All papers

The paper proposes VEDJE, compressing video features into reusable caches with feature-change prediction for efficient and scalable video-text retrieval.

The paper introduces FreeInpaint, a feed-forward framework performing 3D scene inpainting directly from unposed multi-view images using masked attention propagation.

The paper introduces FreeInpaint, a feed-forward framework performing 3D scene inpainting directly from unposed multi-view images using masked attention propagation.

The paper proposes Relative Patch Response Learning to improve AI-generated image detection by mitigating self-attention-induced contextual shifts across mixed patches.

The paper proposes Relative Patch Response Learning to improve AI-generated image detection by mitigating self-attention-induced contextual shifts across mixed patches.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series.

The paper evaluates whether contrastive encoder adversarial alignment metrics transfer to target recovery by independent models across different perturbation budgets.

The paper introduces LGWM, an action-conditioned world model predicting next-screen latent representations to verify GUI agent state transitions.

The paper presents CELL, learning pose-informative correspondence confidence end-to-end via differentiable probabilistic PnP to localize event cameras in LiDAR maps.

The paper uses activation steering of affective circuits in vision-language models to increase hazard-detection recall for post-disaster damage assessment.

The paper proposes EUA-Cal, regularizing late training against early-stage predictive uncertainty to prevent models from developing overconfidence.

The paper proposes EUA-Cal, regularizing late training against early-stage predictive uncertainty to prevent models from developing overconfidence.

The paper introduces ViMoD, maintaining a compact visual context while dynamically routing back to fine-grained tokens during multi-step reasoning.

The paper presents LIVIN, a spatial and embodied intelligence benchmark featuring digital twins of 30 lived-in homes with human-reviewed object arrangements.

The paper proposes a compact optical-flow representation to classify tactile object rotation across Earth, Mars, Moon, and orbital gravity domains.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

Recasts referring expression segmentation as a closed-loop policy iteratively refining explicit boundary contours via visuomotor actions.

Presents an agent harness that pairs frozen multimodal language models with modular navigation skills for general robot navigation tasks.

Uses diffusion inpainting to erase lesions from mammograms, creating synthetic healthy counterfactuals to augment breast cancer classification training.

Uses diffusion inpainting to erase lesions from mammograms, creating synthetic healthy counterfactuals to augment breast cancer classification training.

Proposes a training framework reconnecting backward gradient paths across autoregressive chunks to improve long video generation consistency.

Proposes a training framework reconnecting backward gradient paths across autoregressive chunks to improve long video generation consistency.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Presents a semi-automatic stride-independent image patching method guided by user input to train deep learning segmentation models.

Presents a semi-automatic stride-independent image patching method guided by user input to train deep learning segmentation models.

Proposes a set-associative spatial memory retrofit for streaming 3D reconstruction to preserve distinct viewpoints at shared spatial locations.

Proposes a set-associative spatial memory retrofit for streaming 3D reconstruction to preserve distinct viewpoints at shared spatial locations.

Investigates extracting spatial, attribute, and relational modifier grounding directly from frozen multimodal large language model representations using optimal transport.

Introduces an adaptive tokenization method allocating variable compute budgets to image diffusion based on visual complexity and spatial layout.

Introduces an adaptive tokenization method allocating variable compute budgets to image diffusion based on visual complexity and spatial layout.

Develops a framework incorporating character animation principles like exaggeration into generative motion models via adaptation and guidance.

Develops a framework incorporating character animation principles like exaggeration into generative motion models via adaptation and guidance.

Introduces a multi-dimensional benchmark evaluating reasoning-informed visual editing across temporal, causal, spatial, and logical operations.

Proposes representing robot policies entirely in deterministic stateful code instead of querying vision-language models during run-time execution loops.

Proposes representing robot policies entirely in deterministic stateful code instead of querying vision-language models during run-time execution loops.

Presents a framework linking game engines with a neural renderer to simulate interactive virtual environments for training embodied agents.

Presents a framework linking game engines with a neural renderer to simulate interactive virtual environments for training embodied agents.

Proposes a decoupled 4D reward framework separating static scenes from dynamic objects to improve consistency in generative video models.

Proposes a decoupled 4D reward framework separating static scenes from dynamic objects to improve consistency in generative video models.

Presents a test-time alignment framework that grounds generative 3D foundation models in observed geometry and appearance without model retraining.

Presents a test-time alignment framework that grounds generative 3D foundation models in observed geometry and appearance without model retraining.

Proposes a distributed multiplayer world model architecture where individual clients generate synchronized video views by exchanging player state fields.

Proposes a distributed multiplayer world model architecture where individual clients generate synchronized video views by exchanging player state fields.