Conference Paper

StructCap: Structured Semantic Embedding for Image Captioning

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Abstract

Image captioning has attracted ever-increasing research attention in multimedia and computer vision. To encode the visual content, existing approaches typically utilize the off-the-shelf deep Convolutional Neural Network (CNN) model to extract visual features, which are sent to Recurrent Neural Network (RNN) based textual generators to output word sequence. Some methods encode visual objects and scene information with attention mechanism more recently. Despite the promising progress, one distinct disadvantage lies in distinguishing and modeling key semantic entities and their relations, which are in turn widely regarded as the important cues for us to describe image content. In this paper, we propose a novel image captioning model, termed StructCap. It parses a given image into key entities and their relations organized in a visual parsing tree, which is transformed and embedded under an encoder-decoder framework via visual attention. We give an end-to-end formulation to facilitate joint training of visual tree parser, structured semantic attention and RNN-based captioning modules. Experimental results on two public benchmarks, Microsoft COCO and Flickr30K, show that the proposed StructCap model outperforms the state-of-the-art approaches under various standard evaluation metrics.

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... eling semantics in ImC and embedding semantics to ImD as illustrated in Fig. 1 (Bottom). On the one hand, to overcome the arbitrary syntax of the caption [16,17], we construct a structured semantic tree architecture, where the semantic contents (entities and relations) are automatically parsed given a severely blurred image. On the other hand, to align the semantic contents to the blurred image spatially, we design a spatial semantic representation for the tree nodes, where each entity/relation is represented in the form of feature maps, and the convolution is operated among the nodes in the tree structure. ...
... Based on the CNN-RNN architecture, Xu et al. [14] and You et al. [30] proposed to use an attention module in captioning based on the spatial features and the semantic concepts, respectively. To capture the semantic entities and their relations, Chen et al. [16] proposed a visual parsing tree model to embed and attend the structured semantic content into captioning. To advance the attention model, a bottom-up and top-down attention mechanism was designed in [15], which enabled attention to be calculated at the object/region level. ...
... where j is the index of the tree node as shown in Fig. 3, and H j ∈ R w ′ ×h ′ ×c ′ (w ′ , h ′ , and c ′ denote the width, the height, and the channel, respectively) is a feature map tensor of the j-th node. E represents one of four semantic entities, i.e., subject 1 (Subj1), object 1 (Obj1), subject 2 (Subj2), and object 2 (Obj2) as set up in [16]. σ is an element-wise nonlinear function upon the convolution operation ⊛ and the convolution kernel set K E for Convolutional decoupling in the j-th node (j = 1, 3, 5, 7). ...
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Image deblurring has achieved exciting progress in recent years. However, traditional methods fail to deblur severely blurred images, where semantic contents appears ambiguously. In this paper, we conduct image deblurring guided by the semantic contents inferred from image captioning. Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC). In particular, S3-tree captures and represents the semantic contents in structured spatial features in ImC, and then embeds the spatial features of the tree nodes into GAN based ImD. Co-training on S3-tree, ImC, and ImD is conducted to optimize the overall model in a multi-task end-to-end manner. Extensive experiments on severely blurred MSCOCO and GoPro datasets demonstrate the significant superiority of S3E-Deblur compared to the state-of-the-arts on both ImD and ImC tasks.
... In terms of structured feature embedding, exiting works for multimedia data [2,3,5] employed different structures, e.g., chain, tree, and graph. Chen et al. [2,3] proposed to enhance the visual representation for image captioning task by a linear-based structured tree model. ...
... In terms of structured feature embedding, exiting works for multimedia data [2,3,5] employed different structures, e.g., chain, tree, and graph. Chen et al. [2,3] proposed to enhance the visual representation for image captioning task by a linear-based structured tree model. However, because of the simple linear-based tree model in these schemes [2,3], limited contextual information is transferred between different layers and without using any attention mechanism. ...
... Chen et al. [2,3] proposed to enhance the visual representation for image captioning task by a linear-based structured tree model. However, because of the simple linear-based tree model in these schemes [2,3], limited contextual information is transferred between different layers and without using any attention mechanism. Chen et al. [5] applied a chain structure model using an RNN for visual embeddings, which unfortunately ignores the underlying structure. ...
Preprint
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The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments, like regions in images and words in sentences, and adopt attention modules to highlight the relevance of cross-modal semantic correspondences. However, the retrieval performance remains unsatisfactory due to a lack of consistent representation in both semantics and structural spaces. In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e.g., "dog $\to$ play $\to$ ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities. In this paper, we propose a novel Structured Multi-modal Feature Embedding and Alignment (SMFEA) model for image-sentence retrieval. In order to jointly and explicitly learn the visual-textual embedding and the cross-modal alignment, SMFEA creates a novel multi-modal structured module with a shared context-aware referral tree. In particular, the relations of the visual and textual fragments are modeled by constructing Visual Context-aware Structured Tree encoder (VCS-Tree) and Textual Context-aware Structured Tree encoder (TCS-Tree) with shared labels, from which visual and textual features can be jointly learned and optimized. We utilize the multi-modal tree structure to explicitly align the heterogeneous image-sentence data by maximizing the semantic and structural similarity between corresponding inter-modal tree nodes. Extensive experiments on Microsoft COCO and Flickr30K benchmarks demonstrate the superiority of the proposed model in comparison to the state-of-the-art methods.
... For (2), in most existing works the pre-trained object detector is kept frozen when training the target VL task. This implies that the conditioning relationship between the detected objects and the input image is not jointly optimized with the target VL task. ...
... In the early stage of image captioning, researchers used an image encoder such as ResNet [16] to encode the input image into a global pooled representation [2,3,6,11,13,15,22,24,34,51,54,61]. Captions are then generated conditioned on the encoded global feature. ...
Preprint
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Significant progress has been made on visual captioning, largely relying on pre-trained features and later fixed object detectors that serve as rich inputs to auto-regressive models. A key limitation of such methods, however, is that the output of the model is conditioned only on the object detector's outputs. The assumption that such outputs can represent all necessary information is unrealistic, especially when the detector is transferred across datasets. In this work, we reason about the graphical model induced by this assumption, and propose to add an auxiliary input to represent missing information such as object relationships. We specifically propose to mine attributes and relationships from the Visual Genome dataset and condition the captioning model on them. Crucially, we propose (and show to be important) the use of a multi-modal pre-trained model (CLIP) to retrieve such contextual descriptions. Further, object detector models are frozen and do not have sufficient richness to allow the captioning model to properly ground them. As a result, we propose to condition both the detector and description outputs on the image, and show qualitatively and quantitatively that this can improve grounding. We validate our method on image captioning, perform thorough analyses of each component and importance of the pre-trained multi-modal model, and demonstrate significant improvements over the current state of the art, specifically +7.5% in CIDEr and +1.3% in BLEU-4 metrics.
... Melnyk et al. [22] reported the comparison of context-aware LSTM captioner and co-attentive discriminator for image captioning. Wu et al. [23] used question features and image features, but no information on how question-information is generated, Chen et al. [24] used parsing tree StructCap but handling a tree-based representation is difficult, Jiang et al. [25] sequence-to-sequence framework for joint learning but dependent on resource-hungry training, and Wu et al. [26] dual temporal modal with a different understanding of the same features and complementing each-other, Image-Text Surgery in [16,27] attribute-driven attention with different attribute influences which are difficult to gather from images, Cornia et al. [28] generative recurrent neural network but defining a random generator based captioner is difficult, Zhao et al. [29] introduced MLAIC for better representation but defining a representation for sentences can be challenging. Also there is [30] text-guided attention similar to Sur et al. [31] but has enhanced semantic features due to TPR, Chen et al. [32] reference based LSTM, Chen et al. [33] adversarial neural network [34] with a generative process mimicrying the sentence generation, highdimensional attentions [35,36] coarse-to-fine skeleton sentence where previous generation sentences are used to generate newer sentences with effectiveness, Chen et al. [37] specific styles for different applications based on data that was not shared, Chen et al. [38] structural relevance and structural diversity, multimodal attention [39] with different mode of attentions that has hardly any structural or content based explainable relevance, Harzig et al. [40] popular brands caption, Liu et al. [41] diversified captions, Chunseong Park et al. [42] stylish caption, Sharma et al. [43] sub-categorical styles, Yao et al. [44] personalized [45] captions, Zhang et al. [46] studied actor-critic reinforcement learning [47], Fu et al. [48] scene-specific attention contexts, Ren et al. [49] policy network for captions and these policies are related to image content selection, Liu et al. [50] reinforcement learning based training for finetuning the captioner model, Cohn-Gordon et al. [51] distinguish between similar kind for diversity, Liu et al. [52] improved with correctness of attention in image, Lu et al. [53] adaptivity for attention, Vinyals et al. [54] used combination of computer vision and machine translation, Zhang et al. [55] used adaptive re-weight loss function, Park et al. [56] personalized captioning, Wu et al. [57] high level semantic concept, Vinyals et al. [8] [71] new tree based approach to composing expressive image descriptions, Mao et al. [72] transposed weight sharing scheme, Mathews et al. [73] different emotions and sentiments, and Yang et al. [74] where nouns, verbs, scenes and prepositions used for structuring sentence. ...
... While most of the works were just what the model has learned, we paid more importantly what we can create and feed into the network like shown in Eqs. (24) and (37). With this approach, we have established a new performance level, surpassing previous works in all the metrics. ...
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Region visual features enhance the generative capability of the machines based on features. However, they lack proper interaction-based attentional perceptions and end up with biased or uncorrelated sentences or pieces of misinformation. In this work, we propose Attribute Interaction-Tensor Product Representation (aiTPR), which is a convenient way of gathering more information through orthogonal combination and learning the interactions as physical entities (tensors) and improving the captions. Compared to previous works, where features add up to undefined feature spaces, TPR helps maintain sanity in combinations, and orthogonality helps define familiar spaces. We have introduced a new concept layer that defines the objects and their interactions that can play a crucial role in determining different descriptions. The interaction portions have contributed heavily to better caption quality and have out-performed various previous works on this domain and MSCOCO dataset. For the first time, we introduced the notion of combining regional image features and abstracted interaction likelihood embedding for image captioning.
... Overall, the research on MMT is of great significance. On the one hand, similar to other multimodal tasks such as image captioning [11,12,24,41] and visual question answering [22,33,37,64], MMT involves computer vision and natural language processing (NLP) and proves the effectiveness of visual features in translation tasks. In other words, it not only requires an algorithm with in-depth understanding of visual contexts, but also connects its interpretation with a language model to create a natural sentence. ...
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... Chen et al. [14] introduced StructCap, where they used an extra set of features derived out the parsing tree that was created from the knowledge of the objects gathered from the visual features. The model parsed an image into key entities, derived their relations and organized them into a visual parsing tree. ...
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Deep Learning Architectures has been researched the most in this decade because of its capability to scale up and solve problems that couldn’t be solved before. Mean while many NLP applications cropped up and there is a requirement to understand how the concepts gradually evolved till date after perceptron was introduced in 1959. This document will provide a detailed description of the computational neuroscience starting from artificial neural network and how researchers retrospected the drawbacks faced by the previous architectures and paved the way for modern deep learning. Modern deep learning is more than what it had been perceived decades ago and had been extended to architectures, with exceptional intelligence, scalability and precision, beyond imagination. This document will provide an overview of the continuation of work and will also specifically deal with applications of various domains related to natural language processing and visual and media contents.
... The framework is used to "translate" an image to a sentence, where the visual features are extracted from convolutional neural network (CNN) and fed into Long Short-Term Memory (LSTM) to generate captions. Image captioning techniques have been extensively explored in [22,45,31,9,4,5]. A few models [51,54,2] seek to apply attention mechanism to bridge the gap of visual understanding and language processing. ...
Preprint
Image captioning has attracted ever-increasing research attention in the multimedia community. To this end, most cutting-edge works rely on an encoder-decoder framework with attention mechanisms, which have achieved remarkable progress. However, such a framework does not consider scene concepts to attend visual information, which leads to sentence bias in caption generation and defects the performance correspondingly. We argue that such scene concepts capture higher-level visual semantics and serve as an important cue in describing images. In this paper, we propose a novel scene-based factored attention module for image captioning. Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image. Then, an adaptive LSTM is used to generate captions for specific scene types. Experimental results on Microsoft COCO benchmark show that the proposed scene-based attention module improves model performance a lot, which outperforms the state-of-the-art approaches under various evaluation metrics.
... [8] reported the comparison of context-aware LSTM captioner and co-attentive discriminator for image captioning. [9] used question features and image features, [11] parsing tree StructCap, [12] sequence-tosequence framework, and [13] dual temporal modal, Image-Text Surgery in [14], [15] attribute-driven attention, [16] generative recurrent neural network, [17] MLAIC for better representation. Also there is [18] text-guided attention, [19] reference based LSTM, [21] adversarial neural network, high-dimensional attentions [22], [23] coarse-to-fine skeleton sentence, [24] specific styles, [25] structural relevance and structural diversity, multimodal attention [26], [27] popular brands caption [28] diversified captions, [29] stylish caption, [30] sub-categorical styles, [31] personalized captions, [32] studied actor-critic reinforcement learning, [33] scene-specific attention contexts, [34] policy network for captions, [35] reinforcement learning based training, [36] distinguish between similar kind for diversity, [37] improved with correctness of attention in image, [39] adaptivity for attention, [40] used combination of computer vision and machine translation, [42] used adaptive re-weight loss function, [43] personalized captioning, [46] high level semantic concept, [47] used visual features and machine translation attention combinations, [56] different caption styles, [57] shifting attention, [58] characteristics of text based representations, [62] variational autoencoder representation, [63] dependency trees embedding, [64] character-level language modeling, [65] fixed dimension representation, [66] 3-dimensional convolutional networks, [67] human judgments, out-of-domain data handling, [70] semantic attention, [73] Semantic Compositional Network (SCN), [74] localize and segment objects, [76] extra semantic attention, [78] content planning and recognition algorithms, [80] new tree based approach to composing expressive image descriptions, [81] transposed weight sharing scheme, [82] different emotions and sentiments, and [85] where nouns, verbs, scenes and prepositions used for structuring sentence. ...
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While image captioning through machines requires structured learning and basis for interpretation, improvement requires multiple context understanding and processing in a meaningful way. This research will provide a novel concept for context combination and will impact many applications to deal visual features as an equivalence of descriptions of objects, activities and events. There are three components of our architecture: Feature Distribution Composition (FDC) Layer Attention, Multiple Role Representation Crossover (MRRC) Attention Layer and the Language Decoder. FDC Layer Attention helps in generating the weighted attention from RCNN features, MRRC Attention Layer acts as intermediate representation processing and helps in generating the next word attention, while Language Decoder helps in estimation of the likelihood for the next probable word in the sentence. We demonstrated effectiveness of FDC, MRRC, regional object feature attention and reinforcement learning for effective learning to generate better captions from images. The performance of our model enhanced previous performances by 35.3\% and created a new standard and theory for representation generation based on logic, better interpretability and contexts.
... Here, object-based semantic image representation was used in a deep network as features to retrieve and select the relevant image(s). Chen et al. [9] introduced StructCap, where they used an extra set of features derived out the parsing tree that was created from the knowledge of the objects gathered from the visual features. The model parsed an image into key entities, derived their relations and organized them into a visual parsing tree. ...
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Progress in image captioning is gradually getting complex as researchers try to generalize the model and define the representation between visual features and natural language processing. In the absence of any established relationship, every time a new dividend is added, it produced very little improvement, not considerable enough to make it general. This work tried to define such kind of relationship in the form of representation called Algebraic Amalgamation-based Composed Representation (AACR) which generalized the scheme of language modeling and structuring the linguistic attributes (related to grammar and parts of speech of language) which will provide a much better structure and grammatically correct sentence. AACR enables better and more unique representation and structuring of the feature space and enables transfer learning like infrastructure for all machines to interact with the external world (both human and machine) with these representations. A large part of the different ways of defining and improving these AACR are discussed and their performance concerning the traditional procedures and feature representations are evaluated for image captioning application. The new models achieved considerable improvement than the corresponding previous architectures.
... Melnyk et al. [33] reported the comparison of context-aware LSTM captioner and coattentive discriminator for image captioning. Wu et al. [34] used question features and image features, [35] parsing tree StructCap, [36] sequence-to-sequence framework, and [37] dual temporal modal, Image-Text Surgery in [11,38] attribute-driven attention, [39] generative recurrent neural network, [40] MLAIC for better representation. Also there is [41] text-guided attention, [42] reference-based LSTM, [43] adversarial neural network, high-dimensional attentions [44,45] coarse-to-fine skeleton sentence, [46] specific styles, [47] structural relevance and structural diversity, multimodal attention [48,49] popular brands caption [50] diversified captions, [51] stylish caption, [52] sub-categorical styles, [53] personalized captions, [54] studied actor-critic reinforcement learning, [55] scene-specific attention contexts, [56] policy network for captions, [57] reinforcement learning-based training, [58] distinguish between similar kind for diversity, [59] improved with correctness of attention in image, [29] adaptivity for attention, [60] used combination of computer vision and machine translation, [61] used adaptive re-weight loss function, [62] personalized captioning, [63] high-level semantic concept, [6] used visual features and machine translation attention combinations, [64] different caption styles, [65] shifting attention, [66] characteristics of text-based representations, [67] variational autoencoder representation, [68] dependency trees embedding, [69] character-level language modeling, [70] fixed dimension representation, [71] 3-dimensional convolutional networks, [72] human judgments, out-of-domain data handling, [28] semantic attention, [12] semantic compositional network (SCN), [73] localize and segment objects, [74] extra semantic attention, [75] content planning and recognition algorithms, [76] new tree-based approach to composing expressive image descriptions, [77] transposed weight sharing scheme, [78] different emotions and sentiments, and [79] where nouns, verbs, scenes and prepositions used for structuring sentence. ...
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... Here, object-based semantic image representation was used in a deep network as features to retrieve and select the relevant image(s). Chen et al [4] introduced StructCap, where they used an extra set of features derived out the parsing tree that was created from the knowledge of the objects gathered from the visual features. The model parsed an image into key entities, derived their relations and organized them into a visual parsing tree. ...
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... [42] reported the comparison of context-aware LSTM captioner and coattentive discriminator for image captioning. [72] used question features and image features, [4] parsing tree StructCap, [23] sequence-to-sequence framework, and [74] dual temporal modal, Image-Text Surgery in [17], [2] attribute-driven attention, [11] generative recurrent neural network, [85] MLAIC for better representation. Also there is [31] text-guided attention, [3] reference based LSTM, [7] adversarial neural network, high-dimensional attentions [80], [70] coarse-to-fine skeleton sentence, [8] specific styles, [5] structural relevance and structural diversity, multimodal attention [34], [21] popular brands caption [32] diversified captions, [9] stylish caption, [48] sub-categorical styles, [78] personalized captions, [83] studied actor-critic reinforcement learning, [16] scene-specific attention contexts, [46] policy network for captions, [35] reinforcement learning based training, [10] distinguish between similar kind for diversity, [33] improved with correctness of attention in image, [37] adaptivity for attention, [68] used combination of computer vision and machine translation, [84] used adaptive re-weight loss function, [43] personalized captioning, [73] high level semantic concept, [69] used visual features and machine translation attention combinations, [19] different caption styles, [24] shifting attention, [27] characteristics of text based representations, [44] variational autoencoder representation, [49] dependency trees embedding, [65] character-level language modeling, [66] fixed dimension representation, [30] 3-dimensional convolutional networks, [67] human judgments, out-of-domain data handling, [82] semantic attention, [18] Semantic Compositional Network (SCN), [20] localize and segment objects, [22] extra semantic attention, [28] content planning and recognition algorithms, [29] new tree based approach to composing expressive image descriptions, [40] transposed weight sharing scheme, [41] different emotions and sentiments, and [77] where nouns, verbs, scenes and prepositions used for structuring sentence. ...
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In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
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We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each superpixel and then refine our results further by using the classifier in a conditional random field operating on the superpixel graph. Our proposed method exceeds the previously published state-of-the-art on two challenging datasets: Graz-02 and the PASCAL VOC 2007 Segmentation Challenge.
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In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets.
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We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is over-segmented into super-pixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.
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We propose a general framework for parsing images into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner. We illustrate our approach on natural images of complex city scenes where the objects of primary interest are faces and text. This method makes use of bottom-up proposals combined with top-down generative models using the data driven Markov chain Monte Carlo (DDMCMC) algorithm, which is guaranteed to converge to the optimal estimate asymptotically. More precisely, we define generative models for faces, text, and generic regions- e.g. shading, texture, and clutter. These models are activated by bottom-up proposals. The proposals for faces and text are learnt using a probabilistic version of AdaBoost. The DDMCMC combines reversible jump and diffusion dynamics to enable the generative models to explain the input images in a competitive and cooperative manner. Our experiments illustrate the advantages and importance of combining bottom-up and top-down models and of performing segmentation and object detection/recognition simultaneously.