Search Results for author:
Found 32 papers, 25 papers with code
Improving Token-Based World Models with Parallel Observation Prediction
We incorporate POP in a novel TBWM agent named REM (Retentive Environment Model), showcasing a 15. 4x faster imagination compared to prior TBWMs.
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error.
Ranked #3 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Harnessing Diffusion Models for Visual Perception with Meta Prompts
Our key insight is to introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception.
Ranked #2 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images.
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL.
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human.
Decoupled Prioritized Resampling for Offline RL
OPER is a plug-and-play component for offline RL algorithms.
Improving and Benchmarking Offline Reinforcement Learning Algorithms
Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the emergence of various algorithms and datasets.
Efficient Diffusion Policies for Offline Reinforcement Learning
2) It is incompatible with maximum likelihood-based RL algorithms (e. g., policy gradient methods) as the likelihood of diffusion models is intractable.
MADiff: Offline Multi-agent Learning with Diffusion Models
To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller.
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention.
Boosting Offline Reinforcement Learning via Data Rebalancing
Therefore, we propose a simple yet effective method to boost offline RL algorithms based on the observation that resampling a dataset keeps the distribution support unchanged.
Mutual Information Regularized Offline Reinforcement Learning
In this work, we propose a novel MISA framework to approach offline RL from the perspective of Mutual Information between States and Actions in the dataset by directly constraining the policy improvement direction.
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.
Deep Long-Tailed Learning: A Survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #172 on
Image Classification
on ImageNet
DeepViT: Towards Deeper Vision Transformer
In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper.
Ranked #423 on
Image Classification
on ImageNet
Exploring Balanced Feature Spaces for Representation Learning
Motivated by this question, we conduct a series of studies on the performance of self-supervised contrastive learning and supervised learning methods over multiple datasets where training instance distributions vary from a balanced one to a long-tailed one.
Ranked #40 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
Learning Safe Policies with Cost-sensitive Advantage Estimation
Reinforcement Learning (RL) with safety guarantee is critical for agents performing tasks in risky environments.
Improving Generalization in Reinforcement Learning with Mixture Regularization
Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments.
Few-shot Classification via Adaptive Attention
To be specific, we devise a simple and efficient meta-reweighting strategy to adapt the sample representations and generate soft attention to refine the representation such that the relevant features from the query and support samples can be extracted for a better few-shot classification.
The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation
Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate classification of object proposals.
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution.
Classification Calibration for Long-tail Instance Segmentation
In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.
Decoupling Representation and Classifier for Long-Tailed Recognition
The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem.
Ranked #3 on
Long-tail learning with class descriptors
on CUB-LT
Exploring Simple and Transferable Recognition-Aware Image Processing
Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks and training datasets.
Regularization Matters in Policy Optimization
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
Similarity R-C3D for Few-shot Temporal Activity Detection
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video.
Few-shot Object Detection via Feature Reweighting
The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.
Ranked #21 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Policy Optimization with Demonstrations
Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse.
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
As most deep learning algorithms are stochastic (e. g., Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness arguments of Xu & Mannor, and introduce a new approach, ensemble robustness, that concerns the robustness of a population of hypotheses.
