Search Results for author:
Found 39 papers, 24 papers with code
Autoregressive Distillation of Diffusion Transformers
ARD offers two key benefits: 1) it mitigates exposure bias by utilizing a predicted historical trajectory that is less susceptible to accumulated errors, and 2) it leverages the previous history of the ODE trajectory as a more effective source of coarse-grained information.
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute
Despite their remarkable performance, modern Diffusion Transformers are hindered by substantial resource requirements during inference, stemming from the fixed and large amount of compute needed for each denoising step.
Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment
The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference.
Movie Gen: A Cast of Media Foundation Models
Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation.
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation
Diffusion models are a powerful generative framework, but come with expensive inference.
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models.
Animated Stickers: Bringing Stickers to Life with Video Diffusion
Our model is built on top of the state-of-the-art Emu text-to-image model, with the addition of temporal layers to model motion.
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations
In this study, we propose two novel input processing paradigms for novel view synthesis (NVS) methods based on layered scene representations that significantly improve their runtime without compromising quality.
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
Our findings provide insights into the efficiency of the conditional denoising process that contribute to more practical and swift deployment of text-conditioned diffusion models.
Bespoke Solvers for Generative Flow Models
For example, a Bespoke solver for a CIFAR10 model produces samples with Fr\'echet Inception Distance (FID) of 2. 73 with 10 NFE, and gets to 1% of the Ground Truth (GT) FID (2. 59) for this model with only 20 NFE.
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task.
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists.
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs).
Re-ReND: Real-time Rendering of NeRFs across Devices
Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines.
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input.
Snapshot HDR Video Construction Using Coded Mask
This paper study the reconstruction of High Dynamic Range (HDR) video from snapshot-coded LDR video.
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #36 on
3D Part Segmentation
on ShapeNet-Part
MovieCuts: A New Dataset and Benchmark for Cut Type Recognition
Advances in automatic Cut-type recognition can unleash new experiences in the video editing industry, such as movie analysis for education, video re-editing, virtual cinematography, machine-assisted trailer generation, machine-assisted video editing, among others.
Learning to Cut by Watching Movies
Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks.
Combating Adversaries with Anti-Adversaries
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks.
MAAS: Multi-modal Assignation for Active Speaker Detection
Active speaker detection requires a solid integration of multi-modal cues.
Active Speaker Detection
Audio-Visual Active Speaker Detection
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions
We add our generalized aggregation into a deep GCN framework and show it achieves state-of-the-art results in six benchmarks from OGB.
SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation
Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network.
Video Self-Stitching Graph Network for Temporal Action Localization
We have two key components in VSGN: video self-stitching (VSS) and cross-scale graph pyramid network (xGPN).
Ranked #17 on
Temporal Action Localization
on ActivityNet-1.3
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks
In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.
DeeperGCN: All You Need to Train Deeper GCNs
Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Rethinking Clustering for Robustness
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness.
Gabor Layers Enhance Network Robustness
We revisit the benefits of merging classical vision concepts with deep learning models.
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds
Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network.
SGAS: Sequential Greedy Architecture Search
Architecture design has become a crucial component of successful deep learning.
Ranked #4 on
Node Classification
on PPI
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
G-TAD: Sub-Graph Localization for Temporal Action Detection
In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.
Ranked #5 on
Temporal Action Localization
on EPIC-KITCHENS-100
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #5 on
3D Semantic Segmentation
on PartNet
BAOD: Budget-Aware Object Detection
We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation.
DeepGCNs: Can GCNs Go as Deep as CNNs?
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
In fact, we show how Morton features can be used to significantly improve performance (+3% for 2 popular semantic segmentation algorithms) in the task of semantic segmentation of point clouds on the challenging and large-scale S3DIS dataset.
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
RefineLoc shows competitive results with the state-of-the-art in weakly-supervised temporal localization.
Temporal Localization
Weakly Supervised Action Localization
+2
Robust Manhattan Frame Estimation From a Single RGB-D Image
This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image.
