4D Contrastive Superflows are Dense 3D Representation Learners

In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly and labor-intensive. To address this challenge from a data representation learning perspective, we introduce SuperFlow, a novel framework designed to harness consecutive LiDAR-camera pairs for establishing spatiotemporal pretraining objectives. SuperFlow stands out by integrating two key designs: 1) a dense-to-sparse consistency regularization, which promotes insensitivity to point cloud density variations during feature learning, and 2) a flow-based contrastive learning module, carefully crafted to extract meaningful temporal cues from readily available sensor calibrations. To further boost learning efficiency, we incorporate a plug-and-play view consistency module that enhances the alignment of the knowledge distilled from camera views. Extensive comparative and ablation studies across 11 heterogeneous LiDAR datasets validate our effectiveness and superiority. Additionally, we observe several interesting emerging properties by scaling up the 2D and 3D backbones during pretraining, shedding light on the future research of 3D foundation models for LiDAR-based perception.

Cooperative Sentiment Agents for Multimodal Sentiment Analysis

In this paper, we propose a new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA. Co-SA comprises two critical components: the Sentiment Agents Establishment (SAE) phase and the Sentiment Agents Cooperation (SAC) phase. During the SAE phase, each sentiment agent deals with an unimodal signal and highlights explicit dynamic sentiment variations within the modality via the Modality-Sentiment Disentanglement (MSD) and Deep Phase Space Reconstruction (DPSR) modules. Subsequently, in the SAC phase, Co-SA meticulously designs task-specific interaction mechanisms for sentiment agents so that coordinating multimodal signals to learn the joint representation. Specifically, Co-SA equips an independent policy model for each sentiment agent that captures significant properties within the modality. These policies are optimized mutually through the unified reward adaptive to downstream tasks. Benefitting from the rewarding mechanism, Co-SA transcends the limitation of pre-defined fusion modes and adaptively captures unimodal properties for MRL in the multimodal interaction setting. To demonstrate the effectiveness of Co-SA, we apply it to address Multimodal Sentiment Analysis (MSA) and Multimodal Emotion Recognition (MER) tasks. Our comprehensive experimental results demonstrate that Co-SA excels at discovering diverse cross-modal features, encompassing both common and complementary aspects. The code can be available at https://github.com/smwanghhh/Co-SA.

FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation

LiDAR segmentation is crucial for autonomous driving systems. The recent range-view approaches are promising for real-time processing. However, they suffer inevitably from corrupted contextual information and rely heavily on post-processing techniques for prediction refinement. In this work, we propose a simple yet powerful FRNet that restores the contextual information of the range image pixels with corresponding frustum LiDAR points. Firstly, a frustum feature encoder module is used to extract per-point features within the frustum region, which preserves scene consistency and is crucial for point-level predictions. Next, a frustum-point fusion module is introduced to update per-point features hierarchically, which enables each point to extract more surrounding information via the frustum features. Finally, a head fusion module is used to fuse features at different levels for final semantic prediction. Extensive experiments on four popular LiDAR segmentation benchmarks under various task setups demonstrate our superiority. FRNet achieves competitive performance while maintaining high efficiency. The code is publicly available.

Adaptively Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation

In practical application, 3D Human Pose Estimation (HPE) is facing with several variable elements, involving the number of views, the length of the video sequence, and whether using camera calibration. To this end, we propose a unified framework named Multi-view and Temporal Fusing Transformer (MTF-Transformer) to adaptively handle varying view numbers and video length without calibration. MTF-Transformer consists of Feature Extractor, Multi-view Fusing Transformer (MFT), and Temporal Fusing Transformer (TFT). Feature Extractor estimates the 2D pose from each image and encodes the predicted coordinates and confidence into feature embedding for further 3D pose inference. It discards the image features and focuses on lifting the 2D pose into the 3D pose, making the subsequent modules computationally lightweight enough to handle videos. MFT fuses the features of a varying number of views with a relative-attention block. It adaptively measures the implicit relationship between each pair of views and reconstructs the features. TFT aggregates the features of the whole sequence and predicts 3D pose via a transformer, which is adaptive to the length of the video and takes full advantage of the temporal information. With these modules, MTF-Transformer handles different application scenes, varying from a monocular-single-image to multi-view-video, and the camera calibration is avoidable. We demonstrate quantitative and qualitative results on the Human3.6M, TotalCapture, and KTH Multiview Football II. Compared with state-of-the-art methods with camera parameters, experiments show that MTF-Transformer not only obtains comparable results but also generalizes well to dynamic capture with an arbitrary number of unseen views. Code is available in https://github.com/lelexx/MTF-Transformer.