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pro vyhledávání: '"JUN, CHENG"'
Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their success beyond image generation, showing competitive performance with dis
Externí odkaz:
http://arxiv.org/abs/2411.17236
In this work, we propose a unified representation for Super-Resolution (SR) and Image Compression, termed Factorized Fields, motivated by the shared principles between these two tasks. Both SISR and Image Compression require recovering and preserving
Externí odkaz:
http://arxiv.org/abs/2410.18083
Prompt Tuning has been a popular Parameter-Efficient Fine-Tuning method attributed to its remarkable performance with few updated parameters on various large-scale pretrained Language Models (PLMs). Traditionally, each prompt has been considered indi
Externí odkaz:
http://arxiv.org/abs/2410.12847
Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochasti
Externí odkaz:
http://arxiv.org/abs/2410.05760
Learning a discriminative model to distinguish a target from its surrounding distractors is essential to generic visual object tracking. Dynamic target representation adaptation against distractors is challenging due to the limited discriminative cap
Externí odkaz:
http://arxiv.org/abs/2409.18901
Autor:
Liao, Jia-Wei, Wang, Winston, Wang, Tzu-Sian, Peng, Li-Xuan, Weng, Ju-Hsuan, Chou, Cheng-Fu, Chen, Jun-Cheng
With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabi
Externí odkaz:
http://arxiv.org/abs/2409.06355
One common belief is that with complex models and pre-training on large-scale datasets, transformer-based methods for referring expression comprehension (REC) perform much better than existing graph-based methods. We observe that since most graph-bas
Externí odkaz:
http://arxiv.org/abs/2409.03385
Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. However, the ease of text-based image editing introduces significant risks, such as malicious e
Externí odkaz:
http://arxiv.org/abs/2408.11810
Class agnostic counting (CAC) is a vision task that can be used to count the total occurrence number of any given reference objects in the query image. The task is usually formulated as a density map estimation problem through similarity computation
Externí odkaz:
http://arxiv.org/abs/2404.09826
With the rise of deep learning, generative models have enabled the creation of highly realistic synthetic images, presenting challenges due to their potential misuse. While research in Deepfake detection has grown rapidly in response, many detection
Externí odkaz:
http://arxiv.org/abs/2404.05583