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arxiv: 2607.02497 · v1 · pith:T66TNPCYnew · submitted 2026-07-02 · 💻 cs.CV

Seek to Segment: Active Perception for Panoramic Referring Segmentation

Song Tang , Shuming Hu , Xincheng Shuai , Henghui Ding , Yu-Gang Jiang This is my paper

Pith reviewed 2026-07-03 14:37 UTC · model grok-4.3

classification 💻 cs.CV
keywords active perceptionpanoramic segmentationreferring segmentationembodied AIspatial memoryvision-language modelsearch trajectoriesreinforcement learning
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The pith

PanoSeeker integrates local views into a unified 360° memory to let an agent search for and segment a referred object without redundant scanning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the task of active panoramic referring segmentation, where an embodied agent must adjust its viewpoint in a full 360° setting to locate and mask an object named in natural language. It proposes PanoSeeker, which pairs a vision-language model with an explicit spatial memory that accumulates observations over time. This memory replaces heuristic scanning with planned trajectories that avoid repeated looks at the same region. Once the target appears, the agent aligns its view and produces the segmentation mask. Experiments on a new benchmark show the approach yields shorter search paths and higher accuracy than adapted passive baselines.

Core claim

PanoSeeker maintains an explicit spatial visual memory called EgoSphere that folds successive narrow-field observations into one consistent 360° representation; the vision-language model then reads this memory to select the next viewpoint adjustment, continuing until the referred object is visible, at which point the agent performs final alignment and outputs the mask. The system is first fine-tuned on expert search trajectories and then refined with reinforcement learning that rewards shorter, non-redundant paths.

What carries the argument

EgoSphere, an explicit spatial visual memory that progressively integrates sequential local observations into a unified 360° representation.

If this is right

  • Referring segmentation can be performed by an agent that must move rather than by a model that receives a single fixed image.
  • Explicit memory of the full panorama removes the need for exhaustive scanning patterns.
  • Reinforcement learning after supervised fine-tuning directly reduces the number of viewpoint changes required.
  • Active viewpoint alignment at the end of search improves mask quality over masks taken from arbitrary angles.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same memory structure could be reused for other embodied tasks that require stitching partial views, such as object rearrangement or navigation to a described landmark.
  • If EgoSphere can be updated in real time on a physical robot, the method would transfer from simulation to hardware without retraining the memory module.
  • The approach suggests that panoramic referring segmentation is best solved by separating the search phase (memory-driven) from the final segmentation phase (view-aligned).

Load-bearing premise

That accumulating local views into one shared 360° memory is sufficient for the agent to choose non-redundant next directions without losing track of the target.

What would settle it

On the APRS benchmark, a version of the agent that discards EgoSphere and uses only the current view plus random or heuristic moves reaches the target in the same or fewer steps than the full PanoSeeker.

read the original abstract

Existing referring segmentation models passively process static images captured from fixed perspectives, limiting their applicability in Embodied AI, where agents must perform active perception in the continuous 360$^\circ$ environments. To bridge this gap, we introduce a novel task: Active Panoramic Referring Segmentation (APRS). In this setting, an agent is required to adjust its viewing direction ($\Delta\theta, \Delta\phi$) to explore the 360$^\circ$ environment, seeking the object specified by a user instruction for segmentation. To tackle this challenging task, we propose PanoSeeker, a memory-augmented agent for efficient APRS. Rather than relying on heuristic scanning, PanoSeeker integrates a Vision-Language Model (VLM) with EgoSphere, an explicit spatial visual memory. By progressively integrating sequential local observations into a unified 360$^\circ$ representation, EgoSphere enables the agent to plan efficient and non-redundant search trajectories. Once the target is found, the agent performs active viewpoint alignment and outputs the segmentation mask. Furthermore, we curate an expert-annotated search trajectory dataset with memory timelines for Supervised Fine-Tuning, followed by Reinforcement Learning post-training to explicitly optimize PanoSeeker's exploration efficiency. Extensive experiments on our newly established APRS benchmark demonstrate that PanoSeeker achieves superior search efficiency and segmentation accuracy, significantly outperforming adapted state-of-the-art baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper introduces the task of Active Panoramic Referring Segmentation (APRS), requiring an agent to actively adjust viewing directions (Δ heta, Δϕ) in a 360° environment to locate and segment a referred object. It proposes PanoSeeker, which augments a Vision-Language Model with EgoSphere (an explicit spatial visual memory) to integrate sequential local observations into a unified 360° representation for planning efficient search trajectories. The pipeline uses supervised fine-tuning on an expert-annotated trajectory dataset with memory timelines, followed by reinforcement learning to optimize exploration efficiency. On a newly curated APRS benchmark, PanoSeeker is reported to outperform adapted state-of-the-art baselines in both search efficiency and segmentation accuracy.

Significance. If the empirical claims hold, the work addresses a clear gap between passive referring segmentation and active embodied perception in panoramic settings. The explicit EgoSphere memory and the SFT-then-RL training regimen constitute a concrete, reproducible pipeline for non-redundant exploration. The release of an expert-trajectory benchmark with memory timelines is a tangible community resource that could support further research in embodied vision-language tasks.

minor comments (1)
  1. The abstract states that 'extensive experiments' demonstrate superior performance, yet provides no quantitative metrics, dataset statistics, ablation results, or descriptions of how baselines were adapted; the full experimental section is required to evaluate these claims.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of our work on Active Panoramic Referring Segmentation and for recognizing the significance of the APRS task, EgoSphere memory, and the expert-trajectory benchmark. The recommendation of 'uncertain' is noted, but the report lists no specific major comments to address. We provide a point-by-point structure below for completeness; if additional comments were intended, we would be glad to respond in a revision.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a new task (APRS) and proposes PanoSeeker as a VLM + EgoSphere pipeline trained via SFT on expert trajectories followed by RL, with empirical claims of superior efficiency and accuracy on a newly curated benchmark. No equations, derivations, or load-bearing steps are present that reduce by construction to fitted inputs, self-definitions, or self-citation chains. The method description and performance claims are self-contained empirical contributions without the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities beyond the high-level description of EgoSphere are stated.

invented entities (1)
  • EgoSphere no independent evidence
    purpose: explicit spatial visual memory that merges sequential local observations into a unified 360° representation
    Introduced as the core memory component of PanoSeeker; no independent evidence provided in abstract.

pith-pipeline@v0.9.1-grok · 5791 in / 1123 out tokens · 24365 ms · 2026-07-03T14:37:36.899255+00:00 · methodology

discussion (0)

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