Search Results for author:
Found 7 papers, 3 papers with code
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
Additionally, this study explores a novel recurrent routing strategy that may inspire further advancements in enhancing the reasoning capabilities of language models.
Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation
In this paper, we put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD), which can detect objects beyond training categories without costly collecting new labeled data.
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object Scenarios
CamObj-Instruct is collected for fine-tuning the LVLMs with improved instruction-following capabilities, and it includes 11, 363 images and 68, 849 conversations with diverse instructions.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
The performance of OVD greatly relies on the quality of class-agnostic region proposals and pseudo-labels for novel object categories.
On the Evaluation and Refinement of Vision-Language Instruction Tuning Datasets
Instead of evaluating the models directly, in this paper, we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets.
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i. e., not seen during training), they always predict them as one of the training classes.
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction.
