6 papers · 2 filters
Dataset Quantization with Active Learning based Adaptive Sampling
Zhenghao Zhao, Yuzhang Shang, Junyi Wu +1
Deep learning has made remarkable progress recently, largely due to the availability of large, well-labeled datasets. However, the training on such datasets elevates costs and comp…
Visual Grounding with Attention-Driven Constraint Balancing
Weitai Kang, Luowei Zhou, Junyi Wu +2
Unlike Object Detection, Visual Grounding task necessitates the detection of an object described by complex free-form language. To simultaneously model such complex semantic and vi…
PTQ4DiT: Post-training Quantization for Diffusion Transformers
Junyi Wu, Haoxuan Wang, Yuzhang Shang +2
The recent introduction of Diffusion Transformers (DiTs) has demonstrated exceptional capabilities in image generation by using a different backbone architecture, departing from tr…
On the Faithfulness of Vision Transformer Explanations
Junyi Wu, Weitai Kang, Hao Tang +2
To interpret Vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these interpretations refl…
Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformer
Junyi Wu, Bin Duan, Weitai Kang +2
While Transformers have rapidly gained popularity in various computer vision applications, post-hoc explanations of their internal mechanisms remain largely unexplored. Vision Tran…
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Haoxuan Wang, Yuzhang Shang, Zhihang Yuan +3
The practical deployment of diffusion models is still hindered by the high memory and computational overhead. Although quantization paves a way for model compression and accelerati…