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Found 10 papers, 4 papers with code
Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer
In this work, we propose a novel solution to the text-driven style transfer task, namely, Adaptive Style Incorporation~(ASI), to achieve fine-grained feature-level style incorporation.
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators.
SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation
We introduced SSR, which utilizes SAM (segment-anything) as a strong regularizer during training, to greatly enhance the robustness of the image encoder for handling various domains.
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
By pulling the learned features to these semantic anchors, several advantages can be attained: 1) the intra-class compactness and naturally inter-class separability, 2) induced bias or errors from feature learning can be avoided, and 3) robustness to the long-tailed problem.
High-level Feature Guided Decoding for Semantic Segmentation
One crucial design of the HFG is to protect the high-level features from being contaminated by using proper stop-gradient operations so that the backbone does not update according to the noisy gradient from the upsampler.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2. 23% mIOU with superior generalization ability.
CAR: Class-aware Regularizations for Semantic Segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules.
Ranked #8 on
Semantic Segmentation
on PASCAL Context
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation.
Ranked #6 on
Semantic Segmentation
on COCO-Stuff test
See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation
Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that the proposed sharing strategy can not only boost a network s generalization and representation abilities but also reduce the model complexity significantly.
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role.
