Trending Research
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
internlm/internlm-xcomposer
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We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
caiyuanhao1998/retinexformer
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ICCV 2023
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
Ranked #1 on
Low-Light Image Enhancement
on LOL-v2-synthetic
Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models
To address these challenges, we introduce a system that can jointly optimize distributed execution and gradient checkpointing plans.
MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation
We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
huawei-noah/Efficient-Computing
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In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
Ranked #3 on
Object Detection
on COCO 2017 val
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
stanfordnlp/dsp
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Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
huawei-noah/Efficient-Computing
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Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high.
GPT4Image: Can Large Pre-trained Models Help Vision Models on Perception Tasks?
huawei-noah/Efficient-Computing
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We present a new learning paradigm in which the knowledge extracted from large pre-trained models are utilized to help models like CNN and ViT learn enhanced representations and achieve better performance.
Positive-Unlabeled Compression on the Cloud
huawei-noah/DAFL
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NeurIPS 2019
In practice, only a small portion of the original training set is required as positive examples and more useful training examples can be obtained from the massive unlabeled data on the cloud through a PU classifier with an attention based multi-scale feature extractor.
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
adwardlee/renderih
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ICCV 2023
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited.
