Search Results for author:
Found 9 papers, 4 papers with code
skscope: Fast Sparsity-Constrained Optimization in Python
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact.
SDXL-Lightning: Progressive Adversarial Diffusion Distillation
We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL.
ManiCLIP: Multi-Attribute Face Manipulation from Text
In this paper we present a novel multi-attribute face manipulation method based on textual descriptions.
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
Hybrid Neural Networks for On-device Directional Hearing
On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements.
Ranked #1 on
Real-time Directional Hearing
on VCTK
All Tokens Matter: Token Labeling for Training Better Vision Transformers
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs).
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
Holistic Multi-modal Memory Network for Movie Question Answering
In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.
Modality and Component Aware Feature Fusion For RGB-D Scene Classification
While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification.
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition
We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities.
