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Found 9 papers, 2 papers with code
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
In particular, we build a tree-like Split-Ensemble architecture by performing iterative splitting and pruning from a shared backbone model, where each branch serves as a submodel corresponding to a subtask.
Decouple Content and Motion for Conditional Image-to-Video Generation
The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i. e., one image and text. The previous cI2V generation methods conventionally perform in RGB pixel space, with limitations in modeling motion consistency and visual continuity.
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis.
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
We then use a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and through this process, we create a multi-modal and multi-task training dataset comprising input and output images along with annotated instructions.
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
Since the diffusion model learns the universal structural distribution of biological tissues, which is independent of the axial resolution, DiffuseIR can reconstruct authentic images with unseen low-axial resolutions into a high-axial resolution without requiring re-training.
Chain of Thought Prompt Tuning in Vision Language Models
Chain of Thought is a simple and effective approximation to human reasoning process and has been proven useful for natural language processing (NLP) tasks.
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
The visual prompts have provided an efficient manner in addressing visual cross-domain problems.
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions.
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world
To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model.
