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Found 21 papers, 8 papers with code
Edit as You See: Image-guided Video Editing via Masked Motion Modeling
In this paper, we propose a novel Image-guided Video Editing Diffusion model, termed IVEDiff for the image-guided video editing.
ReNeg: Learning Negative Embedding with Reward Guidance
In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality.
DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces.
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images.
Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise
To provide a rigorous DP guarantee for SVT, prior works in the literature adopt a conservative privacy analysis by assuming the direct disclosure of noisy query results as in typical private query releases.
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
Third, the model is updated with the gradient reconstructed from recycled common knowledge and noisy incremental information.
SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers.
Online Electricity Purchase for Data Center with Dynamic Virtual Battery from Flexibility Aggregation
As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
We prove that pre-projection enhances the convergence of DP-SGD by reducing the dependence of clipping error and bias to a fraction of the top gradient eigenspace, and in theory, limits cross-client variance to improve the convergence under heterogeneous federation.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations
Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transformations that are harmful to semantic representations.
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking
However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.
Extremal Analysis of Flooding Risk and Management
Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency.
Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning
Uncrewed autonomous vehicles (UAVs) have made significant contributions to reconnaissance and surveillance missions in past US military campaigns.
Delayed Rewards Calibration via Reward Empirical Sufficiency
Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.
Towards Real-Time Multi-Object Tracking
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #4 on
Multi-Object Tracking
on HiEve
Adversarial View-Consistent Learning for Monocular Depth Estimation
To this end, we first design a differentiable depth map warping operation, which is end-to-end trainable, and then propose a pose generator to generate novel views for a given image in an adversarial manner.
