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- Graph Contrastive Learning with Augmentations3,051
The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, the GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods.
- EnlightenGAN: Deep Light Enhancement Without Paired Supervision2,636
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
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- Benchmarking Single-Image Dehazing and Beyond2,349
This work presents a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE), which highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes.
- DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better1,173
It is demonstrated that DeblurGAN-V2 has very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency, and is effective for general image restoration tasks too.
- H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models1,025
A novel approach for implementing the KV cache eviction policy that dynamically retains a balance of recent and H$_2$ tokens is introduced and a theoretical guarantee for the novel eviction algorithm is proved.
- Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators896
This paper proposes a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods, making them suitable for the video domain, and introduces a new task, zero-shot text-to-video generation.
- GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection563
This work proposes GaLore, a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA, and demonstrates the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory without model parallel, checkpointing, or offloading strategies.
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- Single Image Deraining: A Comprehensive Benchmark Analysis371
A comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images, and provides a rich variety of criteria for dehazing algorithm evaluation.
- Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models369
The Forget-Me-Not method is proposed, a model-centric and efficient solution designed to remove identities, objects, or styles from a well-configured text-to-image model in as little as 30 seconds, without significantly impairing its ability to generate other content.
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
- AutoGAN: Neural Architecture Search for Generative Adversarial Networks308
This paper presents the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN, and discovers architectures that achieve highly competitive performance compared to current state-of-the-art hand-crafted GANs.
- Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning301
This work introduces adversarial training into self-supervision, to provide general-purpose robust Pretrained models for the first time, and finds these robust pretrained models can benefit the subsequent fine-tuning in two ways: i) boosting final model robustness; ii) saving the computation cost, if proceeding towards adversarial fine- Tuning.
- Versatile Diffusion: Text, Images and Variations All in One Diffusion Model295
This work expands the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image- to-text, and variations in one unified model.
- Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks289
This paper discovers for the first time that the winning tickets can be identified at the very early training stage, which it is term as early-bird (EB) tickets, via low-cost training schemes at large learning rates, consistent with recently reported observations that the key connectivity patterns of neural networks emerge early.
- StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text273
Experiments show that StreamingT2V produces more motion, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion, surpassing competitors in both consistency and motion.
- Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice249
This paper establishes a rigorous theory framework to analyze ViT features from the Fourier spectrum domain, and shows that the self-attention mechanism inherently amounts to a low-pass filter, which indicates when ViT scales up its depth, excessive low- pass filtering will cause feature maps to only preserve their Direct-Current component.
- When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach248
This paper proposes a convolutional neural network for image denoising which achieves the state-of-the-art performance and proposes a deep neural network solution that cascades two modules for image Denoising and various high-level tasks, respectively, and uses the joint loss for updating only the denoise network via back-propagation.
- Self-Supervised Learning for Generalizable Out-of-Distribution Detection234
This work proposes a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time, which does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods.
- SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image233
It is shown that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results and significantly outperforms the current state-of-the-art NeRF baselines in all cases.
- Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models207
Patch Diffusion is proposed, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users.
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- Connecting Image Denoising and High-Level Vision Tasks via Deep Learning200
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.
- Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images189
This work proposes collaborative Global-Local Networks (GLNet) to effectively preserve both global and local information in a highly memory-efficient manner, and achieves much more competitive accuracy-memory usage trade-offs compared to state-of-the-arts.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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