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Jiao-Long Cao

Publications and source records attributed to Jiao-Long Cao.

3 recordsLinked to original sources

A Second-Logarithm Lower Bound for Sets with No Unique Sums

For an odd prime $p$, let $m(p)$ be the minimum cardinality of a set $A\subseteq \mathbb Z/p\mathbb Z$, with $|A|\geq2$, such that no sum in $A+A$ has a unique representation as an unordered pair from $A$, with repetition allowed. Bedert proved \[ m(p)\gg \log p\, \frac{\sqrt{\log^{(3)}p}}{\log^{(4)}p}. \] We prove the stronger lower bound \[ m(p)\gg \log p\,\log\log p. \] More generally, if $G$ is a finite Abelian group and $q(G)$ is the least prime divisor of $|G|$, then the same explicit estimate holds whenever $q(G)>2$, and in particular every subset $A\subseteq G$ with $|A|\geq2$ and no unique sum has cardinality $\gg \log q(G)\,\log\log q(G)$ as $q(G)\to\infty$. The proof has two structural inputs. First, a maximum subset of $A$ whose distinct-element subset sums of size at most four are all different has cardinality $\gg\log p$. This follows from a short-coordinate lemma and a collision-lattice determinant argument. Second, we refine Bedert's density increment. Alternative representations are oriented toward an uncovered endpoint, coalesced by their translation, and separated into wide, exposed, and recurrent batches. A load-sensitive entropy lemma codes the recurrent translations using their actual final fibre multiplicities. The resulting global shift-set complexity is $\exp(O(K))$, where $K$ is the ratio of $|A|$ to the level-four additive dimension. This forces $K\gg\log\log p$, and the theorem follows. All headline statements and the structural implications used to derive them have also been checked in Lean~4 with explicit integer constants. As a secondary and logically independent result, we construct weakly ternary-balanced sets and obtain \[ m(p)\leq \frac{(\log p)^2}{2(\log 3)^2} +\left(\frac{2}{\log 3}+o(1)\right) \frac{(\log p)^2}{\log\log p}. \]

math.CO

OmniSegmentor: A Flexible Multi-Modal Learning Framework for Semantic Segmentation

Recent research on representation learning has proved the merits of multi-modal clues for robust semantic segmentation. Nevertheless, a flexible pretrain-and-finetune pipeline for multiple visual modalities remains unexplored. In this paper, we propose a novel multi-modal learning framework, termed OmniSegmentor. It has two key innovations: 1) Based on ImageNet, we assemble a large-scale dataset for multi-modal pretraining, called ImageNeXt, which contains five popular visual modalities. 2) We provide an efficient pretraining manner to endow the model with the capacity to encode different modality information in the ImageNeXt. For the first time, we introduce a universal multi-modal pretraining framework that consistently amplifies the model's perceptual capabilities across various scenarios, regardless of the arbitrary combination of the involved modalities. Remarkably, our OmniSegmentor achieves new state-of-the-art records on a wide range of multi-modal semantic segmentation datasets, including NYU Depthv2, EventScape, MFNet, DeLiVER, SUNRGBD, and KITTI-360.

cs.CV

DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation

Recent advances in scene understanding benefit a lot from depth maps because of the 3D geometry information, especially in complex conditions (e.g., low light and overexposed). Existing approaches encode depth maps along with RGB images and perform feature fusion between them to enable more robust predictions. Taking into account that depth can be regarded as a geometry supplement for RGB images, a straightforward question arises: Do we really need to explicitly encode depth information with neural networks as done for RGB images? Based on this insight, in this paper, we investigate a new way to learn RGBD feature representations and present DFormerv2, a strong RGBD encoder that explicitly uses depth maps as geometry priors rather than encoding depth information with neural networks. Our goal is to extract the geometry clues from the depth and spatial distances among all the image patch tokens, which will then be used as geometry priors to allocate attention weights in self-attention. Extensive experiments demonstrate that DFormerv2 exhibits exceptional performance in various RGBD semantic segmentation benchmarks. Code is available at: https://github.com/VCIP-RGBD/DFormer.

cs.CV