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Found 11 papers, 3 papers with code
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.
AttributionScanner: A Visual Analytics System for Metadata-Free Data-Slicing Based Model Validation
Data slice-finding is an emerging technique for evaluating machine learning models.
InterVLS: Interactive Model Understanding and Improvement with Vision-Language Surrogates
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement.
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection
We argue that this contextual information should already be embedded within the known classes.
CLIP-S$^4$: Language-Guided Self-Supervised Semantic Segmentation
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
Self-supervised Semantic Segmentation Grounded in Visual Concepts
In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i. e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images.
Novelty-based Generalization Evaluation for Traffic Light Detection
It is on this space we estimate the novelty of the test samples.
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.
Towards a Flexible Embedding Learning Framework
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
Dynamic Graph Representation Learning via Self-Attention Networks
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
