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Found 14 papers, 5 papers with code
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
Using raw images as the sole training data, our method achieves unprecedented performance in self-supervised open-world segmentation, marking a significant milestone towards high-quality open-world entity segmentation in the absence of human-annotated masks.
InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction
However, extending such success to 3D dynamic human-object interaction (HOI) generation faces notable challenges, primarily due to the lack of large-scale interaction data and comprehensive descriptions that align with these interactions.
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
The human visual perception system demonstrates exceptional capabilities in learning without explicit supervision and understanding the part-to-whole composition of objects.
Aligning Large Multimodal Models with Factually Augmented RLHF
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context.
InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
This paper addresses a novel task of anticipating 3D human-object interactions (HOIs).
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
To distill knowledge from a highly accurate but complex teacher model, we construct a sequence of teachers to help the student gradually adapt.
Stochastic Multi-Person 3D Motion Forecasting
This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity of motion and social interactions, and the complexity of articulated motion.
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird's-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain.
Contrastive Mean Teacher for Domain Adaptive Object Detectors
Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain).
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors
Predicting diverse human motions given a sequence of historical poses has received increasing attention.
Ranked #1 on
Human Pose Forecasting
on Human3.6M
(MMADE metric)
BEV-Guided Multi-Modality Fusion for Driving Perception
We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features.
Pose Guided Attention for Multi-label Fashion Image Classification
We propose a compact framework with guided attention for multi-label classification in the fashion domain.
Adversarial Geometry-Aware Human Motion Prediction
We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework.
Few-Shot Human Motion Prediction via Meta-Learning
This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning.
