Search Results for author:
Found 20 papers, 10 papers with code
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
In order to better represent the common and unique characters of multiple weather degradations, we introduce a weather-aware dynamic specific-commonality collaboration method, which identifies weather degradations and adaptively selects optimal candidate units associated with specific weather types.
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning.
Robust Learning under Hybrid Noise
To address the challenge, we propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data.
Enhance Reasoning for Large Language Models in the Game Werewolf
This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents.
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data and fine-tuning in small labeled samples.
Make Pixels Dance: High-Dynamic Video Generation
Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence.
Ranked #2 on
Text-to-Video Generation
on UCF-101
Patch Is Not All You Need
Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks.
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field.
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention
Such a problem is computationally hard to address by exact mathematical programming methods.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
In-Order Chart-Based Constituent Parsing
We propose a novel in-order chart-based model for constituent parsing.
LightSeq: A High Performance Inference Library for Transformers
Transformer, BERT and their variants have achieved great success in natural language processing.
SRQA: Synthetic Reader for Factoid Question Answering
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
A Span-based Linearization for Constituent Trees
We propose a novel linearization of a constituent tree, together with a new locally normalized model.
RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection
The core idea of the RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN.
TightCap: 3D Human Shape Capture with Clothing Tightness Field
In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
Meanwhile, we construct A3Net for the WebQA dataset.
Adversarial Metric Learning
In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.
