L EARNING WHAT AND WHERE TO ATTENDWITH HUMANS IN THE LOOP
Anonymous authors
Paper under double-blind review
A BSTRACT
Most recent gains in visual recognition have originated from the incorporation (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) inclusion (cid:58) of attention mechanisms in deep convolutional networks (DCNs).
Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels.
Here, we demonstrate the beneﬁt of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition.
We ﬁrst describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived “top-down” attention maps.
Using human psychophysics, we conﬁrm that the identiﬁed “top-down ” features from ClickMe are more diagnostic than “bottom-up” (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) saliency (cid:58) features for rapid image categorization.
As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding humans-in-the-loop with ClickMe supervision signiﬁcantly improves its accuracy , while also yielding (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) yields (cid:58) visual features that are more interpretable and more similar to those used by human observers.
I NTRODUCTION
Attention has become the subject of intensive research within the deep learning community.
While biology is sometimes mentioned as a source of inspiration (Stollenga et al., 2014; Mnih et al., 2014;
Cao et al., 2015; You et al., 2016;
Chen et al., 2017; Wang et al., 2017; Biparva and Tsotsos, 2017), the attentional mechanisms that have been considered remain limited in comparison to the rich and diverse array of processes used by the human visual system (see Itti et al., 2005, for a review).
In addition, whereas human attention is driven by varying task demands, attention networks are solely optimized for object recognition.
This means that, unlike infants who can rely on a myriad visual cues to learn to focus their attention (Itti et al., 2005), DCNs must solve the challenging problem of learning where to attend from weak supervisory signals derived from statistical associations between image pixels and class labels.
Here, we investigate how explicit human supervision – to teach (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) teaching (cid:58) DCNs where to attend – affects their performance and interpretability.
1. R ELATED WORK
[Table] has
[Table] posited (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58) there [Table] are
[Table] that [Table] [Table]
features (cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table] the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) entire (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) scene.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) These (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) representations (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) designed (cid:58)(cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) capture (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) scene (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) layout (cid:58)(cid:58) or (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) “gist”, (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) hypothesized (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) capture (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) contextual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) information (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) guides (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) task-relevant (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Oliva and Torralba, 2007). (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) This (cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) most (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) similar (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) feature-based (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58) used (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58) most (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) state-of-the-art (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) networks (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
(Bell et al., 2016; Wang et al., 2017; Hu et al., 2017) (cid:58) .
(cid:58)(cid:58) A (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) complementary (cid:58)(cid:58)(cid:58)(cid:58) form (cid:58)(cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) known (cid:58)(cid:58) as (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) saliency (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Itti and Koch, 2001) (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) derived (cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table] (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ﬁltering (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) clutter (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) detection (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) processes (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Torralba et al., 2006). (cid:58)
Attention networks In addition to extensive work aimed at explicitly predicting human eye ﬁxations and/or detecting objects (see Nguyen et al., 2018, for a review), much recent work (cid:58)(cid:58)(cid:58)(cid:58) recent [Table] has [Table]
on the integration of
[Table]
integrating [Table] attention modules within end-to-end trainable deep network architectures. These attention modules fall into two broad categories
[Table] are
[Table] a [Table] mask that enhances/suppresses the activity of units inside/outside a “spotlight" positioned over a scene.
This is done according to units’ spatial location independently of their feature tuning.
Such mechanisms have been shown to signiﬁcantly improve performance on visual question answering and captioning (e.g., Nam et al., 2017; Patro and Namboodiri, 2018). Feature-based attention (also called “channel-wise” attention) is a complementary form of attention which involves learning a task-speciﬁc modulation that is applied across an entire scene to adjust feature maps. In this work,
[Table] Here, (cid:58)(cid:58)(cid:58) we (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) combine (cid:58) spatial- and feature-based attention are combined into a single mask that modulates feature representations, as is typically done in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) similarly (cid:58)(cid:58) to (cid:58) state-of-the-art approaches (e.g., Chen et al., 2017;
Wang et al., 2017; Biparva and Tsotsos, 2017; Park et al., 2018; Jetley et al., 2018), [Table] with
[Table] that [Table] additionally
[Table] supports [Table] Humans-in-the-loop computer vision A central goal of the present study is to leverage human supervision to co-train an attention network.
Previous work has shown that it is possible to augment vision systems with human perceptual judgments on many difﬁcult recognition problems (e.g.,
Vondrick et al., 2015; Shanmuga Vadivel et al., 2015; Kovashka et al., 2016;
Deng et al., 2016).
In particular, online games, especially multi-player games, constitute an efﬁcient way to collect high-quality human ground-truth data (e.g., von Ahn et al., 2006; Deng et al., 2016; Das et al., 2016;
Linsley et al., 2017).
In a two-player game, an image may be gradually revealed to a “student” tasked to recognize it, by a remote “teacher” who draws “bubbles” on screen to unveil speciﬁc image parts (Linsley et al., 2017). The need to assemble [Table]
by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) assembling (cid:58) teams of players reduces annotation noise in these games, but also greatly limits their suitability for large-scale data collection.
This can be alleviated (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) limitation (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) aleviated in one-player games (Deng et al., 2013; 2016; Das et al., 2016), where a single player may be asked to sharpen regions of a blurred image to answer questions about it.
This work differs from (cid:58)(cid:58)(cid:58)(cid:58) The (cid:58)(cid:58)(cid:58)(cid:58) game (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) introduced (cid:58)(cid:58)(cid:58)(cid:58) here (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) differs (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)(cid:58) these earlier studies (Linsley et al., 2017) in that it has a human player collaborate with a DCN to discover important visual features for recognition at ImageNet scale.
Attention datasets Recording eye ﬁxations while viewing a stimulus is a classic way to explore visual attention (e.g., Judd et al., 2009).
It is, however, difﬁcult and costly to acquire large-scale eye tracking data, leading researchers instead to track computer mouse movements during task-free viewing of images (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) image (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) viewing.
Maps collected from individual observers can then be combined into a single “bottom-up” saliency map, (cid:58) as is done for the popular Salicon dataset (Jiang et al., 2015).
Still, Salicon contains saliency maps for 10k images, which is probably at least one order of magnitude short of the number of samples needed for co-training a deep network in object recognition.
We instead describe a gamiﬁcation procedure used to collect nearly a half-million “top-down” (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (task-driven) attention maps over several months.
As we demonstrate through psychophysics, our “top-down ” attention maps collected as human observers are actively engaged in identifying features that are maximally informative about object category better reﬂect the observer’s (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) identifying (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maximally [Table] Salicon (section 2. [Table]
1. C ONTRIBUTIONS
Our contributions are three-fold: ( i)
We describe the large-scale online experiment ClickMe. ai to supplement ImageNet with nearly a half-million “top-down ” attention maps derived from human participants.
These maps are validated using human psychophysics and shown to be more diagnostic than “bottom-up ” attention maps for rapid visual categorization.
( ii)
As a proof of concept, we describe an extension of the leading squeeze-and-excitation (SE) module, which we call the global-and-local attention (GALA) module because it combines global contextual guidance with local saliency.
This extension yields substantial gains in accuracy on ILSVRC12.
( iii) Putting humans-in-the-loop with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Incorporating (cid:58)
ClickMe supervision leads to an even larger gain in accuracy while also creating visual representations that are more interpretable and more similar to those derived from human observers.
By supplementing ImageNet with the public release of ClickMe attention maps, we hope to spur interest in the development of network architectures that are not only more robust and accurate, but also more interpretable and consistent with human vision.
C LICK M E . AI
[Table] Our starting [Table]
this [Table] endeavor (cid:58) is the Clicktionary game introduced by Linsley et al.
(2017), which pairs online human participants to cooperatively annotate object images.
This twoplayer game was successfully used to collect attention maps for a few hundred images [Table] but we found it impossible to scale up the number of maps collected beyond that because of the challenge of matching pairs of players. [Table]
These [Table] us [Table]
to
[Table]
develop ClickMe.ai is (cid:58) , an adaptation of Clicktionary as
[Table]
into (cid:58) a single-player game , which
[Table]
that
[Table] data (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) acquisition. (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Indeed, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58) successfully ran for several months and produced nearly a half-million attention maps.
In brief, ClickMe consists of rounds of game play where human participants play with DCN partners to recognize images from the ILSVRC12 challenge.
Players viewed object images and were instructed to use the mouse cursor to “paint” image parts that are most informative for recognizing its category (written above the image).
Once the participant clicked on the image, 14 × 14 pixel bubbles were placed wherever the cursor went until the round ended (Fig. 1).
Having players annotate in this way forced them to carefully monitor their bubbling while also preventing fastidious strategies that would produce overly sparse salt-and-pepper types of maps.
Participants select [Figure] parts with their mouse by “painting” translucent bubbles on screen.
At the same time, image parts of roughly double the size are shown to a DCN partner, tasked to recognize the image.
Each round lasts until the DCN recognizes the object or if 7 seconds have passed (the latter occurred 47% of the time).
As players bubbled object parts they deemed important for recognition, a DCN tried to recognize a version of the image where only these bubbled parts were visible.
We tried to make the game as entertaining and fast-paced as possible to maximize the number of rounds played by the human players.
Hence, we nearly doubled the size of the bubbled regions shown to the DCN (21 × 21 pixels) to make sure that the objects would be visible to the DCN within a few seconds of play (Fig. 1).
Thus, we do not expect the precise bubble locations to inﬂuence the timing or the accuracy of the DCN response.
The reason for keeping a DCN in the loop (as opposed to a random timer) was (1) to make the game entertaining, and (2) to discourage human players from bubbling random locations in an image which are potentially unrelated to the object. Indeed, we have incorporated all the images used in Clicktionary
Linsley et al. (graciously provided by 2017) [Table] (graciously provided by Linsley et al., 2017) and found that ClickMe maps are strongly correlated with Clicktionary maps (see Appendix).
This suggests that the use of a DCN as a player did not bias the collected annotations, but it did allow us to collect human data at a scale suitable for co-training a DCN with humans in the loop. (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) human [Table]
attention [Table] annotations. 1 A timer controlled the number of points participants received per image, and high-scoring participants were awarded prizes (see Appendix).
Points were calculated as the proportion of time left on the timer after the DCN achieved top-5 correct recognition for the image.
If the player could not help the DCN recognize the object within 7 seconds, the round ended and no points were awarded.
2. G AME STATISTICS
Data collection efforts on ClickMe.ai drew 1,235 participants (unique user IDs) to the game who played an average of 380 images each.
In total, we recorded over 35M bubbles, producing 472, ClickMe maps on 196,499 unique images randomly selected from our preparation of ILSVRC (see Appendix for details).
All ClickMe maps were used in subsequent experiments, regardless of the ability of the DCN partner to correctly identify them within the time limit of a round.
Fig.
2A shows sample ClickMe maps where pixel opacity is set according to how many times a bubble was overlaid on them over all rounds of game play where these images were presented.
The maps typically highlight local image features, emphasizing certain object parts over others.
For instance, ClickMe maps for animal categories (Fig.
2A, top row) are nearly always oriented towards facial components even when these are not prominent (e.g., snakes).
In general, we also found that ClickMe maps for inanimate objects (Fig.
2A, bottom row) tended to exhibit a front-oriented bias, with distinguishing parts such as engines, cockpits, and wheels receiving special attention.
Additional game information, statistics and ClickMe maps are available as Appendix.
2. C LICK M E AND OBJECT RECOGNITION
To directly test the role that ClickMe map features play in human object recognition, we ran a rapid visual recognition experiment (Figure 2B). This experiment compared (cid:58)(cid:58)(cid:58)(cid:58) Rapid
[Table] experiments [Table]
have
[Table] classically [Table] been
[Table] used [Table] We tested human (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) image (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) saliency (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
(Figure (cid:58)(cid:58)(cid:58)(cid:58) 2B), (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) closely (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) following (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) approach (cid:58)(cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Eberhardt et al., 2016) (cid:58) . (cid:58)(cid:58)(cid:58) If (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) human (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) participants (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) were (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
object (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) recognition. (cid:58)(cid:58)(cid:58) Our (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) design (cid:58)(cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) experiment (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) followed (cid:58)(cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) rapid (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) categorization (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) paradigm (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) used (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Eberhardt et al. (2016) (cid:58)(cid:58)(cid:58)(cid:58) where (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) stimuli (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ﬂashed (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) responses (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) forced (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58) be (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) rapid (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (under (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ms; (cid:58)(cid:58)(cid:58)(cid:58) see (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Appendix (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table] groups (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ( N = 60 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) participants (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) each): (cid:58)(cid:58)(cid:58)(cid:58) one (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) viewed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58) were (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) masked (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) according (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps, (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table] participant responses on 40 target (animal) and 40 distractor (non-animal) images gathered from the Salicon (Jiang et al., 2015) subset of the Microsoft COCO [-
-]2014 (Lin et al., 2014) because it includes “bottom-up ” attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) saliency (cid:58) maps derived from human observers(see section 1.1).
Images were presented to human participants either intact or with a phase scrambled perceptual mask which selectively exposed their most important visual features according to attention maps derived from either ClickMe or Salicon.
These maps were ﬁrst processed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Attention (cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)(cid:58) both (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) resources (cid:58)(cid:58)(cid:58)(cid:58) were (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) preprocessed (cid:58)(cid:58)(cid:58) so (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) there (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) spatial (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) continuity (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) pixels (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) covering (cid:58)(cid:58)(cid:58)(cid:58) their (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) most-to-least (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) important (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) This (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ensured (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) low-level (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) difference (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) between (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps, (cid:58)(cid:58)(cid:58)(cid:58) such (cid:58)(cid:58) as (cid:58)(cid:58) [Table] [Table]
Preprocessing (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58) done (cid:58) with a novel “stochastic” ﬂood-ﬁll algorithm that relabeled (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58) map (cid:58) pixels with a score that combined their distance from the most important pixel with their labeled importance (see Appendix for details.)This ensured a spatially continuous expansion from most-to-least important pixel, which let us create (cid:58)(cid:58) ). (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
Next, (cid:58)(cid:58)(cid:58) we (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) created versions of each image that revealed between 1% and 100% (at log-scale spaced intervals) of its most important pixels, and record how introducing additional features from a resource (cid:58) . [Table] see thumbnails in 2A for examples of [Table]
of ClickMe or Salicon features
were revealed) [Figure] [Figure] [Figure]
[Figure] [Table] maps (red curve). ClickMe- and Salicon-masked image exemplars are shown for the condition in which 100% of important features are visible, demonstrating how “bottom-up” saliency is not necessarily relevant to the task.
For clarity, we omitted data between 1-10% of features visible from this plot where accuracy was chance for participants of both groups. Error bars are S.E.M. ***: p <0.001. We followed the rapid categorization paradigm used in (Eberhardt et al., 2016) where stimuli are ﬂashed and responses are forced to be rapid (under 550 ms; see Appendix for details). We recruited 120 participants from Amazon Mechanical Turk ().
Participants were organized into two groups ( N = 60 participants in each), each of which viewed images that were masked to reveal a randomly selected amount of the most important visual features from either ClickMe or Salicon maps.
Results are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Experiment (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) results (cid:58)(cid:58)(cid:58) are shown in Fig.
2B:
Human observers reached ceiling performance when 40% of the ClickMe features were visible (6% of all image pixels).
In contrast, human observers viewing images masked according to Salicon required as much as 63% of these features to be visible, and did not reach ceiling performance until the full image was visible (accuracy measured from different participant groups).
These ﬁndings validate that visual features measured by ClickMe are distinct from “bottom-up ” saliency and sufﬁcient for human object recognition.
P ROPOSED NETWORK ARCHITECTURE
We designed the global-and-local attention (GALA) block as a circuit for learning complex combinations of local saliency and global contextual modulations that can (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) optionally (cid:58) be supervised by ClickMe maps.
The rational for the proposed GALA architecture (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) design, (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) particular (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parallel (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) pathways, (cid:58) is grounded in visual neuroscience(see Appendix for a brief overview. ) (cid:58) , (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) detailed (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Related (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Work.
(cid:58) Below we sketch the main computational elements of the architecture. , (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) describe (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) process (cid:58)(cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58)
it (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58) used (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) modulate (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) activity (cid:58)(cid:58) at (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58) layer (cid:58)(cid:58) in (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58) DCN (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) model (cid:58)(cid:58)(cid:58)(cid:58) (see (cid:58)(cid:58)(cid:58) Fig. (cid:58)(cid:58) 3 (cid:58)(cid:58)(cid:58) for (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
high- [Table]
3. A RCHITECTURE
GALA modulates an input layer [Table] activity U with a (cid:58)(cid:58) an [Table]
attention (cid:58) mask A of the same dimension as the input (cid:58) , [Table] which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
of [Table] attention. Here, the spatial height, width, and number of feature channels are denoted as H , W , and C s.t.
U , A ∈ R H × W × C . We begin with the [Table] We
[Table] begin [Table] by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) describing (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) global (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) based (cid:58)(cid:58)(cid:58) on (cid:58)(cid:58)(cid:58) the (cid:58) SE module (Hu et al., 2017), which is denoted F global (cid:58) .
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Global (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) denoted (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) F global (cid:58) in our model and yields the global feature attention vector g (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) g ∈ R 1 × 1 × C (cid:58) (Fig. 3).
This procedure involves two steps: ﬁrst, calculating per-channel “summary statistics”; and second, applying transformations a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) multilayer (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) perceptron (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (MLP) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) transformation (cid:58) to shrink and then expand the dimensionality of these statistics.
Summary statistics are computed with a global average applied to individual feature channels (cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) vector (cid:58) U =
[ u k ] k =1 ...C , yielding (cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) yield (cid:58) the vector p = ( p k ) k
=1 ...C .
(cid:58)(cid:58)(cid:58)(cid:58) That (cid:58)(cid:58)(cid:58) is, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table]
applied (cid:58)(cid:58) to the vector p by the operator W shrink ∈ R cr × C (so-called “squeeze”) (cid:58) , (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) mapping (cid:58)(cid:58) it (cid:58) into a lower dimensional space, (cid:58) . (cid:58)(cid:58)(cid:58)(cid:58)
This (cid:58)(cid:58) is (cid:58) followed by an expansion operation (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (“excite” from Hu et al., 2017)
W expand ∈ R C
× cr (bias terms are omitted for simplicity) back to the original, higher dimensional space s.t.
g
=
W expand ( δ ( W shrink ( p ))) .
We set δ to be a rectiﬁed linear function (ReLU) and the dimensionality “reduction ratio” r of the shrinking operation to 4. As an (cid:58)(cid:58)
In addition to the SE/global contextual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58) module, we consider a local saliency module F local used to compute (cid:58)(cid:58)(cid:58)(cid:58) F local (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) computing the local feature attention vector S (Fig. 3) as S =
V collapse ∗ ( δ ( V shrink ∗ U )) .
Here, convolution is denoted with ∗ , V shrink ∈ R 1 × 1 × C × cr , and V collapse ∈ R 1 × 1 × cr × 1 .
This is reminiscent of the local computations performed in computationalneuroscience models of visual saliency to yield per-channel conspicuity maps that are then combined into a single saliency map (Itti and Koch, 2001).
Outputs from the local and global pathways are further integrated with F integrate (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) integrated (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) F integrate (cid:58) to produce the attention volume A ∈ R H × W × C .
Because it is often unclear how tasks beneﬁt from one form of attention vs. another, or whether task performance would beneﬁt from additive vs. multiplicative combinations, F integrate (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) F integrate (cid:58) learns parameters that govern these interactions.
The vector ( a c ) c ∈ 1 .. C controls {+the additive combination of S and g per-channel, while ( m c ) c ∈ 
+}
C does the same for their multiplicative combination.
In order to combine attention activities g and S , they are ﬁrst tiled to produce G ∗ , S ∗ ∈ R H × W × C .
Finally, we calculate the attention activities of a GALA module as A h,w,c
= ζ (cid:0) a c ( G ∗ h,w,c + S ∗ h,w,c ) + m c ( G ∗ h,w,c · S ∗ h,w,c ) (cid:1) , where the activation function ζ is set to the tanh function, which squashes activities in the range [ − 1 , 1] . In contrast to other bottom-up attention modules, which use a sigmoidal function to implement a soft form of excitation and inhibition, our selection of tanh gives a GALA module the additional ability to dis-inhibit bottom-up feature activations from U and ﬂip the signs of its individual unit responses.
Attention is applied by F scale (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) F scale (cid:58) as U (cid:48) =
U (cid:12) A . 3. R ES N ET -50 IMPLEMENTATION To validate (cid:58)(cid:58) We (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) validated (cid:58) the GALA approach , we embedded (cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) embedding (cid:58) it within a ResNet50 (He et al., 2016). We identiﬁed 6 mid- to high-level feature layers in ResNet-50 to use with GALA (layers 24, 27, 30, 33, 36, 39; each belonging (cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) belong (cid:58) to the same ResNet-50 processing block).
(cid:58) , (cid:58)(cid:58)(cid:58)(cid:58) since (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58) will (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) principle (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) encode (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visual (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) qualitatively (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) similar (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object-parts (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) highlighted (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
ClickMe (cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58) example of such (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parts).
Each GALA module was applied to the ﬁnal activity in a dense path of a residual layer (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) module in ResNet-50.
(cid:58)(cid:58)
At (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) depth (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)
the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ResNet, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) activity (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58) had (cid:58) a
[Table] 14 × 14. The residual layer’s “shortcut” activity maps were added to this GALA-modulated activity to allow the model to more [Table]
ResNet (cid:58)(cid:58) to (cid:58) ﬂexibly adjust the amount of attention applied to feature activities. Each attention activity map had a height and width of 14 × 14. Table 2 in the Appendix shows that the accuracy of our re-implementations of ResNet-50 (He et al., 2016) and SE-ResNet-50 (Hu et al., F global ( × ) 1 x 1 x C U
H
C
W H C W
A
W
F scale g
H
C
U
H
W
F local ( ∗ )
S (optional) clickmaps
H
W
M ­ H
W
A
F integrate local (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ( F local ) [-c ∈ 
 C does the same for their multiplicative combination.-](cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) global (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
( F global ) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) operators (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) derive (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) local-
(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) global-attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) activity, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) integrated (cid:58)(cid:58)(cid:58)(cid:58) into (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table]
A (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) module (cid:58)(cid:58)(cid:58) can (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) optionally (cid:58)(cid:58)(cid:58) be (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervised by ClickMe maps ( (cid:58)(cid:58) M (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58) the (cid:58) yellow box)can drive (cid:58) , (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) through (cid:58)(cid:58) an (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) additional (cid:58)(cid:58)(cid:58) loss (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) L clickmaps , (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) drives (cid:58) attention to visual features favored by humans (cid:58)(cid:58)(cid:58) (see (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Section
[Table]
[Table]
2017) trained with randomly initialized weights on ILSVRC12 is on par with published results.
Incorporating our proposed GALA module into the ResNet-50 ( GALA-ResNet-50 no ClickMe ) offers a small improvement over the SE-ResNet-50.
As we will see in section 4, the beneﬁts of GALA are much greater on smaller datasets and when we add humans in the loop (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision.
C O - TRAINING GALA WITH HUMANS IN THE LOOP (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
C
[Equation] M E (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) MAPS (cid:58)(cid:58) To (cid:58)(cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) point, (cid:58)(cid:58)(cid:58)(cid:58) we (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) have [Table]
interpretability.
(cid:58) Next, we describe how to (cid:58)(cid:58)(cid:58)
We use ClickMe maps to supervise attention in a GALA module (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) module (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58) by introducing an additional loss.
Let L C denote the cross-entropy between activity from model M with input X and class label y , and R l ( X ) denote the ClickMe map for this input.
ClickMe maps are resized with bicubic interpolation to be the same height and width as a GALA module activity
A l ( X ) at layer l ∈ L , the set of layers where the GALA module is applied.
We (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) denote (cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) resized (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58) map (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) input (cid:58)(cid:58) as (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) R l ( X ) . (cid:58)(cid:58)(cid:58)(cid:58)
We reduced the depth of each column in A l ( X ) to 1 by setting them to their column-wise L 2 norm. Units in ClickMe maps (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
R l ( X ) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps) (cid:58) and A l ( X ) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) activity) (cid:58) are transformed to the same range by normalizing each by their L 2 norms.
ClickMe map supervision for a GALA module is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) scaled (cid:58)(cid:58) by (cid:58)(cid:58) λ (cid:58)(cid:58)(cid:58) and (cid:58) combined with cross-entropy into a global loss:
[Equation] [Table]
[Table] [Table] [Table]
[Table] maps (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) exceed (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58) null (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) inter-participant (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) reliability (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) score; (cid:58)(cid:58)(cid:58) see (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Appendix (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) details). This formulation jointly optimizes a model for both object classiﬁcation and predicting ClickMe maps from input images . (cid:58)(cid:58)(cid:58) (the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) latter (cid:58)(cid:58)(cid:58) loss (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) referred (cid:58)(cid:58) to (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)
4. M ODEL EVALUATION
We split up (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) evaluated (cid:58)(cid:58)(cid:58)(cid:58) our (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) approach (cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervising (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) partitioning (cid:58) the ClickMe dataset for model trainingand evaluation, with (cid:58)(cid:58)(cid:58) into (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table] approximately [Table]
[Table] and [Table] ), another
[Table] [Table] and [Table]
and the rest [Table] [Table] Each ClickMe split contained exemplars from all
[Table] performance (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) interpretability. (cid:58)
4. T RADE - OFF BETWEEN RECOGNITION PERFORMANCE AND C LICK M E MAP PREDICTION
We investigated the trade-off between maximizing object categorization accuracy and predicting
ClickMe maps (i.e., learning a visual representation which is consistent with that of human observers).
We performed a systematic analysis over different values of the hyperparameter λ , which scaled the magnitude of the ClickMe map loss (Eq. 1), while recording object classiﬁcation accuracy and the similarity between ground-truth ClickMe maps and model attention maps (Fig. 7 in Appendix).
This analysis demonstrated that both object categorization and ClickMe map prediction improve when λ = 6 .
We use this hyperparameter value to train GALA-ResNet-50 with ClickMe maps in subsequent experiments.
4. M ODEL ACCURACY
We [Table] split (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) dataset (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Table (cid:58)(cid:58)(cid:58) 1). (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
Here, (cid:58)(cid:58)(cid:58) we report classiﬁcation accuracy and ClickMe map predictions (measured as the fraction of explained human ClickMe map variability (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) explained (cid:58)(cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) feature (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58) at (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) model (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) layer (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) where (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
objects. We found that the GALA-ResNet-50 was more accurate at object classiﬁcation than either the ResNet50 or the SE-ResNet-50.
We also found that all models that incorporated attention were better at predicting ClickMe maps than a baseline ResNet-50. The most notable gains in performance came when ClickMe maps were used to supervise GALA-
ResNet-50 training
[Table] which improved both (cid:58) its classiﬁcation performance and the model’s predictions of ClickMe maps
[Table]
We veriﬁed the effectiveness of ClickMe maps for co-training GALA with two controls. Our ﬁrst control included a GALA network trained , (cid:58)(cid:58)(cid:58)(cid:58) one (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) tested (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) detailed
[Table]
feature [Table] ClickMe (cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58) (see (cid:58)(cid:58)(cid:58)(cid:58) Fig. (cid:58)(cid:58)(cid:58) 2A (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) examples (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58) how (cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) emphasize (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) certain (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58)(cid:58) over (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) others), (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) while (cid:58)(cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58) other (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table] we (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58) on coarse [Table]
[Table] on these were generated). The second tested if ClickMe maps could directly improve feature learning in , without the aid of the GALA module (see Appendix for details on the
In both [Table] we found that the GALA-ResNet-50 trained with ClickMe maps outperformed the (Table 3 in
In [Table] performance [Table]
As an additional analysis [Table] we tested if ClickMe maps could still improve model performance if they were only available for a subset of the training set.
We trained models on the entire ILSVRC training set and provided ClickMe supervision on images for which it was available (test accuracy was measured on the ILSVRC12 validation set).
Here too , the GALA-ResNet-50 with ClickMe map supervision was more accurate than all other models (Table 4 in Appendix).
4. M ODEL INTERPRETABILITY
Because GALA-ResNet-50 networks were trained “from scratch” on the ClickMe dataset, we were able to visualize the features selected by each for object recognition.
We did so on a set of 200 images that was not included in ClickMe training, for which we had multiple participants supply ClickMe maps.
We visualized features by calculating “smoothed” gradient images (Smilkov et al., 2017), which suppresses visual noise in gradient images.
Including ClickMe supervision in GALA-ResNet50 training yielded gradient images which highlighted features that were qualitatively more local and consistent with those identiﬁed by human observers (Fig. 4), emphasizing object parts such as facial features in animals, and the tires and headlights of cars.
By contrast, the GALA-ResNettrained without ClickMe maps placed more emphasis on the bodies of animals and cars as well as their context.
Our ClickMe map loss formulation requires reducing the dimensionality of the GALA attention volume A to a single channel.
We can directly visualize these “reduced attention maps” to see the image locations GALA modules learn to select (Fig. 4).
Strikingly, attention in the GALA-ResNettrained with ClickMe maps, virtually without exception, focuses either on a single important visual feature of the target object class, or segments the ﬁgural object from the background.
This effect persists in the presence of clutter and occlusion (Fig. 4, fourth and last row of GALA w/ ClickMe maps).
In comparison, some object features can be made out in the attention maps of a GALAResNet-50 trained without ClickMe maps, but there is no such localization, and the maps themselves are far more difﬁcult to interpret. [Table]
to
[Table] between (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) models, (cid:58)(cid:58) as (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Edgington (1964))
Attention in the GALA-ResNet-50 trained with ClickMe supervision also segmented objects when it was tested on (cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) capable (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) selecting (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) objects (cid:58)(cid:58) in (cid:58) images from a different dataset than ILSVRC.
Without additional training, the model’s attention localized foreground objects (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58) in Microsoft COCO
(Lin et al., 2014), despite the qualitative differences between this dataset and ILSVRC (multiple object categories, higher resolution than ILSVRC12). (cid:58) ; (cid:58)(cid:58)(cid:58) see (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Fig. (cid:58)(cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) additional (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) exemplars). (cid:58)(cid:58)(cid:58)(cid:58)
We (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) quantiﬁed (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) interpretability (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58) on (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subset (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table] intersection-over-union (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (IOU) (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58) an (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58) map (cid:58)(cid:58) for (cid:58)(cid:58)(cid:58) an (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) image (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58) its (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ground (cid:58)(cid:58)(cid:58)(cid:58) truth (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) segmentation (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) masks (cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) COCO (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Zhou et al., 2017).
(cid:58)(cid:58)(cid:58)(cid:58)
By (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) metric, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58)
the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) signiﬁcantly (cid:58)(cid:58)(cid:58)(cid:58) more
[Table]  D ISCUSSION
We have described a novel global-and-local attention (GALA) module and demonstrated the beneﬁt of putting humans-in-the-loop (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) using (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision to teach DCNs where to attend.
We ﬁrst described the
M i ss il e
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no maps
A
[Figure]
the ﬁnal ResNet layer in which GALA is applied (#39).
collection of the ClickMe dataset aimed at supplementing ImageNet with nearly a half-million humanderived attention maps.
The approach was validated using human psychophysics by demonstrating the sufﬁciency of ClickMe features for rapid visual categorization: on average, human observers were able to reach ceiling accuracy with only 6% of the total pixels made visible derived from the most selected ClickMe locations.
These results indicate that ClickMe.ai may also provide novel insights into human vision with a measure of feature diagnosticity that goes beyond classic saliency measures.
While a detailed analysis of the ClickMe features falls outside the scope of the present study, we expect a more systematic analysis that moves beyond identifying what features are selected to also measure when they are selected (Cichy et al., 2016;
Ha and Eck, 2017) will aid our understanding of the different attention mechanisms responsible for the selection of diagnostic image features.
We release all the ClickMe data, including not only nearly a half-million attention maps, but also the associated timing of human behavioral decisions, with the hope that it will spur interest from other researchers.
We also extended the squeeze-and-excitation (SE) module which constituted the building block of the winning architecture in the ILSVRC17 challenge. We tested [Table]
trained an SE-ResNet-50 with (cid:58)(cid:58) on (cid:58)(cid:58) a reduced amount of training data ( ∼ 300 K samples) and found that the architecture overﬁts compared to a standard ResNet-50.
We have found that the proposed GALA-ResNet-50, however, signiﬁcantly increases accuracy in this regime and cuts down top-5 error by ∼ 25% over both ResNet50 and SE-ResNet-50.
In addition, we described an approach to co-train GALA using ClickMe supervision and cue the network to attend to image regions that are diagnostic to humans for object recognition.
The routine casts ClickMe map prediction as an auxiliary task that can be combined with a primary visual categorization task.
We found a trade-off between learning visual representations that are more similar to those used by human observers vs. learning visual representations that are more optimal for ILSVRC. With the (cid:58)(cid:58)(cid:58) The (cid:58) proper trade-off , the approach resulted in a model with visual representations that are more interpretable in addition to exhibiting a robust improvement in
[Table]
While recent advancements in DCNs have led to models that perform on par with human observers in basic visual recognition tasks [Table] there is also growing evidence of qualitative differences in the visual strategies that they employ (Saleh et al., 2016; Ullman et al., 2016; Eberhardt et al., 2016; Linsley et al., 2017)
[Table]
It remains an open question whether these discrepancies arise because of mechanistic differences during visual inference or because of more mundane differences in the way they are trained.
That it is possible to drive a modern DCN towards learning more human-like representations with proper cuing to diagnostic image regions during training suggests that the observed differences may reﬂect different training regimens rather than different inference strategies.
In particular, DCNs lack explicit mechanisms for perceptual grouping and ﬁgure-ground segmentation which are known to play a key role in the development of our visual system (Johnson, 2001; Ostrovsky et al., 2009).
Such processes alleviate the need to learn to discard background clutter through statistical regularities learned via the presentation of millions of training samples as is the case for DCNs.
In the absence of ﬁgure-ground mechanisms, DCNs are compelled to associate foreground objects and their context as single perceptual units.
This leads to DCN representations that are signiﬁcantly more distributed compared to those used by humans (Linsley et al., 2017).
We hope that this work will catalyze interest in the development of novel training paradigms that leverage the plethora of visual cues (depth, motion etc) available for ﬁgure-ground segregation in order to substitute for the human supervision used here for co-training GALA
A PPENDIX A DDITIONAL C LICK M E STATISTICS
The game was launched on February 1st, 2017 and closed on September 24th, 2017.
Over this period, 25 contests were used to drive trafﬁc to the site by rewarding top-scoring players with gift cards.
Participants were given usernames to track their performance and were allowed to play as many game rounds as they wanted.
More than 90% of these participants played more than one image.
The distribution of number of participants per image is shown in Fig.
Around 5% of the images were skipped by participants because of poor image quality or an incorrect class label.
The CNN correctly recognized object images in about half of the trials that were played (47% of all images) for which participants received points.
In total, we recorded over 35M bubbles, producing 472,946 ClickMe maps on 196,499 unique images.
ClickMe maps typically highlight local image features, emphasizing certain object parts over others (Fig. 6).
For instance, ClickMe maps for animal categories (top row) are nearly always oriented towards facial components even when these are not prominent (e.g., snakes).
In general, we also found that ClickMe maps for inanimate objects tended to exhibit a front-oriented bias, with distinguishing parts such as engines, cockpits, and wheels receiving special attention.
I NTER - RATER RELIABILITY OF THE C LICK M E MAPS
We ﬁrst veriﬁed that despite the large scale of ClickMe, the collected attention maps displayed strong regularity and consistency across participants.
We calculated the rank-ordered correlation between
ClickMe maps from two randomly selected players for an image.
These maps were blurred with a 49x49 kernel (the square of the bubble radius in the ClickMe game) to facilitate the comparison and reduce the inﬂuence of noise associated with the game interface.
Repeating this procedure for 10,000 different images and taking the average of these per-image correlations revealed a strong average inter-participant reliability of ρ = 0 .
58 ( p <0.001), meaning that the kinds of features participants bubbled during game play tend to be stereotyped.
We report the similarity between a model’s feature attention maps and humans as a ratio of this value ρ
[Table]
, and refer to this as the “Fraction of human ClickMe map variability”.
We also derived a null inter-participant reliability by calculating the correlation of ClickMe maps between two randomly selected players on two randomly selected images.
Across 10,000 randomly paired images, the average null correlation was ρ r = 0 .
18 , reinforcing the strength of the observed reliability.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Below, (cid:58)(cid:58)(cid:58) we (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) calculate (cid:58)(cid:58) p (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) values (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) correlations (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) between (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) model (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58) as (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
R ELIABILITY BETWEEN C LICK M E AND C LICKATIONARY MAPS
ClickMe was inspired by the Clicktionary game (Linsley et al., 2017), which has two human partners play together to identify important visual features.
A small set of 10 images from that game were provided to us by the authors [Table] and also used in ClickMe.
A comparison of the reliability in attention maps for these images between the two games supports an evaluation of the extent to which ClickMe models the mechanics of Clicktionary.
The correlation between ClickMe and Clicktionary maps for these 10 images was high ( ρ r = 0 . 59 , p <0.001 (cid:58) ; [Table]
testing (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) randomization [Table] tests (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (Edgington, 1964)) and on par with the inter-experiment reliability reported for the Clicktionary game (Linsley et al., 2017).
This suggests that ClickMe identiﬁes similar visual features as Clicktionary, albeit in a much more efﬁcient way, by swapping out one human partner with a DCN.
O BSERVERS DID NOT ADOPT A DCN- SPECIFIC STRATEGY
Importantly, participants did not adopt strategies to ﬁnd visual features that were more important to their DCN partners than to other humans.
A sensitivity analysis of this sort is impossible given the mechanics and statistics of gameplay: (1) Participants on average played fewer rounds than the number of object categories in ClickMe (380 vs. 1,000).
(2) ClickMe participants were not aware of how their clicked regions were revealed to their DCN partners (Fig. 1). (3) the (cid:58)(cid:58)(cid:58)
The top-
[Figure] [Figure]
most frequent players were just as likely to elicit correct responses from their DCN on the ﬁrst half of their game rounds as on the second half, meaning these “expert” participants did not learn anything about features preferred by DCNs (53.64% vs. 53.61%; t (199) = 0 .
04 , n [Figure]
s. ). (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) according (cid:58)(cid:58) to [Table]
These [Table] show
[Table] learning [Table] test).
In addition, as we will describe below [Table]
the similarity between ClickMe visual features selected by human participants is signiﬁcantly greater than the similarity between human and DCN features.
This means that participants were choosing features that they found important for recognition, and that these features were strongly stereotyped between players.
P SYCHOPHYSICS METHODS
Stimulus generation We created a “stochastic” ﬂood-ﬁll algorithm which we applied to a phasescrambled version of a ClickMe object image to reveal increasingly larger image regions.
First, the image pixel given highest importance by a ClickMe map was identiﬁed.
Second, the algorithm expanded this region anisotropically, with a bias towards pixels with higher attention scores.
The revealed region was set to the center of the image to ensure that participants did not have to foveate to see important image parts and to prevent the spatial layout to affect the results.
Separate image sets were generated by this procedure for ClickMe and Salicon saliency maps.
Participants viewed images masked by one type of map or the other, but never both to prevent memory effects.
Participants saw each unique exemplar only once in a randomly selected masking conﬁguration.
The total number of pixels in the attention maps for a given image was equalized between ClickMe and saliency maps.
Original images were sampled from 4 target and 4 distractor categories: bird, zebra, elephant, and cat; table, couch, refrigerator, and umbrella.
Psychophysics experiment In each experiment trial, participants viewed a sequence of events overlaid onto a white background: ( i) a ﬁxation cross was ﬁrst displayed for a variable time (1,100–1,600ms); ( ii) followed by the test stimulus for 400ms; ( iii) and an additional 150ms of response time.
In total, participants were given 550ms to view the image and press a button to judge its category (feedback was provided when response times fell outside this time limit).
Participants were instructed to categorize the object in the image as fast and accurately as possible by pressing the “s” or “l” key, which were randomly assigned across participants to either the target or distractor category.
Similar paradigms and timing parameters yielded reliable behavioral measurements of pre-attentive visual system processes (e.g., Eberhardt et al., 2016).
The experiment began with a brief training phase to familiarize participants with the paradigm.
Afterwards, participants were given feedback on their categorization accuracy at the end of each of the ﬁve experimental blocks ( images per block). [Table]
ClickMe map exemplars. Experiments were implemented with the psiTurk framework (Gureckis et al., 2016) and custom javascript functions.
Each trial sequence was converted to an HTML5-compatible video format to provide the fastest reliable presentation time possible in a web browser.
Videos were preloaded before each trial to optimize the reliability of experiment timing within the web browser.
A photo-diode was used to verify stimulus timing was consistently accurate within 10ms across different operating system, web browser, and display type conﬁgurations.
Images were sized at 256 × 256 pixels, which is equivalent to a stimulus size between approximately 5 o − 11 o across a likely range of possible display and seating setups participants used for the experiment. [Table]
which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) viewed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) revealed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) according (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps, (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) other (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) revealed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) according (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58) [Table] which (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) compared (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) performance (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) between (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58) vs. (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Salicon (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) groups (cid:58)(cid:58) at (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) every (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) “percentage (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) image (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) revealed (cid:58)(cid:58)(cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table]
was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) called (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Salicon (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) viewer (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) instead), (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) calculating (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58) new (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) difference (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) accuracy (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) between (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58) two (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) groups. (cid:58)(cid:58)(cid:58)(cid:58) This (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
Computational-neuroscience models have posited that there are two main pathways that guide visual attention (Torralba et al., 2006). Global features are typically used to compute so-called “summary statistics” by averaging activities from individual feature channels across the entire scene. These representations are designed to capture the scene layout or “gist”, and are hypothesized to serve as a representation of contextual information to drive attention (Oliva and Torralba, 2007).
This is most similar to the feature-based attention that is used in most state-of-the-art networks (Bell et al., 2016; Wang et al., 2017; Hu et al., 2017). In this work, we explore a complementary form of attention known as visual saliency (Itti and Koch, 2001) derived from local feature representations. While visual saliency has been extensively studied (Nguyen et al., 2018), this is the ﬁrst time,to our knowledge, that this local form of attention has been combined with a global form of attention in a global-and-local (GALA) network that can learn to integrate them in a complex nonlinear combination to solve visual recognition tasks.
GALA-R ES N ET -50 TRAINING
In our experiments, ClickMe maps were blurred with a 49x49 kernel (the square of the bubble radius in the ClickMe game), before training to aid in convergence.
Object image and ClickMe importance map pairs were passed through the network during training and augmented with identical random crops, and left-right ﬂips.
Models were trained for 100 epochs and weights were selected that yielded the best validation accuracy.
All models were implemented in Tensorﬂow and were trained “from scratch” with weights drawn from a scaled normal distribution.
We used SGD with Nesterov momentum (Sutskever et al., 2013) and a piece-wise constant learning rate schedule that decayed by 1 / 10 after 30, 60, 80, and 90 epochs of training.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
Supplementary ﬁgure 2.
ClickMe map exemplars
Models trained on full versions of ILSVRC12 (Table 2 and Table 4) were trained with Google TPUs on Google Cloud Virtual Machines.
The large amount of TPU VRAM enabled models in these experiments to be trained with batches of 1,024 images.
Bicubic interpolation operations used for resizing ClickMe maps were replaced with biliear interpolation on the TPUs, since the former is not supported.
Models trained on the ClickMe subset of ILSVRC12 were trained with TITAN X Pascal
GPUs (Table 1 in the main text and Table 3).
Because of memory constraints, these models were trained with batches of 32 images.
(cid:58)(cid:58)(cid:58) See (cid:58) https://github.com/<user>/GALA_ClickMe(cid:58)(cid:58) for (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) reference (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) implementation. (cid:58)
T RADE - OFF BETWEEN OBJECT RECOGNITION ACCURACY AND CONSISTENCY OF ATTENTION MAPS WITH HUMANS
We investigated the trade-off between maximizing object categorization accuracy and predicting
ClickMe maps (i.e., learning a visual representation which is consistent with that of human observers).
We performed a systematic analysis over different values of the hyperparameter λ , which scaled the magnitude of the ClickMe map loss, while recording object classiﬁcation accuracy the similarity between ClickMe maps and model attention maps.
Attention maps were derived from networks as the feature column-wise L 2 norms of activity from the ﬁnal layer of GALA or SE attention (Zagoruyko and Komodakis, 2016).
Model attention map similarity with ClickMe maps was measured with rank-order correlation.
At each value of λ that was tested, ﬁve models were trained for 100 epochs, and weights that optimized accuracy on the validation ClickMe dataset were selected (Fig. 7).
This analysis demonstrated that both object categorization and ClickMe map prediction improve when λ = 6 .
[Figure]
GALA-R ES N ET -50 VALIDATION
We benchmarked GALA-ResNet-50 versus a vanilla ResNet-50 and a ResNet-50 with SE attention (Hu et al., 2017) on the validation split of the ILSVRC12 challenge dataset (Table 2).
These models link (cid:58)(cid:58)(cid:58) will (cid:58)(cid:58) be (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) updated (cid:58)(cid:58)(cid:58)(cid:58) after (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) review (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) period, (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) protect (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) anonymity.
[Table]
were trained on the original version of ILSVRC12.
Our implementation is consistent with published references [Table] and we ﬁnd that the GALA-ResNet-50 outperforms the other models 3 .
As discussed in the main text [Table] the GALA-ResNet-50 excelled on the ClickMe subset of ILSVRC (Table 3).
The GALA-ResNet-50 was more accurate and better able to predict human attention maps than either the SE-ResNet-50 or the ResNet-50.
This model’s performance was improved dramatically when it was co-trained with ClickMe maps, which cut down top-5 error by ∼ 25% over both ResNet-50 and SE-ResNet-50. [Table] Networks’ [Table]
and fraction of explained human ClickMe map variability on 224 × 224 center crops from the ClickMe test set. The dataset can be downloaded from . ∗∗ denotes p <0.01. To understand the effectiveness of ClickMe supervision when it is only available for a subset of all images in a dataset, we also tested these models on a preparation of the full ILSVRC12 for the
ClickMe game.
Once again, the GALA-ResNet-50 trained with ClickMe maps outperformed all other models in classiﬁcation performance (Table 4). [Table] Networks trained on the full ILSVRC12 training set, with ClickMe supervision on only a subset of these images ( ∼ 16% of all images).
This experiment demonstrates that ClickMe maps are not needed for all training samples to beneﬁt from ClickMe supervision.
Network classiﬁcation error is reported on 224 × 224 center crops from ILSVRC12 validation 4 .
The dataset can be download at .
GALA-R ES N ET -50 CONTROLS
Two control experiments evaluated (1) the effectiveness of ClickMe maps for attention supervision, and (2) whether attention modules were even needed for a model beneﬁt from this form of supervision.
We ﬁrst measured the importance of ﬁne-grained annotations in ClickMe maps for supervising GALA attention.
To do this, we compared the GALA-ResNet-50 with ClickMe maps to one trained on “bounding boxes” derived from these maps.
Bounding boxes were created by drawing a rectangle over the ClickMe map that spanned its spatial extent.
In practice, these bounding boxes do not necessarily line up neatly with classical bounding boxes drawn around objects for localization tasks.
However, it still provides useful information about the resolution at which attention supervision is needed.
The
GALA-ResNet-50 trained with ClickMe maps outperformed one trained with these bounding boxes on both the ClickMe subset of ILSVRC12 (Table 3) and our preparation of the ILSVRC12 (Table 4). pattern of results and approximately equal performance for both the classic preand more recent “post-activation” ﬂavors of ResNet-50 (He et al., 2016).
The version of ILSVRC12 used to train these models is a version that we preprocessed to produce images for .
We standardized the height and width of ILSVRC12 for this dataset by following the method of Eberhardt et al.
(2016): Images shorter than 256 pixels in height or width were removed and all remaining images were cropped to a size then scaled to 256 × 256 pixels.
The resulting dataset includes 1,281,167 images total, 196,499 of which have ClickMe maps. [Table] [Table] downloaded
[Table]
from
[Table]
ClickMe.
ai/about .
[Table]
SE-ResNet- (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 27.
(cid:58)(cid:58)(cid:58) 8.91 (cid:58) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58) no (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 27.
(cid:58)(cid:58)(cid:58) 8.78 (cid:58) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)
w/ (cid:58)(cid:58) b. (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) boxes (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 27.
(cid:58)(cid:58)(cid:58) 8.80 (cid:58) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
[Table]
[Table]
only (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subset (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ( ∼ 16% (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58) all (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images).
(cid:58)(cid:58)(cid:58)(cid:58)
This (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) experiment (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) demonstrates (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58) are (cid:58)(cid:58)(cid:58) not (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) needed (cid:58)(cid:58) for
[Table] all [Table] from
[Table] The
[Table]
at ClickMe.ai/about (cid:58) . We also tested if ClickMe maps can directly supervise feature learning in residual networks. This involved replacing the ClickMe map attention loss with one that minimized the L 2 distance between ClickMe maps and activity volumes from a ResNet-50 during object classiﬁcation training (this loss was applied to the same layers as the attention models above; Table 3, ResNet-50 w/ ClickMe).
This model performed comparably to a normal ResNet-50, but was less accurate than the GALA-ResNetwith ClickMe maps. [Table]
ClickMe (cid:58)(cid:58)(cid:58)(cid:58) map (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision (cid:58)(cid:58)(cid:58) on (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subset (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Microsoft ( cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) COCO (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) detection (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) challenge (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) contained (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
trained (cid:58)(cid:58) on (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ILSVRC12). (cid:58)(cid:58)(cid:58) For (cid:58)(cid:58)(cid:58)(cid:58) each (cid:58)(cid:58) of (cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) selected (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images, (cid:58)(cid:58)(cid:58) we (cid:58)(cid:58)(cid:58)(cid:58) also (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) collected (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) animal (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) vehicle (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) segmentation (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) masks (cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) challenge.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Thus, (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) amounted (cid:58)(cid:58) to (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58) large (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) “zero-shot” (cid:58)(cid:58)(cid:58) test (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) interpretability (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) model (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention. (cid:58) (cid:58)(cid:58)(cid:58)(cid:58)
Each (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) COCO (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) image (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) resized (cid:58)(cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 480 × 640 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) pixels, (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) passed (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) through (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
GALA-ResNet- (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table] module, (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) as (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) described (cid:58)(cid:58)(cid:58) in (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) main (cid:58)(cid:58)(cid:58)(cid:58) text, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) processed (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visualization (cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) setting (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) columns (cid:58)(cid:58) at (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) every (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) spatial (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Table]
volume (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) H × W × 1 .
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Because (cid:58)(cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subsampling (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ResNet-50, (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) height (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) width (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) resulting (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58) mask (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58) less (cid:58)(cid:58)(cid:58)(cid:58) than (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) input (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images. (cid:58)(cid:58)(cid:58)(cid:58)
This (cid:58)(cid:58)(cid:58)(cid:58) was (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) corrected (cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) resizing (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58) to (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 480 × 640 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) pixels.
(cid:58) (cid:58)(cid:58)(cid:58) We (cid:58)(cid:58)(cid:58)(cid:58) ﬁrst (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visualized (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) from (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) models (cid:58)(cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) using (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) them (cid:58)(cid:58)(cid:58) as (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) transparency (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) channel (cid:58)(cid:58)(cid:58) for (cid:58)(cid:58)(cid:58)(cid:58) their (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) corresponding (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) COCO (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58)
(a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) random (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subset (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) depicted (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58) Fig. (cid:58)(cid:58)(cid:58) 8). (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) There (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) striking (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) regularities (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) selected (cid:58)(cid:58) by (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) when (cid:58)(cid:58) it (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
ClickMe (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps: (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) important (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) animal (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parts, (cid:58)(cid:58)(cid:58)(cid:58) such (cid:58)(cid:58) as (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) faces (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) tails (cid:58)(cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) consistently (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [Figure] [Table]
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
Applying (cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) procedure (cid:58)(cid:58) to (cid:58)(cid:58) all (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) 2,055 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) COCO [Table] images [Table]
yielded [Table] per-image [Table]
scores (cid:58)(cid:58) in [Table] the
[Table] range (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) [0 , 1] . (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Comparing [Table]
the
[Table] is [Table] (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
G A
L A n o
C li ck
M e
G A
L A w /
C li ck
M e m a p s
M i c r o s o f t C O
C
O
(cid:58)(cid:58)
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
(cid:58)(cid:58)(cid:58)(cid:58)
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) (cid:58)(cid:58)
(cid:58)(cid:58) random (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) selection (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) from (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) subset (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) depicted (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) object (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) categories (cid:58)(cid:58)(cid:58)(cid:58) also (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) found (cid:58)(cid:58) in (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ILSVRC12.
(cid:58)(cid:58)(cid:58)
In (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) middle (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) panel, (cid:58)(cid:58)(cid:58)(cid:58) each (cid:58)(cid:58) of (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) these (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) images (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) shown (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) transparency (cid:58)(cid:58)(cid:58) set (cid:58)(cid:58)(cid:58) to (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) map (cid:58) it (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) yielded (cid:58)(cid:58) in (cid:58)(cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58)(cid:58)(cid:58) with (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ClickMe (cid:58)(cid:58)(cid:58) map (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) Opaque (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) indicate (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) features (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58)(cid:58)(cid:58)
this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attended (cid:58)(cid:58)(cid:58) to, (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) transparent (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58) are (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ones (cid:58)(cid:58)(cid:58)(cid:58) that (cid:58) it (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) ignored; (cid:58)(cid:58)(cid:58)(cid:58) this (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) emphasizes (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) animal (cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58)(cid:58) like (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) faces (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) tails, (cid:58)(cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) vehicle (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) parts (cid:58)(cid:58)(cid:58) like (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) wheels (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) windshields. (cid:58)(cid:58)(cid:58) The (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) bottom [Table] panel (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) shows (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58)(cid:58) same (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) visualization (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) using (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58)(cid:58)(cid:58)(cid:58) maps (cid:58)(cid:58)(cid:58)(cid:58) from (cid:58) a (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GALA-ResNet-50 (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) trained (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) without (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
ClickMe (cid:58)(cid:58)(cid:58)(cid:58) map (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) supervision.
(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)
Here, (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) attention (cid:58)(cid:58) is (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) distributed (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58) less (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) interpretable.