Search Results for author:
Found 31 papers, 20 papers with code
Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS
In SfM-free methods, inaccurate initial poses lead to misalignment issue, which, under the constraints of per-pixel image loss functions, results in excessive gradients, causing unstable optimization and poor convergence for NVS.
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling.
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
For this purpose, we establish a new benchmark comprising text prompts that fully reflect multiple dynamics grades, and define a set of dynamics scores corresponding to various temporal granularities to comprehensively evaluate the dynamics of each generated video.
Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning
Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency.
DynRefer: Delving into Region-level Multi-modality Tasks via Dynamic Resolution
Unfortunately, most of existing methods using fixed visual inputs remain lacking the resolution adaptability to find out precise language descriptions.
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections.
Ranked #2 on
3D Object Detection
on nuScenes Camera Only
ControlCap: Controllable Region-level Captioning
The multimodal model is constrained to generate captions within a few sub-spaces containing the control words, which increases the opportunity of hitting less frequent captions, alleviating the caption degeneration issue.
Ranked #1 on
Dense Captioning
on Visual Genome
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
Robots play a critical role as the physical agent of human operators in exploring the ocean.
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor neurons.
Generative Prompt Model for Weakly Supervised Object Localization
During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric, using extra
training data)
AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation
Pointly supervised instance segmentation (PSIS) learns to segment objects using a single point within the object extent as supervision.
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.
Ranked #5 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Exploiting Knowledge Distillation for Few-Shot Image Generation
The existing pipeline is first pretraining a source model (which contains a generator and a discriminator) on a large-scale dataset and finetuning it on a target domain with limited samples.
Strengthen Learning Tolerance for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision.
Multiple instance active learning for object detection
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.
Ranked #1 on
Active Object Detection
on MS COCO
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization.
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping.
Robotics
Domain Contrast for Domain Adaptive Object Detection
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation
We present benchmarking results of the DeepClaw system for a baseline Tic-Tac-Toe task, a bin-clearing task, and a jigsaw puzzle task using three sets of standard robotic hardware.
Robotics
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label.
Ranked #9 on
Weakly Supervised Action Localization
on THUMOS’14
A Lobster-inspired Robotic Glove for Hand Rehabilitation
This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation.
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance
Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.
Reconfigurable Design for Omni-adaptive Grasp Learning
Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping.
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger
Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment.
Rigid-Soft Interactive Learning for Robust Grasping
We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.
FreeAnchor: Learning to Match Anchors for Visual Object Detection
In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner.
Ranked #136 on
Object Detection
on COCO test-dev
Utilizing the Instability in Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.
Ranked #15 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Min-Entropy Latent Model for Weakly Supervised Object Detection
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.
SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes.
