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Found 18 papers, 12 papers with code
Text2Story: Advancing Video Storytelling with Text Guidance
We introduce a novel storytelling approach to enable seamless video generation with natural action transitions and structured narratives.
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion Models
We propose a novel diffusion guidance formulation, image frequency guidance, to steer the generation towards the manifold of the source subject and the driving action at every step of the inference denoising.
3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance
Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution.
Text Prompting for Multi-Concept Video Customization by Autoregressive Generation
We present a method for multi-concept customization of pretrained text-to-video (T2V) models.
Prompt Mixing in Diffusion Models using the Black Scholes Algorithm
We introduce a novel approach for prompt mixing, aiming to generate images at the intersection of multiple text prompts using pre-trained text-to-image diffusion models.
HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View
It seamlessly blends the visual features from the input image within a pretrained text-to-2Dimage stable diffusion model with a test-time optimization process for a careful bias-variance trade-off, which uses an Inverse Perspective Mapping (IPM) homography transformation to provide subtle cues for aerialview synthesis.
PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames.
Ranked #1 on
Action Recognition
on UAV-Human
Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models
Aerial Diffusion leverages a pretrained text-image diffusion model for prior knowledge.
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition
We build a differentiable static-dynamic frequency mask prior to model the salient static and dynamic pixels in the video, crucial for the underlying task of action recognition.
Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes
We compare our method, which we call PAAK, with prior approaches, including POSA, PROX ground truth, and a motion synthesis method, and highlight the benefits of our method with a perceptual study.
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection
SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation.
FAR: Fourier Aerial Video Recognition
Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
Ranked #1 on
Action Recognition
on UAV Human
GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on
Semantic Segmentation
on RUGD
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation
Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues.
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
In this work, we present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems.
