In human cognition, world knowledge supports the perception of object colours: knowing that trees are typically green helps to perceive their colour in certain contexts. We go beyond previous studies on colour terms using isolated colour swatches and study visual grounding of colour terms in realistic objects. Our models integrate processing of visual information and object-specific knowledge via hard-coded (late) or learned (early) fusion. We find that both models consistently outperform a bottom-up baseline that predicts colour terms solely from visual inputs, but show interesting differences when predicting atypical colours of so-called colour diagnostic objects. Our models also achieve promising results when tested on new object categories not seen during training. Research on human perception has shown that world knowledge supports the processing of sensory information (Mitterer et al., 2009; Ishizu, 2013). For instance, humans have been found to use their knowledge about typical colours of an object when perceiving an instance of that object, in order to compensate for, e.g., perceptually challenging illumination conditions and achieve colour constancy (Mitterer and de Ruiter, 2008; Witzel and Gegenfurtner, 2018). Thus, the visual perception of object colours can be thought of as leveraging top-down knowledge for bottom-up processing of sensory input, in accordance with traditional approaches in psychology (e.g. Colman, 2009). The integration of visual information and world knowledge in perception, however, is far from obvious, with views ranging from processing through bidirectionally connected bottom-up and top-down components to the assumption that visual and conceptual representations themselves are inseparably intertwined (Kubat et al., 2009). A lot of recent work in Language & Vision (L&V) has looked at grounding language in realistic sensory information, e.g. images of complex, real-world scenes and objects (Bernardi et al., 2016; Kafle and Kanan, 2017). In L&V, however, the use of top-down knowledge has mostly been discussed in the context of zero-shot or few-shot learning scenarios where few or no visual instances of a particular object category are available (Frome et al., 2013; Xian et al., 2018). 1 We present a simple experiment on language grounding that highlights the great potential of top-down processing even for very common words with a lot of visual instances: we learn to ground colour terms in visual representations of real-world objects and show that model predictions improve strongly when incorporating prior knowledge and assumptions about the object itself. We investigate visual grounding of colour terms by combining bottom-up and top-down modeling components based on early and late fusion strategies, reflecting different interpretations about the integration of visual and conceptual information in human perception. We find that these strategies lead to differ-1 Note that in L&V, the term top-down has recently been used in a different way in the context of attention models where it refers to systems that selectively attend to the output of a certain layer (Anderson et al., 2018). Even recent work on colour terms has mostly been using artificial datasets with descriptions of isolated colour swatches that show a single hue, primarily examining effects of context and conversational adequacy in colour naming (Baumgaert-ner et al., 2012; Meo et al., 2014; McMahan and Stone, 2015; Monroe et al., 2016, 2017; Winn and Muresan, 2018). However, object colours bear a range of additional challenges for perception and grounding: (i) chromatic variation due to lighting and shading (Witzel and Gegenfurtner, 2018), (ii) effects of conventionalization as in e.g. red hair (Gardenfors, 2004) and (iii) the inherent complexity of real-world objects (Witzel and Gegenfurtner, 2018), e.g. a tree with green leaves and a brown trunk is typically called green (see figure 1). In human cognition, several recalibration strategies support the constant perception of object colours given these challenges. In addition to bottom-up driven strategies like the chromatic adaption to situational sources of light, this also includes mechanisms such as the Memory Colour Effect : The automatic perception of canonical colours that accompanies the recognition of objects with characteristic hues (Olkkonen et al., 2008). Our aim in this work is to transfer knowledge-based recalibration mechanisms to the automatic classification of object colours. Mojsilovic (2005) and Van de Weijer et al. (2007) propose pixelwise approaches for modeling colour naming in natural images, accounting for factors such as illumination and non-uniform object colours. Van de Weijer et al. (2007) assign colour terms as labels to colour values of individual pixels and then average over these labels to obtain a colour term for an image region. We use their model as one of our baselines in Section 4. However, they do not take into account object-specific colour tendencies. Zarrie and Schlangen (2016) classify colour histograms for objects in real-world images. They train object-specific classifiers that recalibrate a bottom-up classifier, but only obtain a small improvement from recalibration. We implement a general top-down component that can be 2 Code and data for this project are available at: https://github.com/clause-jena/colour-term-grounding integrated with bottom-up processing in different ways. We focus on the effect of knowledge in language grounding and adopt a slightly idealized setting for modeling: we assume that the object type is available during training and testing. Following e.g. Snoek et al. (2005); Gunes and Piccardi (2008); Baltrusaitis et al. (2019), we distinguish early and late fusion as a way of integrating modeling components with different sources of information. Figure 2 illustrates our models, which we describe below. BOTTOM-UP This component relies solely on sensory input and is implemented as a feed-forward network trained to predict colour terms from 3-dimensional RGB histograms (representing the polychromatic distribution of colour values in complex objects). The output layer has a softmax over the 11 basic colour terms (Berlin and Kay, 1969). For comparability, we adopt the architecture in Zarrie and Schlangen (2016) (Input Layer: 512 nodes, Hidden layers with 240 and 24 nodes and ReLU activation, output layer: 11 nodes, DropOut: 0.2). We did not obtain improvements when testing other colour spaces. We also tried visual features extracted with a neural object recognizer (Simonyan and Zisserman, 2014) which only give a small improvement over colour histograms. Thus, in Section 4, we report results only for RGB histograms, as they are more transparent as representations and do not include any conceptual information on objects. TOP-DOWN This component relies only on conceptual information about the object which consists of assignments of objects to object types and colour distributions for object types reflected in the data. Thus, this classifier predicts colour terms given only the object type, which is supposed to mimic the memory colour effect discussed in Section 2. We use (pre-trained) word embeddings for object types that are not fine-tuned during training. Hence, TOP-DOWN and the combined models can be tested on unseen object types. We use 100-dimensional pre-trained GloVe embeddings (Pennington et al., 2014). The embedding layer is followed by a hidden Layer (24 nodes, ReLU activation, drop-out set to 0.2). LATE-FUSION In this approach, BOTTOM-UP and TOP-DOWN compute their classification deFigure 2: Late and Early Fusion cisions independently. The output probability distributions are interpolated using a constant factor (which is set to 1 in our case), i.e. we simply calculate their arithmetic mean. Hence, the integration of visual and conceptual information is hard-coded. EARLY-FUSION Object type embeddings are processed by a single Hidden Layer (24 nodes, ReLU activation, 0.2 drop out), concatenated with the visual input and then further processed by the network (2 Hidden Layers with 240 and 24 nodes, ReLU activation, 0.2 drop out). The classification decision is computed after this shared processing. The integration of both sources of information is therefore learned by the model. Data We use VisualGenome (Krishna et al., 2016), which contains annotations and bounding boxes for 3.8M objects in more than 100K images. Roughly 2.8M object attributes are annotated, the most frequent being colour descriptions. We extracted all objects with at least one attribute among the basic colour terms black, blue, brown, green, grey, orange, pink, purple, red, white, yellow . Objects with multiple names were split up into distinct entries, basic colour terms were removed from object names. To counter VisualGenome's bias towards images of people (Krishna et al., 2016), we exclude objects with names that are hyponyms of person . 3 We compile our train and test data so that colours are evenly distributed, as we do not want the model to rely on biases in colour frequency. 4 For the development and evaluation sets, we use random under-sampling. To ensure training examples for less frequent object categories, 10K instances for each colour category are randomly 3 Excluding e.g. white person as a case of a highly conventionalized colour. picked from the original train set, with the possibility of objects being picked multiple times. In summary, 110k objects are used for training, 17523 for development and 9328 for evaluation. In Section 4.2, we report results for objects that occur at least 100 times in the data. For testing on unseen objects in Section 4.3, we use object types that occur at least 50 but less than 100 times with a colour attribute in VisualGenome (these are excluded from training). Evaluation We evaluate our models by measuring their accuracy both for the entire test set and for separate subsets of objects. In line with previous research in perceptual psychology (cf. Section 2), we distinguish Colour Diagnostic Objects (CDOs) , that are strongly associated with a specific Memory Colour , and Colour Neutral Objects (CNOs) , objects without a typical colour appearance. We expect the distinction between CDOs and CNOs to be reflected primarily in model predictions that involve the processing of conceptual object information. For CDOs, determining the respective Memory Colour could result in improved classification results, whereas this strategy is less promising for CNOs. Manually identifying objects as CDOs or CNOs is hardly feasible when using large-scale data sets such as VisualGenome. We therefore decide on a quantitative basis whether object types exhibit characteristic colours, namely by means of the entropy of the colour term distribution of an object type. For each object type o , we determine p c as the relative frequency of a colour c for all instances of the object. The entropy of an object's colour distribution is then calculated as E o =  (cid:88) c  C p c log 2 p c CDO All CDO CBO CNO typ. where C is the set of basic colour terms. We use the 100 objects with the lowest entropy as CDOs, the 100 objects with the highest entropy as CNOs. In our data, objects such as tree , carrot , jeans and refrigerator are classified as CDOs. CNO examples include balloon , umbrella , fish and butterfly . We consider CDO instances whose colouring corresponds to their associated colour to be typical (e.g. objects annotated as green tree ). Accordingly, CDOs that differ from their associated colour are considered atypical (e.g. red tree ). Some objects can neither be clearly identified as CDOs nor as CNOs. These include objects such as stone that often occur with specific colours (e.g. grey ) but also with other colours (e.g. brown, green ). To cover such cases we include Colour Biased Objects (CBOs) as a third group, determined as the 100 object types whose entropy is closest to the median of the data set. Our test set contains a total of 1192 object instances categorized as CDOs (887 typical and 305 atypical) as well as 933 CBO and 1755 CNO instances. Pixelwise Baseline For comparison, we report results of Van de Weijer et al. (2007)'s model on our data, that computes colour words for objects by classifying the individual pixels in the respective bounding box. Table 1 shows results for the separate model components and the fusion strategies. We note that BOTTOM-UP largely outperforms the pixelwise baseline. As expected, TOP-DOWN performs much worse than BOTTOM-UP on average, but achieves high accuracy on CDOs. We observe interesting differences between the fusion strategies: LATE-FUSION atypical typical G o l d atypical 33 272 typical 53 834 EARLY-FUSION atypical typical G o l d atypical 69 236 typical 53 834 Table 2: LATE-FUSION and EARLY-FUSION predictions for typical and atypical CDOs LATE-FUSION This model generally performs better than BOTTOM-UP and TOP-DOWN separately. Moreover, the impact of the respective component on the combined result depends on the type of object: For CDOs, the model seems to generally predict the memory colour for these diagnostic objects computed by TOP-DOWN . For CBOs, there is still a clear improvement over BOTTOMUP , whereas for CNOs the model mostly relies on BOTTOM-UP . Thus, even though the fusion is hard-coded, it achieves the desired flexible pattern for combining the components. However, LATEFUSION does not perform well at predicting atypical colours of CDOs, see the right columns of Table 1. This suggests that, here, the prediction of object colours is only based on knowledge and, essentially, not visually grounded. This is unsatisfactory as these atypical colours for CDOs could be particularly salient in conversation (Tarenskeen et al., 2015). EARLY-FUSION This fusion strategy generally improves the accuracy of BOTTOM-UP and TOPDOWN in isolation. On average, it slightly un-derperforms LATE-FUSION , but obtains slightly better accuracy values for CDOs and CBOs than LATE-FUSION (Table 1). Table 2 illustrates that EARLY-FUSION recognizes atypical object colours slightly more often than LATE-FUSION . At the same time, the model achieves higher accuracy for atypical CDOs, indicating that it often predicts the correct object colour in these cases. For typical CDO colours, LATE-FUSION and EARLY-FUSION achieve the same accuracy. Thus, EARLY-FUSION improves the prediction of atypical colours for CDOs as compared LATEFUSION (exemplified in figure 3). But it still predicts canonical object colours too often and achieves a lower accuracy on atypical CDO colours than BOTTOM-UP . This indicates that early link-B OTTOM-UPEARLY-FUSION object % top colour Acc. age and joint processing improves the integration of visual and conceptual information, at least for CDOs. It is, however, not a perfect solution to all problems identified: Even though the model learns to merge both sources of information, the bias for canonical colours is still too strong, and there remains a high dependence on non-sensory data. By using pre-trained embeddings, our models are able to handle object types that are unseen in the training set, via similarity to seen object types in the embedding space. For these objects, BOTTOMUP and EARLY-FUSION achieve an overall accuracy of 37.8 and 31.9, respectively 5 . To provide more qualitative insights, Table 3 shows the top four and bottom four objects in terms of how much EARLY-FUSION improves over the BOTTOM-UP baseline. With heater and wipers , the top four objects include types with highly characteristic 5 Note that these figures are not directly comparable with the results described above, since the instances for the individual colours are not evenly distributed in this set. colours. EARLY-FUSION appears to correctly derive their object-specific colour tendencies from similarities to trained objects. In the lower four objects, all instances of cherry and lime share the same colour. Here, EARLY-FUSION also predominantly predicts a particular but incorrect colour, i.e. similarity in the off-the-shelf embedding space does not lead to good generalization for colour tendencies. This is particularly evident with lime : The prevailing prediction of yellow suggests that the (in this case, misleading) semantic similarity to the trained object type lemon is captured. These findings support previous work on multimodal distributional semantics showing that off-the-shelf embeddings do not necessarily capture similarity with respect to visual attributes of objects (Silberer and Lapata, 2014). As in human perception, knowledge about typical object properties seems to be a valuable source of information for visual language grounding. Our fusion models clearly outperform a bottom-up baseline that relies solely on visual input. We also showed that the fusion architecture matters: the early integration of visual and conceptual information and their shared processing appears to be bene-ficial when colour diagnostic objects have atypical colours. However, even Early Fusion does not yet achieve a perfect balance between top-down and bottom-up processing. Future work should look at more complex fusion strategies, possibly coupled with bottom-up recalibration mechanisms (Zarrie and Schlangen, 2016; Mojsilovic, 2005) to further enhance colour classification under difficult illumination conditions. Our experiment on objects unseen during training looks promising but can be extended towards a more general approach that interfaces colour prediction with object recognition.
abstain method abstain result objective abstain abstain abstain abstain abstain abstain result objective result other other other other other other other objective other other method other other other other method method abstain abstain abstain method abstain method abstain abstain abstain method abstain method abstain abstain method abstain abstain abstain abstain method abstain method abstain method method method abstain abstain abstain method method method abstain abstain abstain abstain abstain abstain abstain abstain method abstain abstain method abstain abstain abstain abstain abstain abstain abstain result abstain result abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result result abstain abstain objective