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Found 11 papers, 6 papers with code
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e. g., CLIP-ViT-B/16.
Ranked #2 on
Prompt Engineering
on ImageNet
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i. e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph.
Evolving Knowledge Mining for Class Incremental Segmentation
In this paper, we for the first time investigate the efficient multi-grained knowledge reuse for CISS, and propose a novel method, Evolving kNowleDge minING (ENDING), employing a frozen backbone.
Class-Incremental Semantic Segmentation
Knowledge Distillation
A Dive into SAM Prior in Image Restoration
This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images.
Can SAM Boost Video Super-Resolution?
To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information.
Task Residual for Tuning Vision-Language Models
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts.
Prediction Calibration for Generalized Few-shot Semantic Segmentation
To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced.
Generalized Few-Shot Semantic Segmentation
Semantic Segmentation
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels).
Stochastic Classifiers for Unsupervised Domain Adaptation
A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) methods is to employ two classifiers to identify the misaligned local regions between source and target domain.
Geometry Guided Adversarial Facial Expression Synthesis
An expression invariant face recognition experiment is also performed to further show the advantages of our proposed method.
Recent Progress of Face Image Synthesis
Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning.
