Search Results for author:
Found 9 papers, 5 papers with code
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks.
Ranked #21 on
Fine-Grained Image Classification
on CUB-200-2011
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Ranked #23 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Scene-Intuitive Agent for Remote Embodied Visual Grounding
Intuitively, we comprehend the semantics of the instruction to form an overview of where a bathroom is and what a blue towel is in mind; then, we navigate to the target location by consistently matching the bathroom appearance in mind with the current scene.
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation
This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 10% labeled data
(detector metric)
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting
We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
