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Found 35 papers, 22 papers with code
Chain of Thought Explanation for Dialogue State Tracking
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding values.
Sora Generates Videos with Stunning Geometrical Consistency
We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality.
Magic-Me: Identity-Specific Video Customized Diffusion
To achieve this, we propose three novel components that are essential for high-quality identity preservation and stable video generation: 1) a noise initialization method with 3D Gaussian Noise Prior for better inter-frame stability; 2) an ID module based on extended Textual Inversion trained with the cropped identity to disentangle the ID information from the background 3) Face VCD and Tiled VCD modules to reinforce faces and upscale the video to higher resolution while preserving the identity's features.
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
Copyright is a legal right that grants creators the exclusive authority to reproduce, distribute, and profit from their creative works.
Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost
Accordingly, we propose a novel factorization self-attention mechanism (FaSA) that enjoys both the advantages of local window cost and long-range dependency modeling capability.
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing, demonstrating exceptional capabilities in reasoning, tool usage, and memory.
ChatAnything: Facetime Chat with LLM-Enhanced Personas
For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically.
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome
On the hardware side, we modify the datapath of current PIM accelerators to accommodate epitomes and implement a feature map reuse technique to reduce computation cost.
Low-Resolution Self-Attention for Semantic Segmentation
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
In this work, we identify that a crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning between the prompt and the output image.
Dataset Quantization
Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human.
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains.
DiM: Distilling Dataset into Generative Model
To the best of our knowledge, we are the first to achieve higher accuracy on complex architectures than simple ones, such as 75. 1\% with ResNet-18 and 72. 6\% with ConvNet-3 on ten images per class of CIFAR-10.
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
To solve this problem, we propose \textbf{InfoBatch}, a novel framework aiming to achieve lossless training acceleration by unbiased dynamic data pruning.
Diffusion Probabilistic Model Made Slim
Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms.
Expanding Small-Scale Datasets with Guided Imagination
Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content.
MagicVideo: Efficient Video Generation With Latent Diffusion Models
In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model.
Ranked #10 on
Text-to-Video Generation
on MSR-VTT
MagicMix: Semantic Mixing with Diffusion Models
Unlike style transfer, where an image is stylized according to the reference style without changing the image content, semantic blending mixes two different concepts in a semantic manner to synthesize a novel concept while preserving the spatial layout and geometry.
Deep Model Reassembly
Given a collection of heterogeneous models pre-trained from distinct sources and with diverse architectures, the goal of DeRy, as its name implies, is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints.
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
With the proposed SSF, our model obtains 2. 46% (90. 72% vs. 88. 54%) and 11. 48% (73. 10% vs. 65. 57%) performance improvement on FGVC and VTAB-1k in terms of Top-1 accuracy compared to the full fine-tuning but only fine-tuning about 0. 3M parameters.
Sharpness-Aware Training for Free
Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights.
Understanding The Robustness in Vision Transformers
Our study is motivated by the intriguing properties of the emerging visual grouping in Vision Transformers, which indicates that self-attention may promote robustness through improved mid-level representations.
Ranked #4 on
Domain Generalization
on ImageNet-R
(using extra training data)
M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
In this paper, we propose M$^2$BEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inputs.
Shunted Self-Attention via Multi-Scale Token Aggregation
This novel merging scheme enables the self-attention to learn relationships between objects with different sizes and simultaneously reduces the token numbers and the computational cost.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #174 on
Image Classification
on ImageNet
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)
AutoSpace: Neural Architecture Search with Less Human Interference
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction.
DeepViT: Towards Deeper Vision Transformer
In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper.
Ranked #426 on
Image Classification
on ImageNet
Coordinate Attention for Efficient Mobile Network Design
Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e. g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps.
ConvBERT: Improving BERT with Span-based Dynamic Convolution
The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning.
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
Ranked #70 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Neural Epitome Search for Architecture-Agnostic Network Compression
The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs).
