28 research outputs found
A uniform human multimodal dataset for emotion perception and judgment
Face perception is a fundamental aspect of human social interaction, yet most research on this topic has focused on single modalities and specific aspects of face perception. Here, we present a comprehensive multimodal dataset for examining facial emotion perception and judgment. This dataset includes EEG data from 97 unique neurotypical participants across 8 experiments, fMRI data from 19 neurotypical participants, single-neuron data from 16 neurosurgical patients (22 sessions), eye tracking data from 24 neurotypical participants, behavioral and eye tracking data from 18 participants with ASD and 15 matched controls, and behavioral data from 3 rare patients with focal bilateral amygdala lesions. Notably, participants from all modalities performed the same task. Overall, this multimodal dataset provides a comprehensive exploration of facial emotion perception, emphasizing the importance of integrating multiple modalities to gain a holistic understanding of this complex cognitive process. This dataset serves as a key missing link between human neuroimaging and neurophysiology literature, and facilitates the study of neuropsychiatric populations
Differences in the link between social trait judgment and socio-emotional experience in neurotypical and autistic individuals
Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others\u27 faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants\u27 emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants\u27 self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others\u27 faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD
Rethinking Range View Representation for LiDAR Segmentation
LiDAR segmentation is crucial for autonomous driving perception. Recent
trends favor point- or voxel-based methods as they often yield better
performance than the traditional range view representation. In this work, we
unveil several key factors in building powerful range view models. We observe
that the "many-to-one" mapping, semantic incoherence, and shape deformation are
possible impediments against effective learning from range view projections. We
present RangeFormer -- a full-cycle framework comprising novel designs across
network architecture, data augmentation, and post-processing -- that better
handles the learning and processing of LiDAR point clouds from the range view.
We further introduce a Scalable Training from Range view (STR) strategy that
trains on arbitrary low-resolution 2D range images, while still maintaining
satisfactory 3D segmentation accuracy. We show that, for the first time, a
range view method is able to surpass the point, voxel, and multi-view fusion
counterparts in the competing LiDAR semantic and panoptic segmentation
benchmarks, i.e., SemanticKITTI, nuScenes, and ScribbleKITTI.Comment: ICCV 2023; 24 pages, 10 figures, 14 tables; Webpage at
https://ldkong.com/RangeForme
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Contrastive Language-Image Pre-training (CLIP) achieves promising results in
2D zero-shot and few-shot learning. Despite the impressive performance in 2D,
applying CLIP to help the learning in 3D scene understanding has yet to be
explored. In this paper, we make the first attempt to investigate how CLIP
knowledge benefits 3D scene understanding. We propose CLIP2Scene, a simple yet
effective framework that transfers CLIP knowledge from 2D image-text
pre-trained models to a 3D point cloud network. We show that the pre-trained 3D
network yields impressive performance on various downstream tasks, i.e.,
annotation-free and fine-tuning with labelled data for semantic segmentation.
Specifically, built upon CLIP, we design a Semantic-driven Cross-modal
Contrastive Learning framework that pre-trains a 3D network via semantic and
spatial-temporal consistency regularization. For the former, we first leverage
CLIP's text semantics to select the positive and negative point samples and
then employ the contrastive loss to train the 3D network. In terms of the
latter, we force the consistency between the temporally coherent point cloud
features and their corresponding image features. We conduct experiments on
SemanticKITTI, nuScenes, and ScanNet. For the first time, our pre-trained
network achieves annotation-free 3D semantic segmentation with 20.8% and 25.08%
mIoU on nuScenes and ScanNet, respectively. When fine-tuned with 1% or 100%
labelled data, our method significantly outperforms other self-supervised
methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we
demonstrate the generalizability for handling cross-domain datasets. Code is
publicly available https://github.com/runnanchen/CLIP2Scene.Comment: CVPR 202
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Point-, voxel-, and range-views are three representative forms of point
clouds. All of them have accurate 3D measurements but lack color and texture
information. RGB images are a natural complement to these point cloud views and
fully utilizing the comprehensive information of them benefits more robust
perceptions. In this paper, we present a unified multi-modal LiDAR segmentation
network, termed UniSeg, which leverages the information of RGB images and three
views of the point cloud, and accomplishes semantic segmentation and panoptic
segmentation simultaneously. Specifically, we first design the Learnable
cross-Modal Association (LMA) module to automatically fuse voxel-view and
range-view features with image features, which fully utilize the rich semantic
information of images and are robust to calibration errors. Then, the enhanced
voxel-view and range-view features are transformed to the point space,where
three views of point cloud features are further fused adaptively by the
Learnable cross-View Association module (LVA). Notably, UniSeg achieves
promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes,
and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks,
including the LiDAR semantic segmentation challenge of nuScenes and panoptic
segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg
codebase, which is the largest and most comprehensive outdoor LiDAR
segmentation codebase. It contains most of the popular outdoor LiDAR
segmentation algorithms and provides reproducible implementations. The
OpenPCSeg codebase will be made publicly available at
https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at
https://github.com/PJLab-ADG/PCSe
Distinct neurocognitive bases for social trait judgments of faces in autism spectrum disorder.
Autism spectrum disorder (ASD) is characterized by difficulties in social processes, interactions, and communication. Yet, the neurocognitive bases underlying these difficulties are unclear. Here, we triangulated the 'trans-diagnostic' approach to personality, social trait judgments of faces, and neurophysiology to investigate (1) the relative position of autistic traits in a comprehensive social-affective personality space, and (2) the distinct associations between the social-affective personality dimensions and social trait judgment from faces in individuals with ASD and neurotypical individuals. We collected personality and facial judgment data from a large sample of online participants (N = 89 self-identified ASD; N = 307 neurotypical controls). Factor analysis with 33 subscales of 10 social-affective personality questionnaires identified a 4-dimensional personality space. This analysis revealed that ASD and control participants did not differ significantly along the personality dimensions of empathy and prosociality, antisociality, or social agreeableness. However, the ASD participants exhibited a weaker association between prosocial personality dimensions and judgments of facial trustworthiness and warmth than the control participants. Neurophysiological data also indicated that ASD participants had a weaker association with neuronal representations for trustworthiness and warmth from faces. These results suggest that the atypical association between social-affective personality and social trait judgment from faces may contribute to the social and affective difficulties associated with ASD
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Differences in the link between social trait judgment and socio-emotional experience in neurotypical and autistic individuals.
Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD
Multimodal investigations of human face perception in neurotypical and autistic adults
Faces are among the most important visual stimuli that we perceive in everyday life. Although there is a plethora of literature studying many aspects of face perception, the vast majority of them focuses on a single aspect of face perception using unimodal approaches. In this review, we advocate for studying face perception using multimodal cognitive neuroscience approaches. We highlight two case studies: the first study investigates ambiguity in facial expressions of emotion, and the second study investigates social trait judgment. In the first set of studies, we revealed an event-related potential that signals emotion ambiguity and we found convergent response to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discussed recent findings about neural substrates underlying comprehensive social evaluation, and the relationship between personality factors and social trait judgements. Notably, in both sets of studies, we provided an in-depth discussion of altered face perception in people with autism spectrum disorder (ASD) and offered a computational account for the behavioral and neural markers of atypical facial processing in ASD. Finally, we suggest new perspectives for studying face perception. All data discussed in the case studies of this review are publicly available