Data:

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Background:

Source: statcdn

Charts talk

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For context

Source: pewresearch

This is instructive

Source: pewresearch

This is instructive

Source: migrationpolicy

Evaluates DiT-based architectures, incl. PixArt-style/MMDiT, explores concatenated text/noise inputs. Novel parameter-sharing cuts model size by 66% with minimal impact. Analyzes text-conditioning and VAEs for better image quality. Sets SOTA on GenEval & T2I CompBench.

Uses SE(3) equivariant vectors for tight cloth-to-body fitting with sparse markers. Excels in loose clothing & diverse scenarios, outperforming other methods. Detailed ablation studies highlight equivariance benefits & marker use. Strong across extreme poses & garment dynamics.

Uniform graph aids diverse goal handling (objects, images, text) boosting generalization. Converts observations into scene graphs for structured navigation. Utilizes graph matching, dynamic strategies like subgraph search, and blacklist for robust transitions.

Enhances content/style consistency with two-step strategy separating content/style learning. Stepwise loss transition captures global/local details. Inference guidance controls content/style. Outperforms SOTA in reducing content leakage and improving style alignment.

Leverages hierarchical perturbations via patient-slide-patch correlations in biomedical images. Outperforms current self-supervised adversarial methods on OpenSRH with hierarchical adversarial perturbations. Employs multi-level contrastive learning to boost robustness.

Empirical evidence: DyT in Transformers can match/exceed norm layers in vision/language tasks, supervised/self-supervised settings. Challenges norm layer necessity, offers potential speed benefits, and eases hyperparameter tuning, making it apt for efficient network designs.

Used DAG method for Hugging Face model atlas. Shows predicting CV model attributes/trends. Proposed charting undocumented regions with high-confidence priors. Improved documentation approach. Provided datasets, code, and interactive atlas for research.

Integrates diffusion & autoregressive action prediction in a single LLM for seamless robotic action plans. Collaborative training fuses predictions from both approaches, boosting task robustness and generalization to new environments. Optimized for 9.4 Hz inference speed.

Progressive strategy breaks edits into subtasks via a unified control. Utilizes initial noise, step noise & cross-attention maps for precision. 'Render-edit-reconstruct' method extends edits to 3D, handling fast cameras and major geometric shifts effectively.

Targets semantically rich areas over uniform distributions. Uses novel optimization with random cropping/resizing for improved embedding alignment. Model ensemble boosts semantic perturbations. Introduces KMRScore to objectively gauge attack transferability.