Search Results for author:
Found 18 papers, 15 papers with code
AdaContour: Adaptive Contour Descriptor with Hierarchical Representation
Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes.
S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing
S3Editor is model-agnostic and compatible with various editing approaches.
OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators
Compressing a predefined deep neural network (DNN) into a compact sub-network with competitive performance is crucial in the efficient machine learning realm.
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape.
DREAM: Diffusion Rectification and Estimation-Adaptive Models
We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models.
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
CaesarNeRF explicitly models pose differences of reference views to combine scene-level semantic representations, providing a calibrated holistic understanding.
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs.
MMVP: Motion-Matrix-based Video Prediction
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames.
Automated Search-Space Generation Neural Architecture Search
To search an optimal sub-network within a general deep neural network (DNN), existing neural architecture search (NAS) methods typically rely on handcrafting a search space beforehand.
OTOV2: Automatic, Generic, User-Friendly
We propose the second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning.
Sparsity-guided Network Design for Frame Interpolation
As a result, we achieve a considerable performance gain with a quarter of the size of the original AdaCoF.
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
To our knowledge, RSOD is the first quantitatively evaluated and graded snowy OD dataset.
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) is the task of interpolating videos with both Low Frame Rate (LFR) and Low Resolution (LR) to produce High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts.
Ranked #2 on
Space-time Video Super-resolution
on Vimeo90K-Medium
Space-time Video Super-resolution
Video Frame Interpolation
+1
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices.
CDFI: Compression-Driven Network Design for Frame Interpolation
DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e. g., mobile devices.
Ranked #1 on
Video Frame Interpolation
on Middlebury
(LPIPS metric)
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig
To our knowledge this is the most complete dataset for super resolution, ISP and image quality enhancement.
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
We have validated our approach on four recognized datasets (three synthetic and one real-world).
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.
