All papers

The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments.

The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models.

The paper introduces a training-free technique and benchmark to help vision-language models correctly process and reject negated concepts in text prompts.

The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras.

The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models.

The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime.

The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks.

The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation.

The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks.

The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites.

The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images.

The paper introduces a 4D latent cache to reuse static and dynamic visual representations for efficient conditioning in video generation models.

Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics.

Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback.

Introduces HelpMeOut, an embodied AI benchmark for diagnosing human execution errors across household activity trajectories motivated by neuropsychology research.

Proposes a training-free framework that mitigates vision-language model hallucinations by admitting claims only when supported relative to a visual-null input.

Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations.

This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation.

Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers.

Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures.

Analyzes answerability signals in frozen visual question answering models, finding standard confidence and alignment metrics fail to reliably detect unanswerable inputs.

Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning.

Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently.

Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation.

Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals.

Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images.

The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery.

FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods.

The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets.

The paper introduces a training-free token pruning and merging framework with new robustness metrics for vision transformers.

This paper introduces an agentic post-training framework that coordinates multiple agents to evaluate and adaptively train text-to-image models.

This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding.

This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks.

The authors present a framework using level-wise multimodal memory to reconstruct coherent heading hierarchies across long, multi-page documents.

LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection.

FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models.

AffectWorld benchmarks world models on predicting emotion-driven human actions and subsequent environment changes across annotated video samples.

ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis.

Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation.

The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use.

The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar.

The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation.

The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks.

The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback.

The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication.

The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks.

The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data.

The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass.

The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations.

The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling.

This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character.

The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics.

This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors.

The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model.

This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models.

The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics.

The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning.

The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks.

The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback.

The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics.