Search Results for author:
Found 14 papers, 7 papers with code
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature.
Decode Neural signal as Speech
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper.
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision
The proposed BELT method is a generic and efficient framework that bootstraps EEG representation learning using off-the-shelf large-scale pretrained language models (LMs).
CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Contrastive Language-Image Pre-training (CLIP) is a powerful multimodal large vision model that has demonstrated significant benefits for downstream tasks, including many zero-shot learning and text-guided vision tasks.
Ranked #2 on
Open Vocabulary Semantic Segmentation
on COCO-Stuff-171
(mIoU metric)
Interactive Segmentation
Open Vocabulary Semantic Segmentation
+4
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation
We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process.
Generalizing Multimodal Variational Methods to Sets
Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena.
Cross Task Neural Architecture Search for EEG Signal Classifications
Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition.
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
For this problem, we propose the Explainable Contrastive Language-Image Pre-training (ECLIP), which corrects the explainability via the Masked Max Pooling.
Continual Learning With Lifelong Vision Transformer
Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting.
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
So we try to improve WSSS in the aspect of noise mitigation.
Ranked #23 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Progressive Open-Domain Response Generation with Multiple Controllable Attributes
More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage.
Semantic Inference Network for Few-shot Streaming Label Learning
In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels.
Learning Internal Dense But External Sparse Structures of Deep Neural Network
In this paper, we bridge these two by proposing a new network structure with locally dense yet externally sparse connections.
