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Found 26 papers, 8 papers with code
InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
3D Gaussians have recently emerged as an efficient representation for novel view synthesis.
Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks.
Intention-driven Ego-to-Exo Video Generation
Finally, the inferred human movement and high-level action descriptions jointly guide the generation of exocentric motion and interaction content (i. e., corresponding optical flow and occlusion maps) in the backward process of the diffusion model, ultimately warping them into the corresponding exocentric video.
CCM: Adding Conditional Controls to Text-to-Image Consistency Models
Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality.
Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously.
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets.
Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models
To bring the useful knowledge back into light, we first identify a set of parameters that are important to a given downstream task, then attach a binary mask to each parameter, and finally optimize these masks on the downstream data with the parameters frozen.
Eliminating Lipschitz Singularities in Diffusion Models
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models.
Cones 2: Customizable Image Synthesis with Multiple Subjects
Synthesizing images with user-specified subjects has received growing attention due to its practical applications.
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection
Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set.
Cones: Concept Neurons in Diffusion Models for Customized Generation
Concatenating multiple clusters of concept neurons can vividly generate all related concepts in a single image.
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning aims to recognize both the old and new classes without access to old class samples.
Neural Dependencies Emerging from Learning Massive Categories
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization
However, its generalization performance to heterogeneous tasks is inferior to other architectures (e. g., CNNs and transformers) due to the extensive retention of domain information.
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.
Self-Paced Imbalance Rectification for Class Incremental Learning
Exemplar-based class-incremental learning is to recognize new classes while not forgetting old ones, whose samples can only be saved in limited memory.
Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior
We introduce a Noise Disentanglement Module (NDM) to disentangle the noise and content in the reflectance maps with the reliable aid of unpaired clean images.
Ranked #1 on
Low-Light Image Enhancement
on MEF
Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction
Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California.
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes.
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.
Self-Supervised Tuning for Few-Shot Segmentation
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples.
One-Shot Texture Retrieval with Global Context Metric
In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image.
FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs
In recent years, there is a surge on machine learning applications in industry.
Distributed, Parallel, and Cluster Computing Mathematical Software
Recognize Complex Events From Static Images by Fusing Deep Channels
A considerable portion of web images capture events that occur in our personal lives or social activities.
Collaborative Filtering with Information-Rich and Information-Sparse Entities
In particular, we consider both the clustering model, where only users (or items) are clustered, and the co-clustering model, where both users and items are clustered, and further, we assume that some users rate many items (information-rich users) and some users rate only a few items (information-sparse users).
Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs
In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure.
