Search Results for author:
Found 14 papers, 9 papers with code
MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation
To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively.
Ranked #1 on
Video Panoptic Segmentation
on VIPSeg
Learning Part Segmentation from Synthetic Animals
We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution.
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on Cityscapes
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+1
Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation
Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0. 9% and 1. 3% on PartImageNet, 0. 4% and 1. 7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4. 4% and 7. 1% on part and object respectively.
PartImageNet: A Large, High-Quality Dataset of Parts
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
Learning from Temporal Gradient for Semi-supervised Action Recognition
Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i. e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i. e., different ratios of labeled data).
TransMix: Attend to Mix for Vision Transformers
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning
In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only.
TransFG: A Transformer Architecture for Fine-grained Recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
CORL: Compositional Representation Learning for Few-Shot Classification
In particular, during meta-learning, we train a knowledge base that consists of a dictionary of component representations and a dictionary of component activation maps that encode common spatial activation patterns of components.
Semi-synthesis: A fast way to produce effective datasets for stereo matching
Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data.
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion.
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks.
