All papers

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

This paper analyzes visual token utilization in high-resolution vision-language models to optimize token pruning across network layers.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper introduces a visual and multimodal knowledge graph grounding model designed for visual navigation tasks.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper presents a multimodal vision-language model for Alzheimer's disease classification and question answering capable of handling missing imaging or clinical data.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

The paper reviews language-vision fusion architectures and deployment strategies for medical diagnosis in low-resource healthcare settings.

The paper presents a framework for balancing multimodal integration using conditional dependency modeling across diverse data signals.

This paper introduces a multimodal meme dataset and a pipeline to extract entities and their semantic relationships.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints.

This paper introduces a multimodal federated learning framework that combines spectrograms and network metrics for on-device wireless jamming detection.

This paper presents an ensemble machine learning framework for tracking student knowledge and recommending multimodal content in tutoring systems.

The paper introduces a multimodal dataset combining physiological sensor signals and video recordings to study human social stress.

The paper presents a multimodal deep learning method using PET and CT scans to predict brain metastasis in non-small cell lung cancer.

The summary text is too thin to judge, containing mostly page navigation text instead of the research abstract.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.