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Found 40 papers, 14 papers with code
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
We also introduce a relation regularization method to further disentangle image transformation features from irrelevant contents in exemplar images.
Towards Automated Model Design on Recommender Systems
Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware.
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.
An Analysis on Quantizing Diffusion Transformers
Prior works address PTQ of DMs on UNet structures have addressed the challenges in calibrating parameters for both activations and weights via moderate optimization.
Layout Agnostic Scene Text Image Synthesis with Diffusion Models
While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge.
Efficient Quantization Strategies for Latent Diffusion Models
By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies.
Cache Me if You Can: Accelerating Diffusion Models through Block Caching
However, one of the major drawbacks of diffusion models is that the image generation process is costly.
LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning
Additionally, existing diffusion-based image manipulation models are sub-optimal in controlling the state transition of an action in egocentric image pixel space because of the domain gap.
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e. g., ~4 AP on COCO/LVIS) over other fast SAM models.
Ranked #3 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations
However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns.
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence
Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence.
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion
We note that the novelty of our model lies in that we introduce contrastive learning during training the diffusion prior which enables the generation of the valid view-invariant latent code.
Trainable Projected Gradient Method for Robust Fine-tuning
To solve this problem, we propose Trainable Projected Gradient Method (TPGM) to automatically learn the constraint imposed for each layer for a fine-grained fine-tuning regularization.
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors
Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images.
Pruning Compact ConvNets for Efficient Inference
The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark.
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference
Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens.
3D-Aware Encoding for Style-based Neural Radiance Fields
In the first stage, we introduce a base encoder that converts the input image to a latent code.
Token Merging: Your ViT But Faster
Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518 models on images and 2. 2x the throughput of ViT-L on video with only a 0. 2-0. 3% accuracy drop in each case.
Ranked #13 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions.
Ranked #4 on
Semantic Segmentation
on Replica
Hydra Attention: Efficient Attention with Many Heads
While transformers have begun to dominate many tasks in vision, applying them to large images is still computationally difficult.
Open-Set Semi-Supervised Object Detection
To address this problem, we consider online and offline OOD detection modules, which are integrated with SSOD methods.
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
Cross-Domain Adaptive Teacher for Object Detection
To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap.
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run
To tackle these challenges, we propose FBNetV5, a NAS framework that can search for neural architectures for a variety of vision tasks with much reduced computational cost and human effort.
Ranked #7 on
Neural Architecture Search
on ImageNet
Adaptive Unbiased Teacher for Cross-Domain Object Detection
This enables the student model to capture domain-invariant features.
An Investigation on Hardware-Aware Vision Transformer Scaling
In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from $74. 6\%$ to $76. 7\%$ ($\uparrow2. 1\%$) under the same 0. 7G FLOPs; and when transferred to the COCO object detection task, the average precision is boosted by $\uparrow0. 7\%$ under a similar throughput on a V100 GPU.
Visual Transformers: Where Do Transformers Really Belong in Vision Models?
A recent trend in computer vision is to replace convolutions with transformers.
FP-NAS: Fast Probabilistic Neural Architecture Search
Furthermore, to search fast in the multi-variate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude.
Fully Dynamic Inference with Deep Neural Networks
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction.
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships.
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously.
Ranked #5 on
Neural Architecture Search
on ImageNet
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands.
Ranked #68 on
Neural Architecture Search
on ImageNet
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
On MNIST dataset, our CNN architecture achieves an error rate of 0. 66%, with 8. 6x fewer parameters compared to the LeNet-5 baseline.
DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks
For server (edge) side inference, we achieve a 96. 3% (95. 3%) accuracy in classifying diabetics against healthy individuals, and a 95. 7% (94. 6%) accuracy in distinguishing among type-1/type-2 diabetic, and healthy individuals.
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications.
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Ranked #946 on
Image Classification
on ImageNet
Grow and Prune Compact, Fast, and Accurate LSTMs
To address these problems, we propose a hidden-layer LSTM (H-LSTM) that adds hidden layers to LSTM's original one level non-linear control gates.
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
To address these problems, we introduce a network growth algorithm that complements network pruning to learn both weights and compact DNN architectures during training.
