What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
Authors: Dongheng Lin
Han Hu
Jianbo Jiao
Venue: Accepted by TMLR ()

Abstract: Time becomes visible through illumination changes in what we see. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: *what time tells us?* To this end, we first introduce a Time-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset, we propose a Time-Image Contrastive Learning (TICL) approach to jointly model timestamps and related visual representations through cross-modal contrastive learning. 
We found that the proposed TICL, 1) not only achieves state-of-the-art performance on the timestamp estimation task, over various benchmark metrics, 2) but also, interestingly, though only seeing static images, the time-aware embeddings learned from TICL show strong capability in several time-aware downstream tasks such as time-based image retrieval, video scene classification, and time-aware image editing. Our findings suggest that time-related visual cues can be learned from static images and are beneficial for various vision tasks, laying a foundation for future research on understanding time-related visual context. Project page: https://rathgrith.github.io/timetells_release/

Published in Transactions on Machine Learning Research (09/2025)
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
Dongheng Lin"
d. ..in....hham.... uk
The MIx Group, University of Birmingham
Han Hu*
hzh347 (0td7//ttttdnnbbiee www.rntt..hamaa. uk
The MIr Group, University of Birmingham
Jianbo Jiao
j. j.Jao@bhamm.mco uk
The MIz Group, University of Birmingham
Reviewed on OpenReview: https: // opw.revieeeeeee....ommo net/ forum? id- f1MY0G44DD
Abstract
Time becomes visible through illumination changes in what we sec. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: what time tells us? To this end, we first introduce a Fime-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset, we propose a Time-Image Contrastive Learning (TICL) approach jointly model timestamps and related visual representations through cross-modal contrastive learning. We found that the proposed TICL, 1) not only achieves srate-of-the-art performance the timestamp estimation task, over various benchmark metrics, 2) but also, interestingly, though only seeing static images, the time-aware embeddings learned from TICL show strong capability in several time-aware downstream tasks such as time-based image retrieval, video scene classification, and time-aware image editing. Our findings suggest that time-related visual cues can be learned from static images and are beneficial for various vision tasks, laying foundation for future research understanding time-related visual context. Project page: httpp::/ratthre github. io/timetells_reteeeeeeeeeeeeeeee
"Time is the moving image of eternity."
Plato
1 Introduction
On our planet, the day-night cycle occurs every 24 hours, phenomenon recorded systematically by various clock systems developed by human society. Surprisingly, such clock systems emerged much earlier than our recognition of Earth as a "blue marble" engaged in constant orbital movement (Dohrn-van Rossum 1996). Although most people possess a vague, intuitive sense of current time (Moorc) 1992). the origin of this metaphysical consciousness of time, which is key concept. for both our bodies and sociely, remains elusive. Research in neuroscience has revealed that visual stimuli from photoreceptors are crucial for the adaptation of mammals day-night rhythms (Duffy & Czeisler 2009). This implies that the concept of time for humankind could emerge from various visual experiences. Given the implicit relations between clock time and visual experiences, are interested in asking: 1) Can neural networks gain similar awareness to clock time from solely visual stimuli i.e. static images? 2) If so, what implications does such time awareness tell us lowards underslanding the world?
"These authors contributed equally.

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Published in Transactions on Machine Learning Research (09/2025)
params: 3.7M
12
TICL (Ours) (2022)
Acc: 20.6%
11
*
et (2019)
Ne:-110 i2hwng el al., 2022)
10
2
8/28/32 (Zhang 20221
Sameen home Pribaillyy 290
16:00
00:00
20:00
12:00
17%
08:00
04:00
params:
Acc: 14.11%
Acc: 13.87%
Acc: 13.24%
params: 92.5M
Timestamp Embeddings
Acc: 10.73%
Time-aware Image Embeddings
0.0M
Hypothetical Day-Night Cycle
Parameters
(a) TOC dataset
(b) Time-estimation performance
(c) Expected time aware embedding space
Figure 1: An overview of our study, in which presented new high-quality dataset for time-of-day estimation (a); based which we propose new approach, achieving state-of-the-art performance (b). We further explore the implications of learned time-aware embeddings (c), showing effectiveness over several time-related downstream tasks.
To answer these questions, in this study, we propose an approach to learn and disentangle the visual cues related to time from static images, via a pre-text task estimating the clock timestamps from images, and exploration on various downstream tasks to find their visual implications.
Learning to model captured timestamps of images requires a reliable natural image dataset with timestamps. There are previous surveillance camera datasets with fixed views, such the Time of Year Dataset (TYD) (Volokitin et al.] 2016) and other subsets of the Archive of Many Outdoor Scenes (AMOS) (Jacobs et al. 2009), featuring images captured by a few stationary webcams at different times of the day. However, these datasets do not reflect the complexity and diversity of views in real-world applications. To address this issue; (Salem et al. (2020) proposed a mixed subset of AMOS and YFCC100M (Thomee et al.) 2016),, containing diverse samples. However, many images in this dataset suffer from incorrect timestamps due to unsynchronised time zones (Padilha et al. 2022 which undermines its reliability for learning robust time-awareness.
In addition to the challenge of lacking reliable datasets, designing effective solutions for the pre-text task also faces significant difficulty. Providing accurate clock time estimates requires the model to go beyond understanding basic illumination, as the task is complicated by inherent ambiguitics between the clock timestamp and images. These ambiguities arise because daylight time is influenced by additional metadata, such regional climate and seasonal variations that affect the duration of daylight hours (Volokitin et al., 2016 Sharma et al. 2016 Zhang al., 2022). To cope with this issue, [Salem et al. (2022) ; Zhai et al. (2019) introduced additional metadata inputs, aiming to model the joint conditional probabilities between geolocation, hour and date to provide performance improvements to the estimation task. While these works made reasonable and valuable improvements, they have introduced extra dependencies on additional metadata, limiting the generalisation ability when such metadata is unavailable as reported. On the other hand, they primarily focus on the specific task of clock time imation, without exploring further implications of time to other applications. Whereas in this work, in addition to estimating more accurate timestamps, we further utilise the learned time and time-aware image features to investigate their impact on several other downstream tasks.
Specifically, due to the lack of high-quality data, we first curate a new benchmark dataset comprising social media images featuring diverse views and objects, along with manually verified reliable timestamps. Such a dataset has the potential to become the new de facto choice for future research. Secondly, we propose a Time-Image Contrastive Learning (TICL.) approach that extracts timo-of-day awareness from rich semantics from foundation vasion-language model via contrastive learning outperforms all existing methods on the pre-text timestamp estimation task. Moreover, we conduct explorations of utilising such time-awareness
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Published in Transactions on Machine Learning Research (09/2025)
several downstream tasks, including time-based image retrieval, video scene recognition, and time-aware visual editing, showing the indirect relations between time and scene understanding.
Note that this work is not aiming at purely time estimation, but more about an exploration of what the learned embedding tells us, through such a pre-text task. Our key contributions can be summarised as follows:
We introduce Time-Orientee Collection (TOC), a new benchmark dataset containing 130,906 images with reliable timestamps (examples shown Fig. lab
We propose TICL, an approach jointly modelling time and related visual representations, achieving statc-of-the-art (SOTA) performance on timestamps cstimation from static images. Fig. Tb shows the achieved performance, boosting SOTA from 14.11% to 20.6%, while keeping small number of trainable parameters.
We study the potential of the learned time-aware visual embeddings (Fig. lc by validating them on several downstream tasks (e.g. time-based image retrieval, video scene classification, and time-aware image editing), showing clear evidence of their effectiveness.
2 Related Works
2.1 Image datasets with timestamps
Estimating the time of day from static images is notable and underexplored challenge. Earlier studies were hampered by the scarcity of datasets with images paired with accurate local limestamps. Many images from social networks often have metadata that is inacourate, missing, or uncalibrated to local timezones. To cope with this, some rescarchers have turned webcam image datasets, which naturally include accurate timestamps. However, these datasets are limited to fixed views and are often degraded by noise, low light, obstructions, hindering their generalisation to diverse applications.
For example, established social media image datasets such MIRFLICKR-IM (Hisses & Lew 2008 and YFCC100M (Thomee et al. 2016) were found to contain many unnatural non-photographic images (e.g.
memes, scribbles) and inaccurate timestamps due to unsynchronised clocks and ot sources of inconsistencey
On the other hand, webcam datasets contain only fixed stationary views, such as AMOS (Jacobs et al., 2007) and TYD dataset (Volokitin et al.) 2016), which fail to represent the complexities of temporal variations within diverse environments. The CVT-Time dataset (Salem et al.) 2020),, despite combining stationary webcam images with YFCC100M subsets with images captured by smartphone, still struggles with unreliable timestamps and low-quality webcam images.
2.2 Clock timestamp estimation
Previous work have studied joint attribute estimation of images, including captured clock time, date, and geolocation. In this work, we focus solely clock time eslimation regardless of other fields in limestamps (e.g., date, year), since we are primarily interested in relations between clock time itself originated from human activities (Moore 1992) to visual cues. Volokitin et al. (2016) used VGG-16 to classify tempera- ture, month, and hour intervals from images taken by 6 webcams during daylight, which insufficient for comprehensive day-long analysis. In addition to such carlier simple approaches, /Zhai al. (2019) worked with a mixed dataset of Flickr and webcam images, classifying images taken at the same hour but in dif- ferent months into 288 classes, optionally incorporating geolocation inputs. Similarly, (Salem et al. (2022) used webcam images, predicting month, week, and hour dependent tasks trained jointly while consid- cring geolocations as optional inputs. Such joint predictions improve hour-based timo-of-day classification by leveraging meladata cues on regions and climate, which correlate with daylight length. However, such dependency also puts risks on generalization ability when there are no reliable geolocation or date metadata available (Salem et al. 2020 Zhai et al.) 2019). Therefore, we deliberately chose use only input images for clock time prediction, without utilising any additional metadata with regard to the generalization problem acknowledged in previous work.
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3 Time- Contrastive Learning
Before introducing the proposed method, we revisit the problem formulation and goal of our study. Unlike previous pwwestampppredicttonn architectures that simply attach classifier to an image backbone (Salem) 2020; Zhai et al.) 2019 Volokitin et al. 2016), our goal is not to engineer another bespoke head. Instead, we introduce minimal, generic recipe that transforms a certain environmental attribute (in our case, the clock time) into embedddings that elucidates the implications of world understanding from the attribute. The novelty of TICL therefore lies in providing explicit semantic-aware time embeddings for further investigations with its robustness on time encoding validated by superior pre-text time estimation task performance.
ct al.
For the clock-time estimation of images specifically, we seek to train model fo()), which predicts timestamp given input image The estimate can be written as i= fa()). While regression seemss ideal owing to the continuous nature of time, it faces significant challenges. The cyclic nature of the clock introduces discontinuity to regression methods that treat target values as scalars within a range that is a disconnected set (Zhou et al. 2019). In regression, cyclic data often causes to cluster near the midpoint of the range (Adams & Vamplew 1998). For instance, timestamps like 23:59 and 00:00, despite their visual similarity, are treated as opposite extremes on the time scale. In such cases, the regression model tends to reach a sub-optimal solution around 12:00, which is far from accurate. Apart from this extremal case, the scnsitivity of the scalar time values also encourages the model to predict an average ground-truth value across visually similar images. Encoding time into cyclic space partially mitigates scalar discontinuities (Adams & Vamplew 1998 Kazemi et al. 2019: but sensitivity issues still limit performance (sce detailed analysis in Appendix C
This justified why prior studies have employed classification over discrete time periods (e.g. hours); in which has finite value options corresponding to classes. Classification mitigates the above issue in regression by simplifying the model to give a coarser estimate from mutually orthogonal discrete classes. Even for boundary cases like 23:59 and 00:00, the classification model tends to predict one of the adjacent classes (e.g. 23:00 or 00:00), which is more reasonable. However, the orthogonality of one-hot vectors (Rodriguez et al. 2018 overlooks the relationships (partial order, cyclic) between time periods.
On top of these observations, we propose Time-Image Contrastive Learning (TICL), a multi-modal approach that jointly learns time and image representations via cross-modal contrastive learning, inspired by GeoCLIP (Vivanco et al., 2023). Each imput image z; is associated with a label f; E RC indicating its time period. Empirically, we fix C= 24 for all the results in the main paper for a fair comparison with previous work (see further discussions on the choice of C' in Appendix C.2 Each one-hot vector t; is projected into a high-dimensional representation space R.K using a Time Encoder T; for(.) where K = 768 to match the dimensionality of the image representation.
As visualised in Fig. 2 during training we maximise the cosine similarity between the CLIP image feature 4, = (x:) and its corresponding time-class embodding T, = for (t,). Here, fa, (-) denotes the combined operation of the frozen CLIP image encoder and an Image- Time Adaptor (TTA). The alignment is optimised by minimising the contrastive loss (He et al. 2019), ; as defined in Eq- 1 where is learnable temperature that controls the sharpness of the softmax distribution (Wu et al. 2018 At inference, TICL flexibly supports both classification at any class granularity and nearest-neighbout inference (see Appendix B.1 Appendix C.2
and A Appendix B.2
LA = log exp(1, T3/T)) 1= E exp(L: T//T)
B-1 Σ
(1)
Several key intuitions support this design. Previous work has shown that combining additional geolocation and date information can improve the performance of time estimation, but reliance on such attributes propagates errors from prior to posterior predictions (Salem et al.) 2020). We observed that the CLIP image encoder is a powerful foundation model capturing rich semantic context, exhibiting strong zcro-shot capabilities in geo-localisation and scene recognition (Radford et al. 2021 Agarwal et al., [0021] Vivanco et al.] 2023 These results suggest that CLIP implicitly encodes cues (e.g. climate, region) relevant. to
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Figure 2: Overview of TICL. Given static images and one-hot time labels, two encoders (Timm Encoder and image encoder + ITA) project inputs into a shared feature space; contrastive loss aligns the corresponding pairs.
Table 1: Classification accuracy on the TOC Lest set of the baseline model Salem el al. 2022) when using different training datasets.
Training Dataset t
Top-1 f
Top-3 f
Top-5
Original Salem et al. 2022
12.02%
34.05%
56.45%
Cleaned TOC (ours)
13.87%
39.36%
60.71%
Ia training datasels, overlapped samples from the lest sel are excluded. The new dataset details are introduced Section and Appendix A
timestamp estimation. Therefore, we freeze the CLIP backbone and extract these cues directly, rather than ingesting raw geolocations or season inputs that may themselves be noisy (Salem et al. 2022)
Another benefit comes from the Icarnable time embedding the contrastive scheme. In a vanilla classifier, the final output ÿ confined to the simplex [|00] 1, E RC], where each target is a fixed one-hot vector. Samples optimised only toward their target, and activations to related classes may be suppressed (He & Garcia 2009). In contrast, method endows each time class with trainable vector that absorbs shared information among temporally adjacent samples, aligning timestamps and visual inputs more effectively especially for tail classes. These learnable embeddings also prove useful in downstream tasks (Section
In summary, we expoct TICL combines the benefit from the orthogonality onc-hot encoded labels and flexibility of learnable high-dimensional embeddings. This simple yet. principled design delivers consistent gains over prior estimators and alternative time-encoding schemes (Rahimi & Recht 2007 Kazemi et al.
2019 Salem et al., 2022 Zhai et al.) 2019) (see Sections 5.2 and 5.3). while remaining computationally lightweight and conceptually generalisable.
4 Benchmark Dataset TOC
With regards problems Scction 2.1 we introduce new benchmark Time- Oriented Collection (TOC) dataset consisting of high-quality images sourced from social media, featuring reliable image metadata. We collected 117,815 training samples and 13,091 test samples from the Cross- View Time (CVT) (Salem et al.
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2020,, mitigating various limitations in previous datasets. This dataset reflects real-world scenarios and human activities, making time-of-day estimation more applicable to potential practical applications.
During dataset curation, we manually filtered out unnatural, non-photographic images from the CVT dataset and calibrated the timestamps to match the images. To accelerate the process, we utilized ResNet18 features of the images to quickly identify the outliers deep image feature space for different periods of the day. After which, we conducted meticulous manual inspection for each outlier image check their timestamp contents natural and valid (sce more details in Appendix A). This revised dataset reflects natural variations in human activity throughout the day, with improved reliability in terms of time metadata. As evidence, Table 1 shows performance gap the same test set using different levels of filtering on the CVT dataset, justifying the effectiveness of the filtering process indicated by improvements in baseline model performance, suggesting that repetitive wwweeillance-ccmrra-oooooo images we removed do not help the model in better time recognition for images in the wild. A few examples of the exact format and appearances of the remaining samples within the TOC dataset are provided in Fig. 3 Geolocation distribution of the sample images within our final TOC dataset. in Fig, also suggests that, due to the inherent geographic distribution of the internet, the northern hemisphere has more data captured by nature. Our dataset well represents such natural distribution. Further information and statistics about the dataset are available in Appendix A
and unnatural non non-photographic
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Figure 3: Sample images and metadata from the TOC dataset w.r.t. GPS coordinates. Metadata contains several fields indicating timestamps and geolocations. The samples spread across all the continents and show a natural distribution of internet images, where the southern hemisphere has relatively fewer samples due a sparser population of photo capturing.
5
Experiments
5.1 Experiment Setting
Dataset and metrics: We use different evaluation metrics to mcasure performance image clock time estimation tasks: top-k classification accuracy with & 1,3,5, and Time Mean Absolute Error (MAR) a minute basis. In addition to the TOC test set, to better evaluate the generalisation ability of the proposed method, we selected & subset of the AMOS dataset (Jacobs et al. 2007) as an additional test set. This additional test set contains 3,556 images with high SNR (which ensures good sample quality) captured by stationary surveillance cameras with a more balanced time label distribution (sce curation process and statistics in Appendix A.2). That is, all the compared models are trained solely on the TOC training set and evaluated on different test sets demonstrate generalisation ability across different. domains.
Implementation dctails: For our proposed TICL, we use Adam optimiser with an initial learning rate of 5 x 10-4 and a weight decay of 1 x 10-6 The training process spans 20 epochs, with the learning rate halved every 2 epochs and a batch size of 512. The temperature parameter is initialized to 0.07. All input images are resized to 224 224. For a fair comparison, we retrained all the previous bascline methods on the cleaned TOC train set, using the best training configurations reported in Zhang al. (2022): Zhai et al. (2019) available in Appendix B.1
Salem al. 2022 respectively. Additional details about implementat

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Published in Transactions on Machine Learning Research (09/2025)
Table 2: Time estimation performance on our TOC dataset and the AMOS test set.
TOC test set
AMOS test set!
Top-3
Top-5
Time MAE (min.)
Top-1
Top-3
Top-5
Time MAE (min..)
BLIP-VQA-bees boue
Li et al
2022
9.30%
211.38
7.28%.
302.82
GPT-to-miui OpenAt al
15.35%
11.15%
Zhang et ml.
2022
(ResNet-101) (VI-BB332)
13.24%
37.30%
58.23%
177.84
7.85%
24.26%
40.10%
261.89
Zhang
10.73%
31.21%
49.05%
7.25%
21.03%
Zias al.
119
14.11%
40.47%
65.94%
188.75
9.14%
27.95%
45.36%
262 262.68
13.87%
39, 19.36%
60.71%
8.63%
TICL (Ours)
20.6095
19.01%
67.82%
171.65
13.55%
38.49%
37.28%
187.87
TICL-Neurest-Neigbbee (Ours)*
25.67%
49.32%
06.74%
11.14%
31.01%
Zhai al. 2019
15.0155
42.54%
68.24%
185.34
8.85%
24.12%
38.60%
268.41
Salom of
13.535
38.47%
10%
176.70
8.16%
23.85%
30.07%
257.00
Experiments on this test eet are conducted in arro-shot masner, in which un diveeily mas mnte model Lealerd solely on the TOC datwont
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5.2 Time estimation performance
As shown in Table 2 TICL not only outperforms all previous pure vision methods but also outperforms previous methods that require additional geolocation inputs on most metrics. TICL also demonstrates better performance in the additional AMOS test set, thereby indicating better generalisation ability. Tn summary, our experimental results indicate an overall improvement of the proposed methods in clock time estimation, espocially in terms of accuracy and generalisation ability.
Salem (2022)
Zhai (2019)
TICL (Ours)
TICL-NN (Ouns)
Groedd
Prodiction
Prodiction
Prodiction
Profiction
Ground
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Prodiction
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Figure 4: Confusion matrices. They provide more detailed comparisons throughout the 24 hours TOC test set (lop), and the AMOS test set (bottom).
Additional error analysis on pre-text tasks In addition the quantitative results, we also visualised the confusion matrices in Fig. to provide more in-depth evaluation of the task. An interesting finding is that both Salem et al. 2022 and Zhai et al. (2019) overlooked minority classes in the training set (classes from 1 a.m. to 5 a.m.), resulting in nearly no predictions for these classes on both test sets. This indicates a notable bias in these models towards classes during hours of intense human activity, when more images are present dataset. In contrast, our proposed TICL method exhibits more balanced class-wise distributions of positive predictions on both test sets, suggesting better estimation fairness. The general trend in all the confusion matrices also suggests the remaining challenges faced by all methods. Notable anti-diagonal patterns indicate inherent visual ambiguities of the clock system w.r.t. appearances.
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Table 3: Detailed component analysis of the proposed method design.
Image Encoder'
fxz
TOC test set
AMOS test set
Top-1 nec
Top-3
Top-5
Time MAE (min..)
Top- acc
Top-3 acc
Top-5
Time MAE (min.)
x
7.68%%
23.36%
302.84
5.633%
17:1256
27.2455
319.00
DINOv2.hse
Ours
8.01%
23.94%
30.0636
295 34
5.2356
17.3155
29.2255
320.76
1.02%
3.29%
12.04%
456.77
4.11%
11.41%
19,62'8
361.92
Ours
9,58%
27.31%
41.17%
254 69
5,09%
14.74%
25.10%
327.72
X1
11.45%
52.27%
08%
240.77 77
7.87%
22. (9%
36.815%
281.80
Swi)V2(D)
Ours
X
11.6455
32. 13%
50.3356
243 86
7:5136
22. 22.9656
37.5455
218.21
12.7455
33.65%
32,0656
222 76
6.7556
23.7656
38.41%6
264.30
Ours
13.3755
34.04%
32.9336
216.17
7.37%6
22.0856
38.0855
276.60
x
X1
11,5995
32. 9556
8058856
240 64
6.4156
21 .956
37 6355
300 74
ConvNeXt(L.)
Oues
11,86%
32.8156
50.1836
240.80
6.1036
20.6636
35.85%
302.44
x
13.5198
35.2916
32.76%
216.28
7.7156
24.331%
39 961
275 23
Ours
14.07%8
36.75%
201.19
8.27%
24.78%
40.80%
263.03
X
X
16.06%
43%
55,075
193. 66
12.97%
36, 95.9
55. 55.96%
200.93
Ours
16.735%
44.055%
03.9056
195.41
5096
38.49%
58.30%
189,99
CLIP (VIT-L/14)
X
18.00 13,0095
48.4
65.98%
181 22
12.57%
37.51%
57.23%%
189.09
Jazz
faz
19.26%
45. 90%
189 07
11.42%
35.035%
54.06%
197.00
RFF T2V
16.7555
35.145%
46.613 16
206-50
6.073
15.7856
22.2756
200.70
17,70%5
45.6015
66.1156
185.89
7.3756
21.74%
35.1095
264
Ours
20.01%
49.01%
67.83%
13.35%
38.50%
57.25%
187.87
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5.3 Detailed component analysis
In this section, we present the ablation study investigating the effectiveness of each module in the proposed TICL model across different configurations. To ensure fair comparison, we use a classification- basccd infer-
ence pipeline for all experiments (see implementation details in Appendix B.2 Table provides performance
comparisons under various settings, including different backbones (Tan & Le 2021 Oquab et al. 2023 Liu
ct al. 2022a b) within the image encoders.
Impact of different backbone image encoders: The differences in performance across the image en- coder backbones highlight the effectiveness of the CLIP Image Encoder. Thanks to its rich semantic repre- sentations, the CLIP Image Encoder consistently achieves better results across all configurations than other backbones.
Ablation on proposed modules: We observed that the Time Encoder for and the Image-Tinee Adaptor ferra have varying effects when used individually, either slightly improving or degrading the baseline. How- ever, when both modules are employed simultaneously, they lead to universal improvements across all image encoder backbonos, underscoring the joint contribution of the Time Encoder and Imago-Time Adaptor.
Ablation on different time encoding methods: We also tested the performance using other variants of Time Encoder. RFF (Rahimi & Recht 2007) encodes input (hour, minute) into 512-dim vectors to align with ITA outputs directly using the same dynamic queue as in Vivanco et al. (2023), which is outperformed by our methods on TOC test set and does not generalise well on AMOS test set. In addition, T2V (Kazemi et al. 2019) based Time Encoder also shows similar problems on its performances. These comparisons suggest that the one-hot class embeddings exhibit better generalisation ability and performance on most metrics. A possible explanation could be that, the sensitivity of accurate time encoding results in some clock timestamp embeddings being assigned with very limited training samples to represent them. This makes them not robust against the visual ambiguity of time, as images with the same clock time could have very different appearances due to variations in geolocation, season, and climate. In contrast, clock time class embeddings for each hour are vaguely associated with many samples that lie in the same hour interval. Representing target clock timestamp embeddings using spectrums of temporally close samples may reflect the ambiguity of clock time w.r.t. image appearances, making the estimates more robust and generalizable. (See more analysis in the Appendix C.1).
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Table 4: Joint localisation of geolocation and time. probabilities that the top-1 retrieved image has
GPS coordinates' L1-difference S 0.01 and a clock time L1-difference S minutes to query images.
Chance
Salem et al.
2022
Zhai et al 2019
CLIP (VIT-L/14)
TICL (Ours)
0.03%
2.08%
4.17%
6.25%
10.42%
6 What Time Tells Us Downstream Tasks?
Apart from the possible media forensics application that time estimation can be applied (Padilha al, 2022). We also interested in the relation other computer vision tasks and the capability of the learned time awareness. Therefore, in this section, explore 1) time-based image retrieval which is direct use of the model in retrieval & recommendation applications (Section 6.1 2) video scene classification, revealing an interesting connection between static images and dynamic visual scenes learned from timestamp supervision (Section 6.2 and 3) time-based image editing, showing the model learned can provide proper perceptual guidance to the generative models (Section 6.3
6.1 Time-based image retrieval
An intuitive application of the time-aware model is time-based image retrieval. It aims to effectively retrieve images from a database with a similar cap-
TICL (Ours)
0.7
6.0
tured time of day to the query images. We con-
CLIP (VIT-L144
sider zero-shot vector search engine that retrieves the nearest neighbours of query images based their time-aware feature similarities. To evaluate this task, separated the TOC test set into 13,043 database images and 48 query images spanning all hours. The performance is measured using Re- call@k reported in Fig. 5 Images retrieved with a lime difference of no more than 30 minutes from the queries are considered as positives. The results clearly show that the proposed TICL model achieves the best performance across all Recall@k metrics.
0.6
Zhal (2019)
Random
Recall Rate
0.43
047
0.27
0.3-
$200.2
0.30
0.44
0.0
R@5
Figure 5: Recallãk for time-based image retrieval.
We also analysed the distribution of metadata differences between the retrieved images and their correspond- ing query images. Specifically, Fig. 6a illustrates the distribution of clock time errors among the top-100 retrieved samples for different features. The results show that TICL retrieves a higher percentage of images with smaller time errors compared to other features. Fig. 6b further shows the geolocation error dist.ribution. Images retrieved by vanilla CLIP embeddings are geographically closest to the queries, suggesting that CLIP represents rich understanding of scene priors strongly related to geolocations, which delineated in some previous Visual Place Recognition (VPR) work using CLIP backbone (Radford et al. 2021 Keetha ct al. 2024 Vivanco et al.) 2023). We suspect that this contextual awareness is partly inherited by TICL, which achieved moderately better performance than other time-aware features of previous work. From this observation, we suspect that TICL disentangled time-aware features from other metadata attributes. To validate this hypothesis, for each query image, we consider an additional task of localising geolocation and time jointly using retrieval. As shown in Table g the advantage of TICL suggests has more balanced capability of underslanding geolocation and time jointly than other models.
6.2 Video scene classification
Understanding dynamic scenes is an important challenging problem that visual models currently face (Miao ct al.] 2021]. A fundamental task this domain vidco scene classification. Pretraining models on static images with object categories have been proven to be helpful in video classification (Carreiaa & Zisserman 2017). Intuitively, dynamic scenes represented in videos have temporally consistent frames within.

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Published in Transactions on Machine Learning Research (09/2025)
29.2%
Zhol al. (2019), Mean Error 4.50
Zhai et 12019), Mean Emor 96.96
8.3%
CLIP (VIT-1114) Mean Error 4.90
25.0%
(VIT-L/14), Mean Error 50.85 Salem et al. (2922), Mean Error 95.72
(2022),
TICL (Ours), Mean Error 4.12
Percentage
20.8%
TICL (Ouns), Mean Enro 79.07
6.2%
2.1%
0.0%
10
12
50
100
150
200
250
300
350
Time Difference (hour)
Geolocation Difference
(a) Retrieval time error distribution
(b) Retrieval geolocation error distribution
Figure 6: Comparison of geolocation and time error distribution. It is collected among top-100 retrieved images using different feature extractors.
Table 5: Performances the video scene classification task.
Classifier
Hollywood2--oene.
YUP++ 't
360+x (Panoramic)
360+x (Third-view) f
VideoMAE
Tong el al.
2022
48. 83%
97.29%
53.70%
54.55%
VideoMAF
52.
98.33%
57.40%
50.91%
VideoMAE
Salem
2022
45.53%
97.50%
44.45%
52.72%
VideuMAE
Ziuai
2019
51.03%
97.71%
48.15%
56.36%
VideoMAE + TICL
56.53%
98.33%
59.26%
58.18%
CLIP (VTT-L/14) (Linear Probing)
39 60% 57.04%
97.08% 98.33%
35. 19%
10%1
TICL (Linear Probing)
51.85%
42.59%
We use an unofficial train train//ol/// /wal/tess teet split of 8:1:4, since the original 1:9 train, best split overfit prematurely.
Therefore, despite dynamic scene categories seeming to be irreverential to the time day, we are particularly curious about whether the proposed TICL model, which pretrained to estimate clock time for input static images, can provide additional understanding of continuous sequence of frames other than discrete moments represented by static images.
Experiment setup: To assess whether our time-aware models provide valuable priors for understanding different categories of dynamic scenes, we provide classification results under two different constructions. 1) We concatenate the time-aware features from different frozen feature extractors to pretrained VideoMAE backbone (Ton et al. 2022), 2) directly run linear probing video frames with the frozen feature extrac- tors. We compared the performances under different feature extractors on various scene datasets including Hollywood2--Seene (Marszalek et al. 2009), YUP++ (Derpanis et al. (012) and 360+x (Chen et al. 2024 Please refer to Appendix for implementation details and other experimental settings.
Possible correlations between the time of day and scene: According to Table 5, TICL features provide consistent improvements to the scene classification task under different settings. The most straight- forward explanation for this boost that scene classes are correlated with the learned time of day by definition. To prove this, we visualized the cosine similarity between certain text embeddings of certain scenes that clock time class embeddings, shown in Fig. The imbalanced distributions proved the conceptual correlation of scenes to time due to human activity patterns.
Consistency in time-aware frame embeddings: As shown in Section 6.1 the TICL representations capture similarities between images with close clock times. Natural videos, although they sometimes involve drastic subjects or view movement, frames within each should still represent continuous time periods. TICL features for frames across the whole video should be more consistent than those of vanilla CLIP, which have stronger locality per frame (Tang et al.) 2021). This intra-video consistency allows for more general timo-aware priors extracted using TICL. The t-SNE visualisation of the video features Fig. supports this claim, showing that TICL features are more separable than vanilla CLIP features (see Appendix E. for more in-depth analysis of the phenomena and claims above).
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Toxt Polability
020
Lighting Storm
Firewarks Sky Clouds
0:15
0.30
Figure 7: The probability distribution for different input text queries of scenes that are seem- ingly irrelevant time. This is calculated by Softmax exp(IctIP-1.)) where Tú Tj TELIp are "exp(TouII.TT)
the TICL clock time class embeddings and CLIP text emboddings.
(a) CLIP
(b) TICL (ours)
Figure t-SNE visualisation comparison. It compares video features before the final classifier layer
using either (a) CLIP or (b) TICL, on the Hollywood2- dataset (Marszalek et al. 2009), each different scatter point shape/colour corresponds with classes.
6.3 Time- image editing
As aforementioned in Scction the TICL model provide the corresponding embeddings for certain periods of the day. Therefore, it is natural to consider using these clock timestamp embeddings guidance to edit images toward different classes. To assess the extent to which clock time embeddings aid this task, we adopted the following experiment framework from Patashnik et al. (2021) that conducts image editing via latent vector searching through optimisation steps instead of tuning the models directly.
Experiment setup: To provide comprehensive evaluations, we conducted experiments on three different baseline StyleGAN2 models [Krras et al., 2020b) focusing on different subjects trained on (Skorokhodov et al. 2021 Yu et al. 2015). The pretrained generator weights are adopted from existing codebases Pinkney
(2022): Karras et al. (2020a). The editing pipelines were restricted follow the same latent optimisation baseline method introduced in StyleCTIP (Patashnik et. al., 2021 Additionally, we designed time-aware synergy loss combining directional CLIP loss and TICL feature similarity loss. Specifically, the editing process can be formulated as:
(2024 Epstein
arg
arg min (AILTIOL XaLoLirdir Ava ||w Wsource l weW+
in which w, Wsouree represents latent vectors for ongoing edit outcomes and original images, (design, hyper- parameter and implementation details in Appendix F.I.
Qualitative evaluation: The proposed lime-aware synergy loss yields the most plausible synthesis out- come as illustrated in Fig. g The limitations of solely text-guided image editing methods could be due to certain adversarial solutions fooling CLIP image encoders with certain patterns only [Lu al.) 2021). Specifically, Fig. 9 shows the vanilla StyleCLIP edits using the CLIP loss tend to focus the general tint of the image but. fail to reflect realistic illuminations. We find that replacing the CLIP
their susceptibility
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Latenn-Optimisation Methods
source
Doylime
Daytime
Daytime
Source Images
Lextgarget
Noon
Sunset
Nisht
Noon
Sunset
Night
Noop
Sunset
Nisht
CLIP Loss
(ICCV 2021)
Directional CLIP Loss
12:00
19:00
23:00
19.00
12,00
19:00
25:00
Directional
+ TICL Loss (Ours)
Other Non-Laient CpaliiitOtiiiiooionn Methods
CLIPStyler (CVPR 2022)
StyleGAN NADA (ACM TOG 2022)
Figure 9: Time-aware image editing. It shows the results of applying time-aware editing method (green overlay) on three different StyleGAN2 models trained on LHQ-Landseape (Skorokhodor et al. 2021). LSUN-Church, and LSUN-Bedroom (Yu et al.] 2015) datasets. The results of other non-latent optimisation methods are also demonstrated (under grey overlay).
assist in overcoming larger domain gaps. Despite showing improvements over the baseline editing method, the results still show unrealistic artefacts and shape distortions. These limitations show the necessity of incorporating additional time-aware features other than just guidance text embeddings when computing loss functions for image edits. Our qualitative evaluations demonstrated the effectiveness of the TICL embeddings on the specific task. We also included other bascline method results that work under different frameworks other than latent optimisation for a more comprehensive comparison. See more quantitative evaluations (Table 1 user studies (Table 13 and results TICL-aided editing with diffusion models in Appendix F.1 and Appendix F.2 respectively.
loss with directional variant introduced in previous work (Gal et al. 2021 Kwon & Ye 2022
7 Conclusion
In this paper, we tried to answer the question of what lime tells us, through exploring the pretext task of time-of-day estimation and downstream tasks. A new reliable benchmark dataset, TOC was introduced to support the pretext task, consisting of images captured in natural settings with verified timestamps. This dataset addresses the limitations of existing datasets by providing a more diverse and realistic collection of images that better reflect daily visual experiences. Building upon that, proposed, which aligns clock timestamp and image in representation space via pretext time predic- tion task, surpassing previous work in timc-of-day estimation. The learned time-aware representations were further studied via validations on several downstream lasks. The strong performance in these downstream tasks highlighted its capability to recognise the similarity of the captured time (in time-based image re- trieval), frame-coherent priors in TICL for vidco scene understanding (significantly improved video scene classification), and produce realistic and time-consistent performance time-aware image editing (accurately reflecting typical lighting conditions for different times of day).
learning paradigm (TICL)
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Appendix Roadmap
This is the appendix for the main paper. Here general roadmap describing the contents of each part of this document supporting the main paper:
We first provide additional details to the datasets in the Appendix A which includes how we cleaned the originally noisy data into datascts that reflect the diversity of natural images paired with accurate metadata.
In Appendix we cover the detailed illustration of the implementation of the model and the setup of the experiment. Along with additional performance and error analysis. We also provide addit ional results testing the ability to jointly predict date related metadata other than just the clock time.
In Appendix C explore various scalar encoding methods to time variables the pre-text task through regression example in Appendix C.T] We also discussed the inherent trade-off of fine- grained classification via additiona ablation to the number of classes in Appendix C.2
Appendix D provides additional qualitative evaluation the time-based image retrieval task.
Appendix B gives experimental setup details, well more evidences of the intra-video consistency identified in the main paper in Appendix F.2
In Appendix F.1 we provide a detailed setup of the experiment along with additional qualitative and quantitative evaluation of the capability of time-aware features image editing tasks.
Appendix F.2 also shows results of using time-aware features to further improve the fidelity w.r.t. clock time via timc-aware features under more advanced diffusion model basclines.
In the main paper, we discussed about the implications of time-awareness in visual scene under- standing, in Appendix G we provide more examples of text query about the conceptual relations between clock time and ceone//atioo/oojeeee text embeddings.
A More Details on Datasets
A.1 The proposed TOC dataset
Comparison to previous works: In this work, introduce a new benchmark dataset that combines images from the YFCC100M (Thomee et al.) 2016) and Cross-Vins Time datascts (Salem et al. 2020 As we briefly summarised in Section 4. the major differences of our datasets to previous static image dalasets featuring time-mette meneeattadata lies in the overview of the differences. For previous generic social media image datasets, there have been persisting issues of unreliable timestamp metadata due to unsynchronized user/devies activity. Apart from unreliable groundtruth, the visual appearances of time in some of the images often undefined in non-photographic
diversity and metadata correctness. Table gives an
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Uncalibrated timestamp
DBSCAN outliers
Images kept
Images kept
(a)
(b)
Figure 10: Dataset filtering process, where (a) shows examples of finding unnatural images in DBSCAN [Ester et al., 1996) outliers that may degrade dataset quality, and (b) shows examples of removed images with uncalibrated clock timestamps.
Table 6: Comparison of existing image datasets with timestamps.
Image souree
Timestamp reliahility
diversity
MIRFLICKR-IN (Hiidos
Low
mubile miscellancous
high
YFCC100 Thomee
2010
mobile miscellaneous
ligh
AMOS JJccob al. 2001
fxxe webcaus
limited
Croes low Time (CV
Salem el al.
2020
webcaus + mobile devios
mixed
TOC (ours)
wild-view natural photography images
verified timesone-aligneed
high
images which do not reflect any natural time-of-day. Despite such these problems are mitigated for datasets with proportional samples from static surveilance camera, the repetitive views and occasional ground-truth leakage overlayed on the camera footage suggests limitity usablilty. These issues are depicted separately Fig. 11 Therefore, our dataset resolved these issues by manually verifying metadata fidelity given the image on purely social media samples.
Detailed curation steps: Now, we cover more details of the dataset curation process. Fig. 10 gives a clear illustration of the data filtering steps to the dataset, improving the sample quality and metadata reliability, We firstly inspected all the night-time images with average pixel brightness > 100 determine whether they have clearly mislabeled timestamps. Specifically, extreme cases like polar day were considered, images with |altitudes] 2 75 were retained regardless of illumination. This step removes clearly unsynchronized images, which do not align with human consensus about nighttime illuminations. To reduce the workload of filtering unnatural images, we firstly partitioned the images into 24 different hour intervals; within each of
Unsynchronized Timestamps
Lacking Visual Cues of Time
Label Leakage in Webcam Data
VOR: (E BOTE 22
Figure 11: Existing issues in previous datasets
Salem et al.
2020 Thomee et al. 2016 Jacobs et al.
2007
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5:00
7:00
5:00
8:00
4,00
8.00
4,00
9.00
3:00
9.00
3.00
10:00
2,00
10:00
2.00
8%
0%
11: 00
8%
00
11:00
3%
44%
1:00
12:00
0:00
12:00
0:00
13:00
13:00
14:00
00
14:00
22.00 00
15:08
21.00
15:00
21.00
16:00
20:00
16:00
20.00 00
17:00
19:00
17:00
19:00
Dataset hourly sample distribution, where (a) shows hourly sample distribution for TOC dataset, in which daytime images are significantly more prevalent than nighttime images, and (b) shows hourly sample distribution for AMOS-test dataset displaying a similar skewed but more balanced distribution towards daylight hours.
Figure 12:
Before Cleansing
After Cleansing
20
100
Figure 13: Mean brightness with standard deviation over images taken at different periods of the day.
them, we apply DBSCAN (c = 10, minPts = 100) on ResNet-18 features, which gives a majority group and outliers. We recruited workers to manually review all the outlier images determining whether to add them back to the dataset. This pipeline allows for efficient removal of unnatural images without proper visual cues about time in the dataset by looking samples groups with distinct features the majority group containing natural photographs.
We conduct a statistical sanity check on the dataset- processing pipeline. The most intuitive visual cue correlated with the time of day is scene illumination, which we approximate by the statistical average of pixel brightness. In Fig. 13 we observe that, before cleaning, images labelled 00:00-06:00 in the previous dataset have brightness comparable to daytime photos, and an unnatural gap appears between the 21:00 23:59 and 00:00-03:59 bins. Both patterns violate common sense, where 1) lato-night images should be markedly darker than daytime images, and 2) illumination should change most rapidly around sunrise and sunset, while remaining relatively stable during midnight and noon periods. These anomalies point to unsynchronised capture timestamps in the unprocessed dataset. After cleansing, the TOC dataset follows the expected smooth day night trend, confirming that our pipeline yields data consistent with human intuition about illumination over 24-hour cycle.
Following the data filtering, we partitioned the TOC datasct into a training set and test set a 9 ratio, with stratified sampling to ensure that the clock time distributions of both subsets were approximately equivalent. We observed significant scarcity of images with reliable metadata captured at night compared
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"image_ "2011000 "20110125 084002.jps"
"image, path" "20130124, 0646611
"atttude:: 513105118775
"Iattude"s 56.3803333
"longitud"'
"longitude": 11,85
"Bmeet: 2201-00000 08:40.02"
22013-01-24 07:96:31"
"image path": 220120615 06092A.pp"
path": "20110712
093520..08
"lattuue": 58 587744444664
"longtude": 84.3883666381
"lonaitude' S.87:: 5.87359446579
"time": "2012-09-15 02:09:24"
"time" "J011-00001:02:::: 00011-07-12 91:35.20*
Figure 14: Sample images from the AMOS test dataset. The images showcase different scenes captured by stationary surveillance cameras at various times of the day with decent visual quality.
to daytime images. This observation corroborates our hypothesis that the distribution of timestamps in images shared on social media inherently unbalanced as depicted in Fig. 12
Such imbalance presents challenges in learning equitable embeddings for class time periods that are under- reprosented due to limited sample availability. This imbalance necessitates strategic approaches to model training that can adequately compensate for these discrepancies.
A.2 AMOS test dataset
Dataset Filtering and SNR Estimation: The AMOS-test dataset was selected from the CVT test set, containing 5,000 AMOS images, which was further reduced to 3,556 images. The dataset filtering involves several steps to ensure metadata reliability and sample quality, First, we calibrated the original UTC timestamps to their respective local timezones using the geolocation metadata. Then, we filtered out (1) noisy images with low Signal-to-Noise Ratio, where the SNR is estimated using block-based variance method. Specifically, for an image I with N pixels, the SNR is computed as
a Gaggnal
SNR(I) 1110--lge
2
where the noise variance o is estimated as the average variance over the lowest 10% of non-overlapping blocks of size 16 16 pixcls, and the signal variance is given by
signal = oootal noise?
with of being the variance of the entire image. Images with SNR(T) S 15 were discarded. After cleansing, the average SNR improved from 1.93 (std 10.35) to 3.38 (std = 3.50). This filtering ensured that only images with recognizable time-of-day related appearance were included in the evaluation. Figure 14 shows few sample images from the dataset.
As the images represents different domain to the proposed TOC dataset. Although the dataset contains repetitive visual appearances due to the stationary setup of the cameras, it benefits from more balanced distribution of timestamps throughout the day, as shown in Appendix A.1
captured automatically by surveillance cameras with fixed views, the AMOS test set
B Implementation Details of TICL
In the main paper, we covered the high-level design of the TICL model we devised learn time-awareness via clock time estimation pre-text task. This section provides additional details.
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Trainable
Input [224x224a3
[D4-dimnssknil))
Frozen
CLIP (MT-U)1)
Encaterr
(Linean24 Butchhomm U
lUnear1512 1024)
Batchkorm ReLUU Dropout
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BatchWorm ReLU Dropouej 311
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BatchNorm ReLU Dropcunj
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(Lmear7088
Unear1192 700) RelU)
Hoad (Linwer 11004 -> 7689)
(788-immnsionnl)
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Image-Time Time Adapter
Time Encoder
Figure 15: Visualisation of TICL sub-module architectures.
B.1 Model details
The Time Encoder consists of several fully-connected layers, with the detailed architecture shown in Fig. 15 The raw timestamps first preprocessed into 24 one-hot class embeddings. The Time Encoder then takes these input class embeddings and projects them to the desired representation space.
Time Encoder:
The Image-TTinee Adaptor module is employed to adapt the raw backbone features with Time Encoder outputs, as depicted in Fig. [5] Training the Image-Time Adaptor module and Time Encoder jointly using contrastive learning scheme allows for effective alignment between the two modalities.
Image- Time Adaptor module:
Extract Labels
Ti e K
Time
Te-
CLIP fmage Araaiin
Inaaee Encoder
Encoding Labels C'xC'
5 Teu
Training Samples
x
FAISS Index
CLIP Image Encoder
trugs Times
max 1 (wm)
CLIPP Imagee Encoder
Image Acapier
Nelphhour
Adqpeer
Grod
Input x x
Input W H x
(a) Classification- inference
(b) Nearest-neighhour inference
Figure 16: Detailed illustration of different inference pipelines. In (a), the model selects the clock time with the highest similarity the input image from a finite set of clock time class embeddings. (b) shows that the model estimates clock timestamp by finding the corresponding timestamp of the nearest-noighbour to the input images from the training based the sample-specific TICL embeddings.
B.2 Details in clock timestamp estimation inference pipelines
Two different clock timestamp estimation inference pipelines were devised. The first pipeline, shown Fig. [6a] adheres the classification schome, selecting the timestamp with the highest similarity within a finite clock timestamp embedding pool encoded from C one-hot embeddings. The second pipeline, shown Fig. 16b. converts the problem to retrieval-style formulation, using known image-timestanp pairs from the
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params:: 3.7M
Acc: 20.6%
TICL (Ours)
20-
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Salem (2922)
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(2019)
Acc
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Poc: 10.23
10-
pueery 107AM
C0LOM
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200,
Parameters
(a) Trainable parameters and performance comparisons.
(b) Total parameters and performance comparisons.
Figure 17: Parameter efficiency and performance. (a) Comparison of trainable parameters and perfor-
mance. (b) Analysis of total parameters and performance.
Table 7: Joint time cstimation performance on our TOC dataset. Namcly, we jointly estimate the month and hour using the same setup the previous baseline Zhai et al. (2019) for fair comparison.
Hour Prediction
Month Prediction
Top-1 acc f
Top-3 ace f
Top-5 ace
Time MAE (min.)
Top-1 ace
Top-3 acc
Top-5 ace
Month MAE
Salem et al.
2022
13.87%
39.36%
60.71%
186.44
7.40%
25.74%
42.93%
3.14
Ziai ct al.
2019
14.11%
40.17%
65.91%
188.78
11.23%
33.03%
56. 35.16%
2.35
Salem es m
12022
13.53%
38.47%
59.10%
176.70
9.59%
24.56%
39.61%
2.74
Zliai
2019
15.01%
22.54%
68.24%
185.34
12.03%
35.91%
60.50%
2.25
TICL. (Hour nmly
20.60%
49.01%
67.82%
TICL (Month only))
34.48%
68.19% 52.00%
82.88%
1.45
TICL (Month, Hour)
19.45%
42.07%
55.57%
176.45
32.28%
62.28%
1.77
Hour Prediction
Season Prediction
Top-1 acc
Top-3
Top-5
Time MAE (min.)
Top-1
Top-3
Top-5
TICL (Season, Hour)
20.11%
45.9 98%
62.58%
170.93
61.52%
71.74%
80.48%
These baselines take adcitional known geolocation metadata impote, which boosted their performances both preciction tacks. Predicting months.
training set. The model returns the class-level timestamp of the most similar samples it in the training set using efficient vector search engine (Johnson et al. 2019)
Settings for VQA baselines: For baseline VQA based methods tested in Table 2 namely the BLIP [Li al. 2022) and GPT-4o-mini (OpenAl al., 2024), we simply use a pipeline with one round Q&A directly outputing times predictions, which takes the input prompt Pquery "Estimate the LOCAL cap- ture clock time of the image, answer with ONLY one 24-hour time in HH:MM format.'.
B.3 Computational efficiency
Since the majority part of the TICL model, the CLIP image encoder, is frozen during training, the TICL training is thus efficient with small number of trainable parameters. Figs. 17a and 17b shows that TICL achieved the best performance with the minimum trainable parameters among existing methods. Benefiting from the fewer trainable parameters, training on precomputed image features is significantly faster. Also, Fig. [7b] demonstrates that simply scaling up the model parameters for previous works may even degrade the performance. We suspect that it is due to the more severe overfitting of the larger models on training samples. In comparison, the TICL model reached better performance with moderate total number of parameters.
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B.4 Joint metadata estimation with time
We noticed that some of the previous baselines support joint time estimation instead of just focusing on clock time only. They often consider the joint contribution of other metadata including geolocation, date, and time of day the image appearances (Salem et al., 2022 Zhai et al. 2019) to deal with the ambiguity of clock time when given only visual inputs. Therefore, in this section, we aim to explore such capability of estimating time and month jointly from only social media images.
We adjusted the network structure of TICL, enabling its abil- ity to estimate month and hour jointly using a similar struc- ture to Zhai et al. (2019), in which the model predicts 12 x 24 classes combining months and hours. We kept all the specified papar-parsmeters and other setups the same. As for the com-- pared baseline methods, provided in previous works and picked the best performances from several trials, all the models are trained and tested on TOC dataset which contains only social media data to demon- strate the challenges on real-world samples. As provided in Table 7 TICL generally outperforms previous baselines when trained an tested on the more challenging TOC dataset without images with fixed views. In addition, under TICL paradigm, jointly predicting clock time and month or season does not pro- vide boosts to individual tasks. We suspect that it is because of the gaps between the visual cues between the two different target variables. Such gaps lead to difficulties to model a joint probability of Ptt, mlx) for clock time and month m with only the input æ. However, the prediction advantages of models focusing on each attributes only suggest the possibility of stacking such different meladala-aware models in joint metadata verification-relaeed lasks focusing on P(t]m, 2), P(m)t,z))
12
11
10
Distance to noon (hours)
used the same hyper-parameters
-8
Season
Winter
Spring
Summer
Autumn
7.5
5.0
0.0
2.5
5.0
7.5
Figure 18: t-SNE visualisation of class emn beddings <clock time, season>.
However, compared with using the date as additional supervision, the scason supervision combines both date and geolocation gives a much smaller performance gap to the purely clock time supervised model. This suggests that learning meaningful combinations of other metadata could possibly provide better clue to normalise the visual variance of the clock time. To support this point, we have included a visualization of learned clock time and season in terms of embedding in Fig. 18 As visualised, the model clearly recognizes season variations of the same clock time, while keeping general pattern for different time periods of the day. This shows the potential of producing possible calibration mechanism by learning unnormalised timestamps with the help of different metadata supervision. This indicate hopeful direction that we may learn an calibration mechanism to the raw clock time to bio-clock by incorporating comprehensive metadata and scene priors as supervision.
C Exploration of More Precise Time Encoding
C.1 Scalar encoding
In this section, we explore limitations in a simple regression solution to the pro-text ostimation task using scalar encoding of the clock time.
Raw scalar encoding: The regression style construction for clock time estimation from images presents significant challenges as covered in main text. There are different issues with regression models, including 1) loss function sensitivity and 2) discontinuity in the scalar range for regression. In the following paragraphs, we first provide brief illustration of the issue on the regression loss function. Secondly, we present experiments of regression model working in circular space instead of the vanilla scalar range which is a disconnected set. (Zhou al. 2019). These experiments provide explanations for the limits of vanilla regression models.
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Ideal Regressor
Sub-optimal Regressor
Symmetric Image Pair
Predicted Mid-point
Image Sample
2012-09-25 18:18:15
2013 10-05 06:14:54
2012-07-27 23:31:14
2011-12-01 00:00:00
ResNet-18 18 Feature Similarity: 0.86
ResNet- 18 Feature Similarity: 0.89
(c)
(d)
Figure 19: Visual ambiguities for ground truth in regression. (a) depicts a sub-optimal regression model where the predictions are biased towards the mid-point, and (b) shows a trend that images with more similar ResNet-18 18 features could have disparate timestamps. Few examples of such cases are provided in (c), (d).
Let us define the problem setting of clock timestamp regression as follows. Given an image æ, the objective is predict the timestamp y in the range [0, 24) hours of the day. In regression framework, the model maps an input image to continuous scalar output ÿ fo(x) E [0,24).
Consider a dataset D consisting of images taken at various times throughout the day. Specifically, consider pairs of images ((x,, vi), (zj, Ji}} taken during "symmetric times" such as sunrise and sunset, where the
general light conditions are similar but the ground truth timestamps are different (see Fig. 19d and 19c With very similar inputs and the same model fo(.), holds that:
So(x,) xe So(2,)
Then the Mean Squared Error (MSE) loss for the regression model over the dataset is defined as:
D (u fo(xx))2
LMSF (0) =
k=0
To find the optimal model parameters 0*, minimise this loss function. Ideally, the goal of the optimiser is:
VoL(O)
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Figure 20: Experiments on regression model. (a) shows prediction distribution of regression model TOC test set, (b) represents the confusion matrix by hour, (c) and (d) visualise t-SNE of regressor representations annotated with ground truth and predicted timestamps, respectively.
For pairs of similar images with different y, this optimisation leads to mid-point predictions:
y/
+ Mj
0: R
This effect leads to local minima the clock timestamp embedding space Fig. 19a particularly when y and yy are at opposite ends of the 24-hour cycle, for example, 00:00 and 23:59. The regression model struggles with the ambiguous nature of time, resulting in systematically biased predictions towards the midpoint of symmetric clock times. Such bias results in incorrect gradient updates that cannot lead to an accurate estimation model for inputs Tz.
The aforementioned phemomenon of similar images with disparate ground truth timestamps prevails in the dataset. As evidence, we visualise the similarity of features using the ResNet-18 backbone throughout hours for the entire dataset Fig. 196 Therefore, this overall trend feature similarity extends the reasoning the entire dataset, where the predictions are systematically biased towards the average of the whole clock time distribution. The predictions are likely follow the normal distribution with the same mean value to
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(b) Cyclic encoded labels and predictions
Figure 21: Cyclic regression model results, (a) shows prediction distribution of cyclic regression model, and (b) visualise how the cyclic encoding of prodictions differ from the ground truth.
the ground truth distribution and smaller variance o Murphy
2012
DI-1
1
i N~N N
yk a
D
We conduct corresponding experiments to provide evidence for the claims above. Particularly, we train a regression model using ResNet-101 backbone. The prediction histogram and confusion matrix provided Fig. 20a and Fig. 20b support claims. The predictions heavily concentrated around the average value of the ground truth distribution, while the actual timestamps in the dataset are more evenly distributed throughout the day. This discrepancy highlights the failure of the regression model to capture the cyclic nature of time, resulting in biased predictions of the average of the whole range. Fig. 20c shows that the regression model fails to discern similar images with different timestamps, where the features form disjoint trails on which images features from totally different time periods are nearly overlapped with each other. Fig. 20d further shows how the regression model predicts average timestamps for these images with similar features. These phenomena show that although the regression model managed to learn certain extent of continuity of time of day from static views, failed to tackle the ambiguity of clock timestamp given visual inpuls with similar illuninations. Therefore, while such a regression model reaches convergence at local minima for the MSE loss, it is not ideal resorts we are looking for.
Cyclic vector encoding: As we identified in the main paper, the regression range for clock timestamp is a disconnected set. Here we present an attempt to solve the discontinuity of the clock timestamp scalar range: we adopted previous method bridging the gap by trigonometric encoding and decoding cyclic data (Adams & Vamplew 1998). Specifically, encodes the scalar data y into points on the unit circle (cos (v/9 (t//mox), sin (y/ Ymaw)), ), and decodes the model outputs by reversing this process. Such representation space is proved to be continuous (Zhou et al.) 2019). It bridged the gap between the end and the start of the regression value range, which was supposed to be close. We tried this remedy and found that it slightly mitigates the issue of over-concentration on the average values, as shown in Fig. 21a
However, although this modification managed to rescue part of night images that are wrongly predicted toward the mean value of the whole target value range, still exhibits poor prediction fairness, with most of the predictions falling in certain short time spans. The possible cause for such phenomena could still be the
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(e) 96 class partitioning
(I) 144 class partitioning
Figure 22: Confusion matrices under different number of classes provide more in-depth comparison of clock timestamp estimation performance.
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Figure 23: Comparative error analysis of different class partitioning schemes, shows trends of mean absolute error (MAE) and observational error.
local minima that persist in the MSE loss landscape due the prevailing timestamp ambiguitics we discussed. Another observation Fig. 21b is that there exists an obvious gap between the distribution of trigonometric encoding of ground truth timestamps and the predictions. This suggests that the cyclical correlation bet ween
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52.32%
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Figure 24: Increasing number of classes does not further improve the time prediction curacy,, Class Accuracy represents the raw classification accuracy, and Hour Accuracy calculated by - ([[%-X]l,, $30 minutes): Yú, Y; are prediction and ground truth timestamps correspondingly.
D -
visual appearances and clock time may not perfectly follow the simple unit-circle assumption in Adams & Vamplew 1998). In contrast, our proposed learnable embeddings for the target clock time labels TICT, can capture more complex correlations between different periods of clock times and visual cues without imposing such assumptions.
Therefore, the regression approaches struggle to properly address the ambiguity between time and visual features. Regression-based solutions are thus not as favourable for the pretext task of image clock time estimation.
C.2 Ablation study on class partitioning
In the main paper, we adhere to the 24-class classification scheme used in previous methods, As loss of precision may introduce observation errors, we explore the effects of different granularitics of class partitioning on pretext tasks.
To measure the precision loss, we compute observational errors, which are the average difference between actual timestamps and the converted class timestamps. Fig. 23 shows the mean absolute error (MAE) and the observational errors for different partitions of classes. As part of MAE, observational errors inherent such that they persist even with perfect class predictions (Conforti et al. 2020). Specifically, small number of classes induces larger MAE, which is reasonable since converting actual timestamps to coarser time-span classes introduces larger additional observational errors.
However, this does not imply that extremely fine partitions should always be used reduce observational error. We find that finer class partitioning, such as 144 classes, does not further improve the performance. In particular, Fig. 22 presents the performance of the TICL model the TOC test set under different class partitioning. The overall distribution of predictions exhibits similar patterns despite different granularities. Fig. 24 highlights both class accuracy and hour accuracy for the model. The visualisation shows that while class accuracy drops significantly as the number of classes increases, the overall hour accuracy remains stable once the number classes exceeds 24. This degradation class accuracy with finer partitioning can attributed the smaller sample volumes within each class. The smaller the sample volume for each class, the more under-represee represeeed tends be (Sangalli al. (021).. This suggests a potential drawback of finer class partitioning for downstream tasks involving time class embeddings.
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Build Database Vector Index
Databesc Images
Query Image
Sample
16 fiames
Sample 4 frames
Feature Extmetor
Extractor
Feature Extractor
VideoMAE
Search
1xl conv
FAISS Index
Classifier
Frezen
Head
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Trainable
(a) Time-based image retrieval
(b) Video scene classification
Figure 25: Zero-shot downstream pipelines. (a) corresponds with experiment pipelines for retrieval in the main paper, which is zero-shot vector search engine for same-hour images based on FAISS (Johnson let al., 2019), and (b) shows one of the pipeline for video scene classification in addition to linear probing, in which we test the capabilities of TICL by plugging in the corresponding models to the feature extractor whose outputs are convoluted and concatenated to the backbone features (Tong et al. 2022).
Since the difference between clock time estimation performance of the 24-class partition and the optimal result achieved with different class partitioning is within an acceptable range, we choose the 24-class partition the default our main work. This choice allows for a fair comparison against previous mothods, to ensure that our improvements are due to the proposed techniques rather than variations class partitioning. Additionally, the 24-class partitioning, which reached Class Accuracy Hour Accuracy, also ensures that each class can be assigned enough samples so that robust time class embedding could be learned.
To sum up, the ablation study on number of classes indicates that while the proposed TICL method can easily be extended to finer class partitioning schemes and maintains good hour accuracy and MAF, moderate granularity in class partitioning yields the best results for time estimation tasks. This supports our choice of 24-class partitioning scheme for consistent benchmarking to previous baselines and verification of our conjecture on visual time awareness.
C.3 Exploration on other contrastive learning methods
Given the ambiguity of visual appearances with respect to time of day, in addition to the vanilla InfoNCE con- trastive learning we applied, another relaxed supervised contrastive learning counterpart also tested to learn the TICL model following Sogsort (Hwang et al. 2019). Concretely, we replace the InfoNCE objective with an hour-aware, custerrbaseed pulling/ pushing scheme: for each hour, embeddings are repeatedly clustered pulling/pushing
with k-means; samples that. fall into the same hour-specifie subcluster within minibatch (memory bank)
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are pulled together, while others pushed apart via negative log-likelihood objective (as in Hwang et al. (2019)) At inference, we embed query image once and predict clock time via cosine k-nearest-neighhour over the frozen gallery of training embeddings. In the experiment below, We follow defaults in Hwang et al.
(2019): : the hour-wise subcluster count is set to Rsuub = 25, the inner k-means refinement uses max_ max_iter= 10 iterations per refresh, and the assignment sharpness is cont by soncentration_constaeee constant= All remaining training and optimization scttings are identical to those in TICL.
Table 8: TOC and AMOS test results under different contrastive learning methods.
TOC
AMOS
Contrastive Loss
Top-1 nec
Top-3 ace
Top-5 acc
Time MAE (min.)
Top-1 nce
Top-3 acc
Top-5 aec
Time MAE (min.)
Segeors-style (Hwaug InfoNCE (Clasiticataan) InfoNCE (RNN)
2019
18:97
171
39.37
20.60
19.01
67.82
171.65
13.55
38.
$7.28
187.87
25.67
11.14
48.84
As shown in Table 8 across both datasets, the Segsort-style variant did not consistently improve over InfoNCE. On both TOC AMOS, InfoNCE methods achieves the best performance. We hypothesize two contributing factors: (i) prototype assignments in Segsort are well-suited to separable semantic classes, whereas time-of-day differences can be subtler than object-level semantics; (ii) hour-wise k-means over CLIP features tends form subclusters by object identity carly training, which may misalign with temporal structure and provide weaker supervisory signals. Developing supervised contrastive objectives that more directly encode temporal neighbourhoods remains promising direction; we therefore retain InfoNCE as the default in TICL.
D Qualitative Time-based Image Retrieval Results
Fig. 26 provides a closer look the retrieved images using the pipeline in Fig. 25a as part of more detailed qualitative evaluation of retrieval performance. Some of the retrieved images have totally different content from the query images, but share similar light, conditions. This suggests that model disentangles the time- awareness from rich semantics of CLIP representations, which have more semantic focus the subjects. In addition, the negative predictions still share similar illumination to the query images, suggesting the essence and ambiguity of clock time to visual appearances.
E
Additional Results on Video Scene Classification
E.1
Experiment setup
The performance of different models the video scene classification task was evaluated across three datasets, each containing videos with distinct styles. Apart from simple linear probing, we also tested the model's performance fused vith/against.... bascline method VideoMAE (Tong et al. 2022 The detailed fusion
architecture is visualized in Fig. 25b
Hollywood2-Scene MMarszalek et al. 2009) is a movie clip-based dataset with 570 training videos and 582 test videos across 10 scene classes, totalling 20.1 hours. Each video represents a specific dramatic scene with multiple shots, meaning drastic view/subject changes within.
YUP++ (Derpanis et al. 2012) comprises 1200 videos across 20 scenes captured by either stationary or moving cameras. Given the significant differences between the 20 scenes and the fact that the average clip duration is only seconds, the classification task on it is considered less challenging Wang & Koniusz 2023
x60+x dataset (Chen et al.) 2024) is more recent dataset introduced for holistic dynamic scene understanding with multiple views captured by stationary cameras. It consists of 15 indoor scenes and 13 outdoor scenes, with 1380 clips totalling 67.78 hours. Its multi-view and stationary camera traits enable us to evaluate how our learned time-awareness perform on different types of views individually.
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Figure 26: Randomly sampled retrieval results. Each image is annotated with its corresponding timestamp, green captioned images positive retrieval while red are negative predictions with Error 00:30, retrieved images closer to the left have larger similarity to the query images.
For fair comparison, a fixed of hyper-parameters was used different experiment trials. Apart from the number of epochs and the learning rate, we followed all the parameter settings in Tong et al. (022). And only varied numbers epochs and learning rate for different datasets in. We report
Hyper-parameters:
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Figure 27: t-SNE visualisation comparison. It visualises timo-aware video features in YUP+ dataset (Derpanis et al. 2012). Each embedding is annotated by their corresponding labels. It exhibits similar trend to the t-SNE results in the main paper.
the best result achieved for each method tested. Specifically, to each original training dataset to fairly select the best checkpoints for each method.
split of 5:1 was applied
E.2 Time embedding coherence on video frames
As discussed in the main paper, the observed improvements when integrating time-aware features with video classification backbone models could be altributed to the stronger intra-video consistency of these time-aware features.
To provide quantitative evidence of this consistency, we examine the characteristies of time-aware features across frames within each video. The backbone VideoMAF (ViT-B) model takes the input by sampling 16
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Table 9: Hyper- parametrs used for video scene classification on different datasets.
Hyper-parameter HollywoodZ-Scene
YUP++
360x (Third-person)
360x (Panoramic)
Learning Rate # Iterations
x 10-6
5 x 10-5
x 10-6
7 10
20
10
20
20
Default Settings from Tong al.
2022
Wolf et al. 2020 (Common Across All Datasets)
Optimizer Type
adamw (3rch = 0.9, 82 = 0.999, 10-8)
LR Scheduler
linear
Batch Size
Table 10: Mean intra-video feature variance. It is computed by the mean feature variance of 16 imput frames for each video using different models, showing quantitative evidence of intra-video feature consistency of time-aware models.
Models
Hollywood2-Sceme
+UP++
360+x (Third-person)
360+x (Panoramic)
CLIP (VIT-1/14)
7,49 x 10
2.49 x 10
3.31 x 10
2.83 x 10
Salem et al. (2022
3.52 x 10-6
1.23 x 10-6
7.55 x 10-7
7.86 x 10-7
Zhai et 2001
2.50 x 10-4
1.00 10
8.50 10-5
7.59 10
TICL (Ours)
3.33 x 10
1.24 x 10
1.44 x 10-4
1.33 x 10-4
frames evenly from each video. For the 16 input frames, we observed that the time-aware features of these 16 frames exhibit siguificantly smaller average variance compared to their CLIP features, as shown in Table I0
This finding supports our intuition that natural video that depicts dynamic scene typically captured over short period of the day, leading relatively small changes in the timo-aware features of consccutive frames. In contrast, the CLIP features show more drastic changes between frames, making it harder to summarise consistent frame-wise features into coherent video-level features. The t-SNE visualisation comparisons to these features in main paper and Fig. 27 provide additional results to prove that TICL video features are more separable than CLIP video features.
Thus, time-aware feature extractors provide more consistency across different frames, making easier capture time-related visual priors in videos, which correlate with scene categories. These time-aware video priors eventually improved the video scene recognition performance, illustrated in the main text.
However, it is observed that the embeddings in Salem et al. (2022 and Zhai et al. 2019)) have much smaller intra-video feature variances, but they perform worse than the TICL features we proposed. Given that the previous methods produce 128-dimensional time-aware embeddings, which dimensionality is much lower than TICL embeddings, is expected that they have much smaller variances. Moreover, although previous methods perform moderately better than the bascline methods in the majority of test datasets, their performance degradation in panoramic video datasets suggests limitation in terms of generalisation ability between different styles of videos, especially for those captured in rare camera views in the 360+x dataset (Chen et al. 2024). In contrast, TICL, utilising strong foundation model generalised better across different kinds of videos.
In summary, time-aware embeddings could provide a more coherent representation among multiple sequential frames in a video, which are relatively invariant sudden view view/object changes altering the semantic moaning of the frame. Among the timo-aware models, TICL gives more robust time-aware priors that generally bring more improvements than all the other time-aware models different styles of video.
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Update
CLIP Represenlation Space
CLIP Text Encoder
Teure TeXTeaarrrrggg
target textgource
ATcw
E W+ +
Tour (G(W)
Generator
Teu (Mosurce)) )
Alcup Moue
Edit = 1
|Acurl P&Teul
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arg CryCri AzCoLiPa + 42 Wsource l12) we
G (w)
Image- Time Adaptor
Lyı = (GG(() (targge))
Time Encoder
target 23: 00
Cosine Distance
Frozen Trainable
TICL Representation Space
Figure 28: Time-aware image editing pipeline. This is the pipeline for latent optimisation for image editing, where Wsource represents latent vectors for ongoing edit outcomes and original images, frarget is the onc-hot encoding of the desired time of day for the output image, G(.) is the generator, dist(:..) computes the cosine distance ween two vectors, AlCLP the difference bet ween CLIP embeddings of the original image, ATcLi stands for the difference between the source and target caption embeddings. for (-), for() corresponds to components of the TICL model.
F
Additional Results on Time-aware Image Editing
F.1 Latent optimisation
Experiment setup & Hyper-parameters: Fig. 28 gives an overview of the experiment pipeline we used for the time-aware image editing task. For the main paper's results, each columns results were obtained via the same hyper-pen trpee-ppraaeter setup. Specifically, we set the target timestamps ttrrget as visualized in the figure and fixed the λ₁ = X2 1, using Adam optimiser with rampup= 0.05 for all experiments; we varied other as visualized in the following Table I w.r.t. different target time periods of the day and the subject contents of images.
Table 11: editing processes.
for LHQ (Pinkney 2024
LSUN-Churde and
Yu et al.
2015
LHQ
LSUN-Churel
LSUN- LSUN-Bedroom
Hypar-parameter
Noon
Evening
Night
Noon
Evening
Night
Noon
Evening
Night
ALa
0.001
0.001
0.0005
0.001
0.001
0.0005
0.001
0.001
0.0005
# itcrations
50
50
100
50
50
100
50
50
100
0.07
0.07
0.1
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0.5
0.5
0.05
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0.05
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Steps
Figure 29: Day-to-Night Edits. An example of transitioning images from daytime to nighttime using latent optimization. This figure shows the progression of edits from various starting points larget times of day 22:00 (The rightmost figures are outputs for each edits.).
Steps
Figure 30: Pay-to-Evening Edits. An example of transitioning images from daytime to evening using latent optimization. This figure shows the progression of edits from various starting points to target times of day 19:00 (The rightmost figures are outputs for each edits.).
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Steps
Figure 31: SuattttStttttooooon Edits. An example of transitioning images from nighttime/sunsse to noon using latent optimization. This figure shows the progression of edits from various starting points to larget times of day 12:00 (The rightmost figures are outputs for each edits.).
Table 12: FID Scores. They quantitatively show how realistic the image editing results are for different methods on two image edit directions.
Methods
Day-to-Night & Day-to-Sunset |
Latent optimisation (CCLiP)
Patashnik et al. 2021
53.55
50.60
Latent optimisation (CeLIP
50.07
50.59
Latent optimisation (CCLIPa. + CrICE)
48.97
50.41
StyleGAN NADA (Gal et al. 2021
78.80
66.58
CLIPStyler Kwon & Ye 2022
71.12
73.59
More qualitative results: Additional results of latent optimisation based editing are presented. We varied the initial latent vectors and target hours to show the broader capabilities of our approach. Fig. Fig. 30 and Fig. 31 provide more examples of time-aware image editing with intermediate results during optimisation steps. The results suggest that our method could be applied to broad lime-aware editing directions, which can start from images from various times of day.
20
Quantitative evaluations (User study): In addition to the qualitative evaluation results, also include quantitative metrics to evaluate the synthesis results. Table 12 gives FID scores HHusell et al. 2017) to different edit directions calculated by the official Pyrorch implemeenatton of Seitzer 2020) on 5000 samples for each methods. Our method outperforms existing methods with a smaller FID score suggesting more realism in the synthesised images. Additionally, we conducted a user study (by using the mcan-opinion- score scheme) on the output images. The preference scores for each method are reported in Table 13 further demonstrating the ad adnatagess brought by incorporating time-aware embeddings.
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Table 13: User study evaluating image editing qualities, which we report preference scores and their standard deviation (in brackets). Preference scores range from 1-5, and higher scores mean better preferences.
Methods
Day-to-Night f
Day-to-Sunset t
Latent optimisation (LCLiP)
Patashnik et al. 2021
2.80 (0.60)
2.84 (0.53)
Latent optimisation (CoLIPar)
2.63 (0.85)
3.28 (0.67)
Latent optimisation (CCLIP + LTICT) (Ours)
3.34 (0.64)
4.01 (0.58)
StyleGAN NADA (Gal et al. 2021
2.41 (0.89)
2.36 (1.17)
CLIPStyler Kwon & Ye 2022
2.08 (0.62)
1.81 (0.93)
Parmar et (2024)
Source Image
Parmar et al. (2024)
TICL refine
(Ours)
Figure 32: Visualisation of Day-to-night edits (Part 1). Transitioning images from day to night using the diffusion model.
F.2 Editing with diffusion models
Given that the previous baseline latent optimisation image editing method has limited capabilities, we extend our experiment to more recent editing method Parmar et al. (2024) using diffusion models (Ho et al. 20200 Rombach et al. 2021
Experiment setup & Ei to minimise the cosine distance between the time-aware emboddings of the output images and the (arget clock timestamp embeddings, which is written
Specifically, we optimise the edit target text embedding
E tentt
arg min dist (fo, (G (x, Exext)) for (ttarget))
Frest
where Ejet is the target text embeddings for the text-based image editing model G(:)) takes input image æ and guidance text embedding Fxext- fer corresponds TICL model components. dist(-.)) measures the cosine distance of two embeddings. essentially optimises the guidance text embeddings Etezt to achieve better editing results that visually align with the target time.
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Parmar (2024)
Source Image
Рarmar et al. (2024)
TICL refine (Ours)
Figure 33: Visualisation of Day-to-night edits (Part 2). Continuing results of time-aware editing using the diffusion model.
Parmar et al. (2024) TICL refine (Ours)
Source Images
Parmar (2024)
Figure 34: Visualisation of Day-to-night edits (Part 3). Further results demonstrating day-to-night transitions using the diffusion model.
As for hyper-parameters, we applied default experiment settings for the baseline editing process as provided in Parmar et al. 2024) with text guidance set to "a photo of [target time period } The subsequent optimisation process Erext uses Adam optimiser with learning rate 0.02 and 10 itcrations without any further configuration.
Fig, 33 and Fig. 34 although additional optimisation steps for each odit are required, refines the existing method with more reasonable synthesis results compared with using purely text editing guidance, further proving the general applicability of the TICL embeddings to the whole image-editing subfield.
Qualitative results: As shown Fig. 32
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Published in Transactions on Machine Learning Research (09/2025)
G Additional Text Queries on Clock Time Class Embeddings
In video scene classification tasks, we explored the semantic correlations between clock timestamps and scenes, and here we provide several examples to illustrate these connections. The Time Encoder and Image- Time Adaptor modules are designed to align visual CLIP representations and clock time embeddings. As a result, the learned time embeddings naturally align with CLIP text embeddings. This aligument allows factorise text concepts using TICL time embeddings and vice versa. Specifically, for each input text embedding, TCLIP, we compute their similarity with time-class embeddings, Ti, using the Softmax function:
exp (TeLIP- - TT)) [C-- 2== exp (TCLIP- T))
Softmax
where T; T the TICL class embeddings. This formulation offers probabillistc measure of the similarity between text embeddings and time classes. The resulting 24-hour class probabilities are shown in Fig. 35
The results clearly demonstrate that texts describing specific times of day are directly associated with corresponding time periods. In addition, we also observe indirect associations. For example, the word "breakfast" is by definition related to morning hours, while "thief" often associated with nighttime activities. These uneven probability distributions across the 24-hour timeline reflect the natural relations between certain events, scenes, concepts and their corresponding time periods.
However, some irregular trends in the probability distributions indicate that our time-aware embeddings, learned from limited image dataset, still have room for further improvements, particularly for night-time related concepts, which corresponds with fewer night-time image samples in the dataset. This highlights the need for further improvement of the datasct/mon model to achieve more robust time-awareness across all clock time periods.
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Published in Transactions on Machine Learning Research (09/2025)
Probabilities "Noon"
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Figure 35: Probability measure of the similarity between time classes and text queries. The x-axis is hour classes and y-axis is probabilities calculated by Softmax.
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Reviews:
[{"review_id": "qEM7IjZYsY", "reviewer": "Reviewer_uEWw", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Streghts:\n\n1. The idea of studying time-awared representation learning is interesting.  When reading the introduction, I had concern about the application value of the studied problem, which was clearly addressed in Section 6.  Esepcially, the application on time-aware image editing is impressive.\n\n2. This paper provides strong argument on the choice of learning framework.  It clearly illustrates the issues of regression-based methods, which often predict midpoint of the range, and the problems of classification-based methods, which overlook the structure between different timestamps.  Based on this observation, the paper proposes a contrastive learning framework, that leverages a strong pre-trained image encoder as prior and learns to distinguish images captured at different time.  The reasoning is technically sound and supported by the experimental results.\n\n3. This paper provides thorough analyses to validate the efficacy of the proposed method, including confusion matrices, time estimation performance, time-based image retrieval, video classification, joint localisation of geolocation and time, t-SNE visualisation comparison, and time-aware image editing.  I'm convinced by all these results.\n\n4. The authors put much effort in presenting different downstream tasks.  I'm convinced that the studied research problem indeed has real-world application value.\n\n5. The paper is clearly written.  Readers can easily grab the high-level idea, implementation details and conclusion of experimental results.\n\n---\n\nWeaknesses:\n\n1. My only concern is that time-image contrastive learning may not be the most optimal choice of time-aware learning framework.  Since all images captured at the same time are grouped to the same timestamp prototype, it overlooks that the same \"clock time\" has different physical meaning at different locations or seasons.  For example, 5 am may be considered late night during winter but early morning during summer.  The proposed method learns to predict similar features of images captures at 5 am in summer and winter, which definitely will sabotages the efficacy on downstream tasks (e.g. time-aware image editing).\n\nAn obvious alternative is supervised contrastive learning [1] that groups and separates images of similar and different categories in the latent feature space, rather than mapping images to the corresponding protype features.  Such relaxed contrastive learning may help the model to respect the appearance of input images and to distinguish images captured at different time, geolocation, season and weather.\n\n2. Following Weakness 1, an interesting idea is to normalize timestamps in different seasons.  For example, what time in winter would 5 am in summer correspond to?  Such results may help the scientific community to calibrate bio-clock of human beings.\n\n---\n\nReference\n\n[1] Hwang, Jyh-Jing, et al. \"Segsort: Segmentation by discriminative sorting of segments.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper studies time-awared representation learning.  Capturing time from input images is not merely detecting illumination changes from input images.  While varying time induces illumination changes in the visual scene, other factors such as weather, geolocation and season convolute the appearance of observed images.  This paper explores if existing performant image encoders, capable of distinguishing semantics, can also extracts time-of-day awareness.  The contribution of this paper is three-fold: (1) It curates a new benchmark dataset with reliable timestamps across a high variety of geolocations, (2) It proposes a time-image contrastive learning framework for extracting distinctive time-awared features from static images, and (3) It provides multiple examples of downstream tasks, demonstrating the application value of the studied problem.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. I'd encourage the authors to compare against Segsort--a supervised contrastive learning alternative.  Probably the authors can obtain more striking results with the relaxed contrastive learning.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "PRysuqNOy7", "reviewer": "Reviewer_VzWe", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n- Method is simple and works for a number of different tasks (e.g., time based image retrieval, geolocation estimation error, video scene classification, time-aware image editing)\n\n**Weaknesses**\n- The proposed method is too simple and technically not novel.\n\t- All components (contrastive loss, CLIP embedding) are very popular for co-embedding of data in two modalities.\n- In addition to Fig. 6, it is better to see the quantitative comparison of errors by a number as it is not clear to see the benefit of the method.\n- Performance of Time-aware image editing is hard to measure because it only shows a few qualitative examples.\n- Although it is clear that performance of time-based image retrieval is improved by better time-image co-embedding, it is not clear that the performance gain of other zero shot task. The paper has no good discussion on it.\n- Main text is not self-contained. A number of unclear presentation is clarified in appendix without pointer.\n\t- Cleaned TOC dataset (looks like A.2 is explaining the cleaning procedure)\n\t- In Figure 4, there is TICL-NN. But it is never explained in the main text (but in the appendix). The main text should have a pointer to the appendix explaining the TICL-NN.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "For the task of estimating time a photo is taken, the authors collect a dataset of 130,906 images with timestamps (and GPS coordinates). To address the task, the authors propose a contrastive learning objective to co-embed a CLIP embedding and a time vector into a single embedding space. Using the proposed dataset as the training set, the authors are able to achieve better test accuracy both on TOC and AMOS test sets. The authors show detailed experimental results by replacing the CLIP encoder with other encoders. By reducing the time estimation error, the method shows lower geolocation estimation error, higher video scene classification accuracy, time-aware image editing (Zero shot setting).", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- works -> work\n- e.g. -> e.g.,\n- \"Similarly, Salem et al. (2022)\" are marked as a hyperlink while \"Salem et al. (2022)\" only should be marked as a hyperlink.\n- Ablation study -> detailed study: The ablation study means that you need to remove each (proposed) component and observe the performance drop due to the absence of the proposed components. Here, you replace the CLIP encoder with other encoders to observe the performance difference. This is not an ablation study.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "UJ1dcTNzOx", "reviewer": "Reviewer_vBRf", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Positive Points: \nP1 - The paper is well written and the ideas are clearly presented. The figures are useful, and illustrative.\n\nP2 - The dataset TOC seems like an important contribution to the field of machine learning. I believe that in the topic of understanding the capabilities of ML, it might be beneficial to assess how training an LLM on another task might correlate with properly predicting the time of the day for example. In any case, I believe that in itself, this dataset is an important and useful contribution. \n\nNegative Points\nN1 - It seems like previous works have already explored the idea of learning to predict the time of the day. \nExamples are:\n\nAnna Volokitin, Radu Timofte, and Luc Van Gool. Deep features or not: Temperature and time prediction in outdoor scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1136–1144, 2016. doi: 10.1109/CVPRW.2016.145.\n\n\nN2 - I believe that the contribution of TICL is minor if any. Essentially, a CLIP model with an extra learnable layer is used to align with time via contrastive learning. This is not a significant contribution to the field of ML. Essentially, this same idea could be used to do any other task: predict the age of the person in the image, classify the element in the image, etc. In other words, the way the problem is solved is super standard. Even more so, the “contribution” of the work is to introduce the contrastive learning approach, as opposed to a classifier with 24 classes (or any number of classes associated with any times of the day). I cannot understand why a simple classifier – that would remove the time econder – is not a better solution. Also, why is clip used instead of training a CNN from scratch. \n\nN3 - The importance of the work is limited. Why would you want to train a model to predict the time of the day regardless of the position and the date? The variability is so high that it seems like a poorly posed problem. For most places sufficiently far away from the equator, the annual variability in sunlight is so large that very little if anything can be said about the time of the day regardless of the position and date. \n\nN4 - The results are not interpretable. In the numerical values of Tables 2, it is very difficult to make sense of what those numbers even mean, given that the problem (as I said before) has a lot of noise. What is for example the human baseline? What is the performance of say ChatGPT or any other multimodal contrastive model? \n\nN5 - The numerical experiments do not work very well. If we look at Figure 9, all of the underlying objects in the image change as time changes. In the bedroom for example, the sheet changes color. This clearly shows that the solution cannot be used for almost any real world application. This is a serious drawback for a paper that does not introduce any substantial theoretical or methodological ideas.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a dataset and a machine learning method to identify the time at which the image was taken. The Time-Oriented Collection (TOC) dataset contains 130906 images, each of which has its associated timestamp of when it was taken. This is in itself a very valuable contribution of this work. The second contribution is Time-Image Contrastive Learning (TICL), a method by which we learn how to predict the time of the image. In all the paper is well written, and the ideas are clearly presented. The main issues that I identify are (i) the contribution of TICL, (ii) the importance and significance of the work, (iii) the quality of the numerical experiments.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "See before.", "broader_impact_concerns": "See before.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "76PbdsjXAQ", "reviewer": "Reviewer_C7jQ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n* The paper is well-motivated and easy to follow.\n* The proposed model empirically performs well on several benchmarks.\n* The experiments and ablation study are comprehensive. The paper ablates different vision backbone / image encoder and time coordinate transformations, and also conducts study on tasks like temporal aware image editing.\n* The findings and analysis of why video scene classification can be improved via incorporating TICL are interesting.\n\nWeakness:\n\n* The proposed method is a combination of several well-established techniques (e.g. GeoClip), therefore the technical advancement could be limited. However, I think these combinations are well-motivated and supported by the ablation. The scope of the proposed method can be relatively limited as it only tackles hour prediction.\n* The discussion of the newly proposed dataset is quite short for the main body part. A more thorough comparison with existing dataset (e.g. what is the main gap being addressed in this dataset) would help clarify its significance and utility for the hour prediction task.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a time-aware contrastive learning framework that aligns visual features with temporal representations for predicting the time coordinates from images. The paper also proposes a newly curated dataset for timestamp prediction.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "I would like to see more discussions regarding the dataset, particularly what makes the new dataset useful and how is it different than previous works.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)