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Found 9 papers, 3 papers with code
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
To achieve this, we employ a sparse mixture-of-experts within each transformer block to utilize semantic information and facilitate handling conflicts in tasks through parameter isolation.
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
This work introduces HarmonyView, a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity.
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection
During training, DiffRef3D gradually adds noise to the residuals between proposals and target objects, then applies the noisy residuals to proposals to generate hypotheses.
Point-DynRF: Point-based Dynamic Radiance Fields from a Monocular Video
Dynamic radiance fields have emerged as a promising approach for generating novel views from a monocular video.
Denoising Task Routing for Diffusion Models
Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL).
Bridging Implicit and Explicit Geometric Transformation for Single-Image View Synthesis
Although recent methods generate high-quality novel views, synthesizing with only one explicit or implicit 3D geometry has a trade-off between two objectives that we call the "seesaw" problem: 1) preserving reprojected contents and 2) completing realistic out-of-view regions.
Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images
Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images.
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition
Here, we propose a unified framework and introduce two datasets for long-tailed camera-trap recognition.
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation
In this paper, we propose residual guidance loss that enables the depth estimation network to embed the discriminative feature by transferring the discriminability of auto-encoded features.
