The paper introduces Privileged Appearance Transfer for Tracking (PATT), a teacher‑student framework that leverages exact target crops from past, current, and future frames during training to improve visual tracking. By weighting the teacher’s guidance with its localization advantage and accuracy, PATT transfers privileged appearance information to a deployable tracker that only uses past‑frame templates at inference. Experiments on seven benchmarks show consistent performance gains across both long‑ and short‑term tracking protocols.
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
arXiv:2606. 23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation.
TempoGround is a vision‑language model–native framework for streaming visual grounding that detects cross‑frame object correspondence and explicitly models object presence states. It uses a curriculum prediction mechanism to resolve 2D instance association, predict object entry, continuation, or exit, decode 2D boxes, and lift them to 3D camera‑frame boxes. The approach is further refined with Streaming Grounding Reinforcement, which optimizes grounding, identity, and consistency rewards, and achieves significant improvements on multiple streaming visual grounding benchmarks.
MAETrack introduces a lightweight framework to adapt pretrained masked autoencoder (MAE) representations for 3D single object tracking (SOT). It uses Layer‑Selective Initialization (LSI) to keep shallow geometric layers from the pre‑training while re‑initializing deeper layers, and Geometric Residual Gating (GRG) to emphasize salient regions in BEV features before template‑search fusion. Experiments on standard 3D SOT benchmarks show consistent improvements over vanilla fine‑tuning with minimal computational cost.
arXiv:2607. 01395v1 Announce Type: cross Abstract: At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world.
The paper introduces S$^3$T, a fully self‑contained framework for continuous video state tracking that uses temporal self‑distillation. It treats denser temporal sampling as privileged information, letting a dense‑view teacher guide a sparse‑view student to match its next‑token distribution without external labels or reward signals. Experiments on LLaVA-OneVision-2-8B show significant accuracy gains on VSTAT and MVBench benchmarks, and the learned capability transfers from synthetic to real videos.
arXiv:2608.29126v1 Announce Type: new Abstract: Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual...
arXiv:2606. 29357v1 Announce Type: cross Abstract: Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness.
arXiv:2608. 01263v1 Announce Type: new Abstract: On-policy distillation (OPD) samples trajectories from the current student policy and minimizes token-level divergence between student and teacher next-token distributions at prefixes along those trajectories.
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
arXiv:2609.10321v1 Announce Type: new Abstract: Knowledge distillation offers an efficient route to transfer a task-adapted vision-language teacher to a compact student. The training target in curren...
arXiv:2606. 10385v1 Announce Type: cross Abstract: On-policy distillation (OPD) has demonstrated strong empirical gains in enhancing complex reasoning in LLMs by aligning a student model with a teacher's predictive distribution over the student's own trajectories.