Visual question answering (VQA) task not only bridges the gap between images and language, but also requires that specific contents within the image are understood as indicated by linguistic context of the question, in order to generate the accurate answers. Thus, it is critical to build an efficient embedding of images and texts. We implement DualNet, which fully takes advantage of discriminative power of both image and textual features by separately performing two operations. Building an ensemble of DualNet further boosts the performance. Contrary to common belief, our method proved effective in both real images and abstract scenes, in spite of significantly different properties of respective domain. Our method was able to outperform previous state-of-the-art methods in real images category even without explicitly employing attention mechanism, and also outperformed our own state-of-the-art method in abstract scenes category, which recently won the first place in VQA Challenge 2016.
@cite_16 proposed spatial memory network in which neuron activations of different spatial regions are stored in memory, and regions with high relevance are chosen depending on the question. The latter step was made possible by their novel spatial attention architecture designed to align words with patches.
@cite_16
2963656855 2255577267 2115441154 1805632810
We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses attention to choose regions relevant for computing the answer. We propose a novel question-guided spatial attention architecture that looks for regions relevant to either individual words or the entire question, repeating the process over multiple recurrent steps, or “hops”. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the network’s attention. We evaluate our model on two available visual question answering datasets and obtain improved results. We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3]. We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level “saliency map” representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions. Visual attention poses a mechanism for the selection of behaviourally relevant information from natural scenes which usually contain multiple objects. The aim of the present work is to formulate a neurodynamical model of selective visual attention based on the "biased competition hypothesis" and structured in several network modules which can be related with the different areas of the dorsal and ventral path of the visual cortex. Spatial and object attention are accomplished by a multiplicative gain control that emerges dynamically through intercortical mutual biased coupling. We also include in our computational model the "resolution hypothesis" in order to explain the role of the neurodynamics control of spatial resolution evidenced in psychophysical experiments. We propose that V1 neurons have different latencies depending on the spatial frequency to which they respond more sensitively. In concrete, we pose that V1 neurons sensitive to low spatial frequency are faster than V1 neurons sensitive to high spatial frequency. In this sense, a scene is first predominantly analysed at a coarse resolution level and the dynamics enhances subsequently the resolution at the location of an object until the object is identified.