Search Results for author:
Found 8 papers, 2 papers with code
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments
Zero-Shot Object Navigation (ZSON) requires agents to autonomously locate and approach unseen objects in unfamiliar environments and has emerged as a particularly challenging task within the domain of Embodied AI.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation
In addition, the lack of large-scale available datasets with emotional transition speech and corresponding 3D human gestures also limits the addressing of this task.
M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation
Moreover, to further enhance the effectiveness of $M^{3}Adapter$ while preserving the coherence of semantic context comprehension, we introduce a two-stage $M^{3}FT$ fine-tuning strategy.
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
In this paper, we propose a Multi-space Alignment Teacher-Student (MATS) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Geometric-space Aligned Student (GAS) model.
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.
