← Search

Xinze Liu

3 accepted papers

2026

Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing

IJCAI 2026

Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient c

Cited by 0Scholar
2026

Discretization Is Not Always Better: Rethinking Deep Quantization for Asymmetric Image Retrieval

AAAI 2026technical

Asymmetric image retrieval (AIR), which typically employs a compact model for the query side and a large model for the database server, has garnered significant attention in resource-constrained environments. While deep hashing methods have shown great potential in large-scale image retrieval, curre

Cited by 0SourcePDFScholar
2026

DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving

AAAI 2026technical

The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant improvements, they still face notable limitations, including the lack of sequential

Cited by 0SourcePDFScholar