Coterminous
发表于 2025-3-23 10:00:04
http://reply.papertrans.cn/25/2424/242326/242326_11.png
amnesia
发表于 2025-3-23 14:22:04
http://reply.papertrans.cn/25/2424/242326/242326_12.png
Factual
发表于 2025-3-23 21:29:02
http://reply.papertrans.cn/25/2424/242326/242326_13.png
惩罚
发表于 2025-3-24 01:42:16
,FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance,e utilized CLIP by incorporating manually designed visual prompts like colored circles and blur masks into the images to guide the model’s attention, showing enhanced zero-shot performance in downstream tasks. Although these methods have achieved promising results, they inevitably alter the original
guzzle
发表于 2025-3-24 02:41:24
,: Taking a Further Step to Universal 9D Category-Level Object Pose Estimation,r respective domains. However, a universal framework capable of estimating the pose of both rigid and articulated objects has yet to be reported. In this paper, we introduce a .niversal 9D .ategory-level .bject .ose .stimation (.) framework, designed to address this gap. Our approach offers a novel
潜移默化
发表于 2025-3-24 08:45:13
,Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization,e learning. Causal domain generalization methods aim to identify latent causal variables that generate input data and build invariant causal mechanisms for prediction tasks, thereby improving out-of-distribution (OOD) prediction performance. However, there is no consensus on the best approach for se
Exposition
发表于 2025-3-24 11:52:02
,Rotary Position Embedding for Vision Transformer,RoPE on computer vision domains have been underexplored, even though RoPE appears capable of enhancing Vision Transformer (ViT) performance in a way similar to the language domain. This study provides a comprehensive analysis of RoPE when applied to ViTs, utilizing practical implementations of RoPE
placebo-effect
发表于 2025-3-24 15:01:21
http://reply.papertrans.cn/25/2424/242326/242326_18.png
grounded
发表于 2025-3-24 22:19:55
,MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection,l weather conditions, characterized by scenarios with clear and optimal visibility. However, the challenge of autonomous driving requires the ability to handle changes in weather conditions, such as foggy weather, not just clear weather. We introduce MonoWAD, a novel weather-robust monocular 3D obje
商店街
发表于 2025-3-24 23:33:43
http://reply.papertrans.cn/25/2424/242326/242326_20.png