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Found 10 papers, 4 papers with code
MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound Vocalization Challenge
The findings suggest promising directions for vocalization analysis and highlight the potential value of audible and ultrasound vocalizations in ASD detection.
LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis
Then we propose a suitable solution according to the modality differences of image, audio, and video generation.
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
In brief, we use the prompts of overlapping masks as corresponding negative signals, resulting in refined masks.
GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval
Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query.
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition
Our approach enriches the context by utilizing label semantics as suffix prompts.
Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions.
Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images
Technically, we learn two parallel streams of an implicit signed distance field and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the implicit signed distance function (SDF) and surface rendering of the surrogate mesh with a shared, neural shader; the unified shading promotes their convergence to the same, underlying surface.
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering.
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently.
Better Pre-Training by Reducing Representation Confusion
Motivated by the above investigation, we propose two novel techniques to improve pre-trained language models: Decoupled Directional Relative Position (DDRP) encoding and MTH pre-training objective.
