anthology-of-the-sota

vision-and-graphics

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139 items with this tag.

Oct 03, 2026
LIT-775: Inductive Moment Matching
Oct 03, 2026
LIT-783: Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
Oct 03, 2026
LIT-784: Mean Flows for One-step Generative Modeling
Oct 03, 2026
LIT-787: One Step Diffusion via Shortcut Models
Oct 03, 2026
LIT-796: InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Oct 02, 2026
LIT-770: DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation
Oct 01, 2026
LIT-743: Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation
Oct 01, 2026
LIT-745: SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Oct 01, 2026
LIT-747: Perceiver: General Perception with Iterative Attention
Oct 01, 2026
LIT-751: "Principal Components" Enable A New Language of Images
Oct 01, 2026
LIT-755: Wonderland: Navigating 3D Scenes from a Single Image
Oct 01, 2026
SOTA-440: Supervise a feed-forward 3D reconstructor with a frozen camera-controlled video model's renderings instead of captured multi-view data
Sep 27, 2026
LIT-732: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Sep 26, 2026
LIT-723: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Sep 26, 2026
LIT-727: Striving for Simplicity: The All Convolutional Net
Sep 26, 2026
LIT-728: Visualizing and Understanding Convolutional Networks
Sep 26, 2026
LIT-730: A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Sep 26, 2026
LIT-731: SmoothGrad: removing noise by adding noise
- analysis-and-evaluation
- vision-and-graphics
Sep 26, 2026
SOTA-434: In a vision backbone, carry position with a relative bias inside the attention window and do not add an absolute position embedding on top
Sep 25, 2026
LIT-687: Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Sep 25, 2026
LIT-691: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Sep 25, 2026
LIT-703: Improved Precision and Recall Metric for Assessing Generative Models
Sep 25, 2026
LIT-706: Scaling Language-Image Pre-training via Masking
Sep 25, 2026
LIT-712: Axiomatic Attribution for Deep Networks
- analysis-and-evaluation
- vision-and-graphics
Sep 25, 2026
LIT-713: Sanity Checks for Saliency Maps
- analysis-and-evaluation
- vision-and-graphics
Sep 25, 2026
LIT-715: Surya: Foundation Model for Heliophysics
Sep 25, 2026
SOTA-414: To make an image diffusion model a dense geometric predictor, fine-tune it end to end as a one-step model at t = T with a task loss
Sep 25, 2026
SOTA-416: When sampling a diffusion model in few steps, start at t = T: use trailing, not leading, timestep spacing
Sep 25, 2026
SOTA-429: Drop half the image patches when training a CLIP-style model, spend the saving on more pairs and a larger batch, and unmask only for a short final tune
Sep 24, 2026
LIT-618: LTX-Video: Realtime Video Latent Diffusion
Sep 24, 2026
LIT-619: Wan: Open and Advanced Large-Scale Video Generative Models
Sep 24, 2026
LIT-620: HunyuanVideo: A Systematic Framework For Large Video Generative Models
Sep 24, 2026
LIT-621: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Sep 24, 2026
LIT-622: CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
Sep 24, 2026
LIT-623: Language Model Beats Diffusion — Tokenizer is Key to Visual Generation
Sep 24, 2026
LIT-624: Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Sep 24, 2026
LIT-625: Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
Sep 24, 2026
LIT-626: Movie Gen: A Cast of Media Foundation Models
Sep 24, 2026
LIT-627: Video Diffusion Models
- generative-modeling
- vision-and-graphics
Sep 24, 2026
LIT-628: Better plain ViT baselines for ImageNet-1k
Sep 24, 2026
LIT-629: Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Sep 24, 2026
LIT-631: From Slow Bidirectional to Fast Autoregressive Video Diffusion Models
Sep 24, 2026
LIT-632: Make-A-Video: Text-to-Video Generation without Text-Video Data
Sep 24, 2026
LIT-633: AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Sep 24, 2026
LIT-634: Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
Sep 24, 2026
LIT-635: Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
- generative-modeling
- vision-and-graphics
Sep 24, 2026
LIT-637: Imagen Video: High Definition Video Generation with Diffusion Models
Sep 24, 2026
LIT-643: One-step Diffusion with Distribution Matching Distillation
Sep 24, 2026
LIT-646: Improved Distribution Matching Distillation for Fast Image Synthesis
Sep 24, 2026
LIT-652: Video generation models as world simulators
Sep 24, 2026
LIT-657: Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Sep 24, 2026
LIT-658: FiT: Flexible Vision Transformer for Diffusion Model
Sep 24, 2026
LIT-661: Photorealistic Video Generation with Diffusion Models
Sep 24, 2026
LIT-662: Vision Transformers Need Registers
Sep 24, 2026
LIT-663: DINOv3
Sep 24, 2026
LIT-664: Emerging Properties in Self-Supervised Vision Transformers
Sep 24, 2026
SOTA-386: Show a video diffusion model images before and alongside video
Sep 24, 2026
SOTA-388: Train a plain ViT on ImageNet-1k with average pooling, fixed 2D sin-cos positions, a 1024 batch and light augmentation
Sep 24, 2026
SOTA-390: In a video diffusion transformer, attend over space and time jointly rather than factorizing, and budget for the cost
Sep 24, 2026
SOTA-393: Freeze the image model's spatial layers when adding time if checkpoint compatibility matters; do not expect a quality gain from it
Sep 24, 2026
SOTA-394: When distilling a causal few-step video generator, take the teacher bidirectional, not causal
Sep 24, 2026
SOTA-395: Post-train an autoregressive video generator on its own rollouts, and score the whole rollout
Sep 24, 2026
SOTA-396: When a latent video model reuses an image autoencoder or upsampler, fine-tune them on video so they see time
Sep 24, 2026
SOTA-397: To condition an unconditionally trained diffusion model on known frames, use reconstruction guidance, not replacement
Sep 24, 2026
SOTA-398: Pretrain a vision transformer on packed, aspect-preserved images at sampled resolutions, not on fixed square crops
Sep 24, 2026
SOTA-400: Append a few register tokens to a ViT so it does not commandeer background patches for scratch space
Sep 24, 2026
SOTA-401: Anchor the patch Gram matrix to an early teacher when training long, because dense features decay while global metrics improve
Sep 23, 2026
LIT-550: Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Sep 23, 2026
LIT-551: Implicit Neural Representations with Periodic Activation Functions
Sep 23, 2026
LIT-554: Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
- generative-modeling
- vision-and-graphics
Sep 23, 2026
LIT-559: Alias-Free Generative Adversarial Networks
Sep 23, 2026
LIT-560: Analyzing and Improving the Image Quality of StyleGAN
Sep 23, 2026
LIT-561: A Style-Based Generator Architecture for Generative Adversarial Networks
Sep 23, 2026
LIT-562: Projected GANs Converge Faster
Sep 23, 2026
LIT-564: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- generative-modeling
- vision-and-graphics
Sep 23, 2026
LIT-566: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
- generative-modeling
- vision-and-graphics
Sep 23, 2026
LIT-573: FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Sep 23, 2026
LIT-587: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Sep 23, 2026
LIT-588: Learning Transferable Visual Models From Natural Language Supervision
Sep 23, 2026
LIT-590: Momentum Contrast for Unsupervised Visual Representation Learning
Sep 23, 2026
LIT-591: A Simple Framework for Contrastive Learning of Visual Representations
Sep 23, 2026
LIT-593: Exploring Simple Siamese Representation Learning
Sep 23, 2026
LIT-594: Bootstrap your own latent: A new approach to self-supervised Learning
Sep 23, 2026
LIT-595: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Sep 23, 2026
LIT-596: Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Sep 23, 2026
LIT-598: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Sep 23, 2026
LIT-599: DINOv2: Learning Robust Visual Features without Supervision
Sep 23, 2026
LIT-601: Masked Autoencoders Are Scalable Vision Learners
Sep 23, 2026
LIT-605: Sigmoid Loss for Language Image Pre-Training
Sep 23, 2026
LIT-611: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Sep 23, 2026
LIT-614: Improved Techniques for Training GANs
Sep 23, 2026
LIT-615: Demystifying MMD GANs
Sep 23, 2026
SOTA-331: Pass low-dimensional coordinate inputs through sinusoids of sampled frequencies before an MLP, and tune the frequency scale rather than the distribution
- representation-and-encoding
- vision-and-graphics
Sep 23, 2026
SOTA-332: When an implicit representation will be supervised through its derivatives, use sine activations with the SIREN initialization rather than a ReLU network
Sep 23, 2026
SOTA-333: For autoregressive generation of continuous sequences, train with an independent noise level per token and condition the rollout on slightly noised history
- generative-modeling
- vision-and-graphics
Sep 23, 2026
SOTA-335: When generated content must move continuously, as in video or animation, build the generator alias-free: treat features as continuous signals and low-pass filter around every nonlinearity
Sep 23, 2026
SOTA-336: Train high-resolution GANs at a fixed topology with output skips in the generator and a residual discriminator, not by progressive growing
Sep 23, 2026
SOTA-338: Train the GAN discriminator on frozen multi-scale pretrained features with fixed random channel and scale mixing
Sep 23, 2026
SOTA-339: For unpaired image-to-image translation of appearance, constrain both mappings with a cycle-consistency loss
- generative-modeling
- vision-and-graphics
Sep 23, 2026
SOTA-341: Condition an image generator on each training image's original size instead of discarding or upsampling small images
Sep 23, 2026
SOTA-358: Drop the domain inductive bias once pre-training data is large enough, and keep it when it is not
Sep 23, 2026
SOTA-359: Supervise vision from the caption, and match image to caption rather than predicting its words
Sep 23, 2026
SOTA-383: Fix the sample count before comparing FID values, and settle a close comparison with an unbiased estimator rather than a tighter error bar
Sep 23, 2026
THEORY-095: No unbiased estimator of FID exists, so its plug-in bias can reverse a comparison while the sample variance stays small
Sep 21, 2026
LIT-475: Dropout Reduces Underfitting
Sep 21, 2026
LIT-488: gen2seg: Generative Models Enable Generalizable Instance Segmentation
Sep 21, 2026
LIT-494: GaussianToken: An Effective Image Tokenizer with 2D Gaussian Splatting
Sep 21, 2026
LIT-511: Image-GS: Content-Adaptive Image Representation via 2D Gaussians
Sep 21, 2026
SOTA-299: Finetune the whole generative model, encoder and decoder, for a dense perception task instead of attaching a head to a backbone
Sep 21, 2026
THEORY-052: Synthesis requires equivariant representations and discriminative pretraining rewards invariant ones, which is why a generative prior groups objects it was never shown
- representation-and-encoding
- vision-and-graphics
Sep 19, 2026
LIT-435: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
- vision-and-graphics
- representation-and-encoding
Sep 19, 2026
SOTA-250: Pretrain by predicting representations of masked regions, not their pixels
- vision-and-graphics
- representation-and-encoding
Sep 19, 2026
SOTA-251: Train at low resolution and raise it only during the decay phase of a warmup-stable-decay schedule
- training-optimization
- vision-and-graphics
Sep 19, 2026
SOTA-252: Make the full-resolution path affordable instead of upsampling, when the shortcut is what breaks correctness
- vision-and-graphics
- systems-optimization
Sep 19, 2026
SOTA-253: Add a new instance by reconstructing it, not by training on it
- vision-and-graphics
- model-architecture
Sep 18, 2026
LIT-421: Gradient-based learning applied to document recognition
- analysis-and-evaluation
- vision-and-graphics
Sep 18, 2026
LIT-422: ImageNet: A large-scale hierarchical image database
- analysis-and-evaluation
- vision-and-graphics
Sep 18, 2026
LIT-423: ImageNet Large Scale Visual Recognition Challenge
- analysis-and-evaluation
- vision-and-graphics
Sep 18, 2026
LIT-424: Learning Multiple Layers of Features from Tiny Images
- analysis-and-evaluation
- vision-and-graphics
Sep 17, 2026
LIT-384: VGGT: Visual Geometry Grounded Transformer
- vision-and-graphics
- model-architecture
Sep 17, 2026
LIT-385: DUSt3R: Geometric 3D Vision Made Easy
- vision-and-graphics
Sep 17, 2026
LIT-386: SuperGlue: Learning Feature Matching with Graph Neural Networks
- vision-and-graphics
- graphs-and-networks
Sep 17, 2026
LIT-387: LoFTR: Detector-Free Local Feature Matching with Transformers
- vision-and-graphics
- attention-techniques
Sep 17, 2026
SOTA-236: Predict scene geometry directly instead of solving for cameras first and triangulating
- vision-and-graphics
Sep 17, 2026
SOTA-237: Match densely without a keypoint detector, because the detector fails where matching is hardest
- vision-and-graphics
Sep 10, 2026
SOTA-205: Replace a large coordinate network with a compact explicit structure and a small decoder
- vision-and-graphics
Sep 07, 2026
LIT-215: V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- vision-and-graphics
Sep 07, 2026
LIT-216: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
- vision-and-graphics
- representation-and-encoding
Sep 07, 2026
LIT-218: Video models are zero-shot learners and reasoners
- vision-and-graphics
Aug 24, 2026
LIT-064: Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
- vision-and-graphics
Aug 24, 2026
LIT-072: Simple Open-Vocabulary Object Detection with Vision Transformers
Aug 24, 2026
LIT-086: K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
- vision-and-graphics
Aug 24, 2026
LIT-094: Neural Scene Graphs for Dynamic Scenes
- vision-and-graphics
Aug 24, 2026
LIT-096: Segment Anything
- vision-and-graphics
- data-pipeline
Aug 24, 2026
LIT-107: MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation
- vision-and-graphics
Aug 24, 2026
LIT-108: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
- vision-and-graphics
Aug 24, 2026
LIT-109: NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction
- vision-and-graphics
Aug 24, 2026
LIT-111: OnePose: One-Shot Object Pose Estimation without CAD Models
- vision-and-graphics
Aug 24, 2026
LIT-113: Gaussian Shell Maps for Efficient 3D Human Generation
- vision-and-graphics

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