We conduct an empirical investigation to show fine-tuning will corrupt the context-aware ability of pre-trained CLIP features. To solve this problem, we propose Context-Aware Robust Fine-tuning (CAR-FT). CAR-FT regularizes the model during fine-tuning to capture the context information. ...
Naive chunking strategies often result in poor outputs for synthetic data generation and thereby finetuning of language models. Context aware chunking can result in reduced hallucinations involving complex document structures. This can facilitate seamless integration across various departments within an organiz...
namely, protein-protein interfaces or PPIs and the complementarity determining regionsR or CDR loops of antibodies. We investigate the performance of models derived from three fine-tuning strategies (Figure 1C). With fine-tuning strategy I, we aim to fine-tune models for the specific functional ...
While umcp run or docker compose up are fine for development, consider these for more robust deployments:1. Running as a Background ServiceEnsure the server runs continuously and restarts automatically.systemd (Linux): Create a service unit file (.service) to manage the process with systemctl ...
Robust Error Handling: Detailed error context and recovery strategies Overview Spatial Feature Understanding: OmniMCP begins by developing a deep understanding of the user interface's visual layout. Leveraging microsoft/OmniParser, it performs detailed visual parsing, segmenting the screen and identifying ...
The improved results obtained by unfreezing the weights of more layers than solely the last fully connected layer (considered for EgoTerrainNets fine-tuning) was likely due to the fewer number of classes in the binary classification approach (vs 5 for EgoTerrainNet-Outdoor), and thus, the ...
(Omni Context Aware Transformer) is a powerful model that leverages RoTE (Rotary Time Embeddings), an innovative extension of RoPE, to enhance temporal grounding and computational efficiency in time-anchored tasks. Through a robust three-stage training pipeline-feature alignment, instruction tuning, ...
Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods hav
To further explore the impact of visual instruction tuning, we transformed Creation-MMBench into a text-only variant, Creation-MMBench-TO, by replacing image inputs with corresponding textual descriptions. Robust Evaluation Methodology. Creation-MMBench includes carefully crafted instance-specific criteria...
The improved results obtained by unfreezing the weights of more layers than solely the last fully connected layer (considered for EgoTerrainNets fine-tuning) was likely due to the fewer number of classes in the binary classification approach (vs 5 for EgoTerrainNet-Outdoor), and thus, the ...