Search Results for author:
Found 39 papers, 19 papers with code
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training.
Ranked #2 on
Zero-shot Generalization
on CALVIN
(using extra training data)
Vision-Language Foundation Models as Effective Robot Imitators
We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions
Ambiguity is ubiquitous in human communication.
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation
In this paper, we present a novel method for mobile manipulators to perform multiple contact-rich manipulation tasks.
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods
However, the recipes of visual pre-training for robot manipulation tasks are yet to be built.
ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations
Instead of directly using the model inference way, i. e., mean-shift clustering, to generate the pseudo labels, we propose to use k-means with fixed initial seeds: the annotated points.
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.
Learning to Explore Informative Trajectories and Samples for Embodied Perception
Unlike static perception methods trained on pre-collected images, the embodied agent can move around in the environment and obtain images of objects from any viewpoints.
Towards Unifying Reference Expression Generation and Comprehension
Additionally, IRTF could generate pseudo input regions for the REC task to enable a uniform way for sharing the identical representation space across the REC and REG.
Generative Category-Level Shape and Pose Estimation with Semantic Primitives
Then, by using a SIM(3)-invariant shape descriptor, we gracefully decouple the shape and pose of an object, thus supporting latent shape optimization of target objects in arbitrary poses.
Ranked #2 on
6D Pose Estimation using RGBD
on REAL275
Exploring Target Representations for Masked Autoencoders
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning.
3D Part Assembly Generation with Instance Encoded Transformer
It is desirable to enable robots capable of automatic assembly.
Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets
Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint?
Navigating to Objects in Unseen Environments by Distance Prediction
With the estimated distance map, the agent could simultaneously explore the environment and navigate to the target objects based on a simple human-designed strategy.
iBOT: Image BERT Pre-Training with Online Tokenizer
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
Ranked #1 on
Unsupervised Image Classification
on ImageNet
Self-Supervised Learning by Estimating Twin Class Distributions
To solve this problem, we propose to maximize the mutual information between the input and the class predictions.
Ranked #1 on
Image Classification
on Oxford-IIIT Pet Dataset
Fine-Grained Image Classification
Representation Learning
+5
Image BERT Pre-training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Separating 3D point clouds into individual instances is an important task for 3D vision.
Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation
In a unified framework, we jointly predict the feasible 6-DoF grasp poses, instance semantic segmentation, and collision information.
Learning to Design and Construct Bridge without Blueprint
In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design.
SOLO: A Simple Framework for Instance Segmentation
Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation.
Adversarial Option-Aware Hierarchical Imitation Learning
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
Scale-aware Automatic Augmentation for Object Detection
We propose Scale-aware AutoAug to learn data augmentation policies for object detection.
Locate then Segment: A Strong Pipeline for Referring Image Segmentation
Referring image segmentation aims to segment the objects referred by a natural language expression.
Generalized Referring Expression Segmentation
Image Segmentation
+2
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin.
SOLOv2: Dynamic and Fast Instance Segmentation
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
Ranked #10 on
Real-time Instance Segmentation
on MSCOCO
FoveaBox: Beyound Anchor-based Object Detection
While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors.
SOLO: Segmenting Objects by Locations
We present a new, embarrassingly simple approach to instance segmentation in images.
Ranked #67 on
Instance Segmentation
on COCO test-dev
Task-Aware Monocular Depth Estimation for 3D Object Detection
In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.
Deep Point-wise Prediction for Action Temporal Proposal
Detecting actions in videos is an important yet challenging task.
Attention-based Transfer Learning for Brain-computer Interface
Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field.
FoveaBox: Beyond Anchor-based Object Detector
In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis.
Ranked #89 on
Object Detection
on COCO test-dev
(APM metric)
Consistent Optimization for Single-Shot Object Detection
We present consistent optimization for single stage object detection.
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination
We propose a light-weight video frame interpolation algorithm.
Deep Feature Pyramid Reconfiguration for Object Detection
In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process.
A Survey on Deep Transfer Learning
As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains.
RON: Reverse Connection with Objectness Prior Networks for Object Detection
To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs.
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances.
