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Found 8 papers, 4 papers with code
ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
We demonstrate the ability of diagnosing the given histopathology images using ChatEXAONEPath with the acceptance rate of 62. 9% from 1, 134 pairs of WSIs and reports.
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations
Our approach includes two key innovations: (1) DST-attention, a novel attention mechanism that disentangles the spatial and temporal tokens within the LLM by using masked attention to restrict direct interactions between the spatial and temporal tokens; (2) Harmonic-RoPE, which extends the dimensionality of the positional IDs, allowing the spatial and temporal tokens to maintain balanced positions relative to the text tokens.
See It All: Contextualized Late Aggregation for 3D Dense Captioning
SIA simultaneously decodes two sets of queries-context query and instance query.
Bi-directional Contextual Attention for 3D Dense Captioning
3D dense captioning is a task involving the localization of objects and the generation of descriptions for each object in a 3D scene.
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
To address this issue, we introduce EXAONEPath, a novel foundational model trained on patches that have undergone stain normalization.
Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition
Vision and language models (VLMs) such as CLIP have showcased remarkable zero-shot recognition abilities yet face challenges in visio-linguistic compositionality, particularly in linguistic comprehension and fine-grained image-text alignment.
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes.
L-Verse: Bidirectional Generation Between Image and Text
Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target.
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