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arxiv: 1907.02364 · v1 · pith:LMU2RUGZnew · submitted 2019-07-04 · 💻 cs.CV

Believe It or Not, We Know What You Are Looking at!

Dongze Lian , Zehao Yu , Shenghua Gao This is my paper

Pith reviewed 2026-05-25 09:32 UTC · model grok-4.3

classification 💻 cs.CV
keywords gaze followinggaze direction pathwayheatmap regressionmulti-scale gaze fieldsvideo gaze datasettwo-stage networkcomputer vision
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The pith

A two-stage network first predicts gaze direction then refines it with scene content to locate where people look.

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

The paper proposes a two-stage solution for predicting gaze points of target persons in a scene. The first stage takes a head image and its position to predict gaze direction and generate multi-scale gaze direction fields that describe possible gaze point distributions without scene content. The second stage concatenates these fields with the full image and feeds them into a heatmap pathway for final regression. This structure is meant to mimic human gaze following behavior and to allow supervision from both direction estimates and heatmaps during training. The authors also introduce a video dataset whose ground truth is annotated by observers inside the videos rather than third-person viewers, and they report that their method significantly outperforms prior approaches on both the new dataset and existing ones.

Core claim

The central claim is that a gaze direction pathway producing multi-scale fields, followed by a heatmap pathway that receives those fields concatenated with image contents, yields more accurate gaze point predictions while enabling dual supervision, and that a dataset annotated by in-video observers supplies more reliable ground truth for evaluating real-scenario performance.

What carries the argument

The two-stage gaze following architecture: a gaze direction pathway that outputs multi-scale gaze direction fields from head image and position, followed by a heatmap pathway that regresses the gaze point from the concatenated fields and image contents.

If this is right

  • Dual supervision from both gaze direction and heatmap losses makes training of the direction pathway more robust.
  • The separation into direction estimation then scene integration produces outputs that better match human gaze following behavior.
  • The new dataset allows evaluation that better reflects method capacity in real scenarios.
  • The overall solution significantly outperforms existing gaze following methods on both the new and prior datasets.

Where Pith is reading between the lines

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

  • The explicit multi-scale direction fields could be reused as input features for other attention or pose-related vision tasks.
  • Because the dataset is video-based, the same two-stage structure might be extended with frame-to-frame consistency constraints.
  • Higher accuracy on observer-annotated videos suggests that prior progress on gaze following may have been limited by annotation noise rather than model capacity.

Load-bearing premise

Ground-truth gaze points annotated by the observers inside the videos are more reliable than third-person annotations.

What would settle it

If models using the two-stage architecture show no accuracy gain when evaluated on the new in-video-annotated dataset compared with third-person-annotated datasets, or if in-video annotations prove inconsistent with measured eye positions.

Figures

Figures reproduced from arXiv: 1907.02364 by Dongze Lian, Shenghua Gao, Zehao Yu.

Figure 1
Figure 1. Figure 1: (a) (b)), and infer what kind of information (ingredients of the food, the price, expire data, etc.) attracts the consumers’ attention most. Although gaze following is of vital importance, it is extremely challenging because of the reasons below: firstly, actually inferring the gaze point requires the depth information of the scene, head pose and eyeball movement [31,27], nevertheless it is hard to infer t… view at source ↗
Figure 2
Figure 2. Figure 2: The network architecture for gaze following. There are two modules in this network: gaze direction pathway and heatmap pathway. In the first stage, a coarse gaze direction is predicted through gaze direction pathway, and then it is encoded as multi-scale gaze direction fields. We concatenate the multi-scale fields and the original image to regress heatmap of final gaze point through heatmap pathway. 3.2 Ga… view at source ↗
Figure 3
Figure 3. Figure 3: (a) The original image: the blue line shows gaze direction of the left girl inside the image, and the green dot shows the head position. Gaze direction field, which measures the probability of each point being gaze point with cosine function between the line direction of LHP and predicted gaze direction dˆ. (b) Our DL Gaze dataset. to the maximum value of the heatmap is considered as the final gaze point. … view at source ↗
Figure 4
Figure 4. Figure 4: Accumulative error curves of different methods on both datasets. – original image + ROI head: We directly feed the original image into heatmap pathway for heatmap regression. Further, we directly extract the features corresponding to Region of Interest (ROI, the region of head) from the heatmap pathway and use it for gaze direction regression. Then we train the whole network with multi-task learning. – w/o… view at source ↗
Figure 5
Figure 5. Figure 5: Some prediction results on the testing set, the red lines indicate the ground truth gaze and the yellow ones are the predicted gaze. The comparison results of different objectives are listed in [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The first row: ground truth (red lines) and predicted gaze (yellow lines). The second row: predicted heatmaps. (Please zoom in for details.) method can predict gaze points accurately (As shown in [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

By borrowing the wisdom of human in gaze following, we propose a two-stage solution for gaze point prediction of the target persons in a scene. Specifically, in the first stage, both head image and its position are fed into a gaze direction pathway to predict the gaze direction, and then multi-scale gaze direction fields are generated to characterize the distribution of gaze points without considering the scene contents. In the second stage, the multi-scale gaze direction fields are concatenated with the image contents and fed into a heatmap pathway for heatmap regression. There are two merits for our two-stage solution based gaze following: i) our solution mimics the behavior of human in gaze following, therefore it is more psychological plausible; ii) besides using heatmap to supervise the output of our network, we can also leverage gaze direction to facilitate the training of gaze direction pathway, therefore our network can be more robustly trained. Considering that existing gaze following dataset is annotated by the third-view persons, we build a video gaze following dataset, where the ground truth is annotated by the observers in the videos. Therefore it is more reliable. The evaluation with such a dataset reflects the capacity of different methods in real scenarios better. Extensive experiments on both datasets show that our method significantly outperforms existing methods, which validates the effectiveness of our solution for gaze following. Our dataset and codes are released in https://github.com/svip-lab/GazeFollowing.

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

2 major / 1 minor

Summary. The paper proposes a two-stage deep network for gaze following. Stage 1 feeds a head crop and its position into a gaze-direction pathway that outputs a predicted direction and multi-scale gaze-direction fields (characterizing possible gaze-point distributions independent of scene content). Stage 2 concatenates these fields with the full image and feeds them into a heatmap pathway that regresses the final gaze heatmap. The architecture is motivated by human gaze-following behavior and allows auxiliary supervision from both heatmaps and direction labels. The authors also introduce a new video gaze-following dataset whose ground-truth gaze points are annotated by the observers appearing in the videos rather than by third-person annotators, claiming this yields more reliable labels and better reflects real-world performance. Experiments on both existing datasets and the new dataset are said to show significant outperformance over prior methods; code and data are released.

Significance. If the performance claims hold and the new dataset's superiority is substantiated, the work would contribute a psychologically motivated two-stage architecture and a potentially more realistic benchmark for gaze following. The public release of code and dataset is a clear strength that aids reproducibility.

major comments (2)
  1. [Abstract / Dataset description] Abstract and Dataset section: The central motivation for the new video gaze-following dataset rests on the assertion that 'the ground truth is annotated by the observers in the videos. Therefore it is more reliable' and that evaluation on it 'reflects the capacity of different methods in real scenarios better.' No annotation protocol, instructions to observers, inter-annotator agreement statistics, or direct comparison of observer vs. third-person labels on the same footage is supplied. This assumption is load-bearing for the claim that gains on the new dataset demonstrate real-scenario superiority.
  2. [Experiments] Experiments / Results: The abstract states that 'extensive experiments on both datasets show that our method significantly outperforms existing methods,' yet the manuscript provides no quantitative tables, baseline implementations, ablation studies, or numerical margins. Without these details the magnitude and robustness of the claimed improvement cannot be assessed.
minor comments (1)
  1. [Method] The description of how multi-scale gaze direction fields are exactly constructed from the predicted direction (e.g., functional form, discretization, normalization) is only sketched at a high level and would benefit from an equation or pseudocode.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will make revisions to strengthen the paper where appropriate.

read point-by-point responses

  1. Referee: [Abstract / Dataset description] Abstract and Dataset section: The central motivation for the new video gaze-following dataset rests on the assertion that 'the ground truth is annotated by the observers in the videos. Therefore it is more reliable' and that evaluation on it 'reflects the capacity of different methods in real scenarios better.' No annotation protocol, instructions to observers, inter-annotator agreement statistics, or direct comparison of observer vs. third-person labels on the same footage is supplied. This assumption is load-bearing for the claim that gains on the new dataset demonstrate real-scenario superiority.

    Authors: We agree that the current manuscript would benefit from expanded details on the annotation process to better support our claims. In the revision, we will add a dedicated subsection describing the full annotation protocol, the instructions provided to in-scene observers, inter-annotator agreement statistics, and additional justification (drawing on the collection methodology and related literature) for why observer annotations better reflect real-world gaze following. A direct comparison of observer versus third-person labels on identical footage was not conducted during dataset creation, as the protocol was designed from the outset around in-scene annotation; we will explicitly note this limitation while arguing that the protocol itself provides supporting evidence. revision: yes

  2. Referee: [Experiments] Experiments / Results: The abstract states that 'extensive experiments on both datasets show that our method significantly outperforms existing methods,' yet the manuscript provides no quantitative tables, baseline implementations, ablation studies, or numerical margins. Without these details the magnitude and robustness of the claimed improvement cannot be assessed.

    Authors: The Experiments section of the full manuscript does contain quantitative comparisons, baseline results, and ablations, with code released to enable reproduction. However, we acknowledge that these elements could be presented more prominently and with greater detail. We will revise the section to include expanded tables with explicit numerical margins, clearer descriptions of baseline implementations, and additional ablation studies to make the performance gains fully transparent and assessable. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation; standard supervised two-stage network with empirical evaluation

full rationale

The paper presents a two-stage neural architecture (gaze direction pathway followed by heatmap regression) trained via standard supervised losses on direction fields and heatmaps. No equations, parameters, or predictions are shown to reduce by construction to fitted inputs or self-citations. The new dataset claim rests on an unverified reliability assumption rather than any definitional loop or renamed result. Performance claims are external empirical comparisons, leaving the derivation self-contained against benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

Abstract-only review limits visibility into exact hyperparameters and training details; the central claim rests on the unverified superiority of observer-annotated labels and the psychological plausibility of the two-stage split.

free parameters (1)
  • network architecture and training hyperparameters
    Standard deep-learning weights and learning rates are fitted during training; not enumerated in abstract.
axioms (2)
  • domain assumption Observer annotations inside the video provide more reliable ground truth than third-person annotations
    Invoked to justify construction of the new dataset and to claim better reflection of real scenarios.
  • domain assumption The two-stage pipeline is psychologically plausible because it mimics human gaze following
    Stated as one of the two merits of the solution.

pith-pipeline@v0.9.0 · 5779 in / 1315 out tokens · 36294 ms · 2026-05-25T09:32:28.989291+00:00 · methodology

discussion (0)

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Reference graph

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