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

• URL: http://arxiv.org/abs/2608.24010v1
• Date: Tue, 25 Aug 2026 03:00:07 GMT
• Title: Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing
• Abstract summary: Kent-based Distributional Proxy Hashing (KDPH) is a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere.<n>To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing.
• Score: 35.37008246669991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 conflicts arising from label co-occurrence lead to severe gradient contention and optimization collapse. To resolve this, we propose Kent-based Distributional Proxy Hashing (KDPH), a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere. Unlike point proxies that must shift their positions to accommodate conflicting gradients, KDPH absorbs these conflicts by dynamically adjusting its directional variance. This allows the proxy to maintain a stable semantic mean direction while stretching to cover diverse label correlations. Furthermore, to ensure stable training of these geometric parameters, we derive a tailored loss function incorporating the Cayley transform to enforce strict orthogonality. To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing. Experiments on three benchmark datasets demonstrate that KDPH mitigates proxy collapse and chaotic oscillation, significantly outperforms state-of-the-art methods. Code is available at https://github.com/Senmo996/KDPH-official-code.

Related papers

• Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere [11.087393998848254]
  In Self-Supervised Learning (SSL), preventing representation collapse by explicitly enforcing a uniform distribution on the unit hypersphere has proven to be effective.<n>Current frameworks typically rely on sliced statistical regularizers such as SIGReg (used in LeJEPA) and SUSReg (used in SPHEREJEPA)<n>This injects projection variance into the training gradients, destabilizing optimization, and hindering convergence.<n>In this work, we first show that analytically integrating out these random projections yields a deterministic Maximum Mean Discrepancy (MMD), bypassing the variance of sliced methods.
  arXiv  Detail & Related papers  (2026-06-16T07:10:05Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging [65.11234199720428]
  Continual Test-Time Adaptation aims to empower perception systems to handle dynamic distribution shifts encountered after deployment.<n>We introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging.<n>During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism.
  arXiv  Detail & Related papers  (2026-05-18T16:18:17Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies [22.609134964894363]
  We introduce latent equivalent classes (LECs) that induce the same conditional proxy distribution.<n>LECs are defined as groups of latent confounders that induce the same conditional proxy distribution.<n>We show that point-identification for the robust predictor remains achievable as long as multiple domains differ sufficiently in how they mix proxy-induced LECs to form the robust predictor.
  arXiv  Detail & Related papers  (2026-03-16T11:52:35Z)
• BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning [49.25750348525603]
  BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions into dynamic, probability-aware clipping intervals.<n>We show that BandPO consistently outperforms canonical clipping and Clip-Higher, while robustly mitigating entropy collapse.
  arXiv  Detail & Related papers  (2026-03-05T08:03:05Z)
• A Statistical Side-Channel Risk Model for Timing Variability in Lattice-Based Post-Quantum Cryptography [0.0]
  Timing side-channels are an important threat to cryptography that still needs to be addressed in implementations.<n> lattice-based schemes may produce secret-dependent timing variability with the help of complex arithmetic and control flow.<n>A scenario-based statistical risk model is proposed for timing leakage as a problem of distributional distinguishability under controlled execution conditions.
  arXiv  Detail & Related papers  (2025-12-26T03:12:33Z)
• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
  SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
  arXiv  Detail & Related papers  (2025-11-03T11:24:06Z)
• Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation [27.518542543750367]
  This paper aims at addressing conflicting issues in federated learning: differential privacy and Byzantinerobustness. Standard mechanisms add transmitted DP, envelops entangles with robust gradient aggregation to defend against Byzantine attacks. We show that the influence of our proposed mechanisms is deperturbed with that robust model aggregation.
  arXiv  Detail & Related papers  (2022-04-29T23:37:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.