增强
发表于 2025-3-25 06:08:58
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骗子
发表于 2025-3-25 10:04:41
,Few-Shot End-to-End Object Detection via Constantly Concentrated Encoding Across Heads,oposals to facilitate the adaptation at lower heads. Extensive experimental results show that our model brought clear gain on benchmarks. Detailed ablation studies are provided to justify the selection of each component.
人类
发表于 2025-3-25 12:22:22
,Implicit Neural Representations for Image Compression,rtion performance. Our contributions to source compression with INRs vastly outperform prior work. We show that our INR-based compression algorithm, meta-learning combined with SIREN and positional encodings, outperforms JPEG2000 and Rate-Distortion Autoencoders on Kodak with 2x reduced dimensionali
信条
发表于 2025-3-25 17:28:48
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蜈蚣
发表于 2025-3-25 23:18:09
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laceration
发表于 2025-3-26 02:30:44
,Learning Ego 3D Representation as Ray Tracing,presentation from 2D images without any depth supervision, and with the built-in geometry structure consistent . BEV. Despite its simplicity and versatility, extensive experiments on standard BEV visual tasks (., camera-based 3D object detection and BEV segmentation) show that our model outperforms
蚊子
发表于 2025-3-26 05:06:03
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GREG
发表于 2025-3-26 10:42:01
,Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning, covering three skeleton-based downstream tasks including action recognition, action detection, and motion prediction. Under both supervised and semi-supervised evaluation protocols, our method achieves the state-of-the-art performance. Additionally, we demonstrate that the prior knowledge learned b
linear
发表于 2025-3-26 13:19:34
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cuticle
发表于 2025-3-26 16:52:42
,Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning,ove the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.