Dettoolchain: A new prompting paradigm to unleash detection ability of mllm
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object
detection ability of multimodal large language models (MLLMs), such as GPT-4V and …
detection ability of multimodal large language models (MLLMs), such as GPT-4V and …
SimDETR: Simplifying self-supervised pretraining for DETR
DETR-based object detectors have achieved remarkable performance but are sample-
inefficient and exhibit slow convergence. Unsupervised pretraining has been found to be …
inefficient and exhibit slow convergence. Unsupervised pretraining has been found to be …
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
Self-supervised pre-training and transformer-based networks have significantly improved
the performance of object detection. However, most of the current self-supervised object …
the performance of object detection. However, most of the current self-supervised object …
[PDF][PDF] Supplementary Material SeqCo-DETR: Sequence Consistency Training for Self-supervised Object Detection with Transformers
We organize the supplementary material as follows. The implementation details are given in
Sec. 2. More results and the ablation study are presented in Sec. 3. Then, we visualize the …
Sec. 2. More results and the ablation study are presented in Sec. 3. Then, we visualize the …
Simplifying Self-Supervised Object Detection Pretraining
Object detectors are often trained by first training the backbone in a self-supervised manner
and then fine-tuning the whole model on annotated data. An unsupervised detector …
and then fine-tuning the whole model on annotated data. An unsupervised detector …