023：Affective Behavior Analysis in-the-wild (ABAW) 第 5 次研讨会和比赛中进行情感反应强度 (ERI) 估计挑战的方法，通过基于多模态数据提取声学和视觉特征，提高了我们的模型表现能力，并使用 Transformer 编码器进行交叉模态关注机制，从而提高了与基线的 Pearson 相关系数。