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Found 9 papers, 3 papers with code
Colorization of Depth Map via Disentanglement
Vision perception is one of the most important components for a computer or robot to understand the surrounding scene and achieve autonomous applications.
MENTOR: Multilingual tExt detectioN TOward leaRning by analogy
Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task.
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly.
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism.
ExReg: Wide-range Photo Exposure Correction via a Multi-dimensional Regressor with Attention
Third, photos with the same content but different exposures may not reach consistent adjustment results.
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation
By training the model with both labeled and unlabeled images, the accuracy of unlabeled images can be improved and the labeling cost can be reduced.
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i. e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner.
Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling
Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling.
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer
In this paper, a better learning network has been proposed by considering three tasks - domain adaptation, disentangled representation, and style transfer simultaneously.
