Attention In Object Recognition Research Articles

Soft Error Resilience of Deep Residual Networks for Object Recognition

Convolutional Neural Networks (CNNs) have truly gained attention in object recognition and object classification in particular. When being implemented on Graphics Processing Units (GPUs), deeper networks are more accurate than shallow ones. Residual Networks (ResNets) are one of the deepest CNN architectures used in various fields including safety-critical ones. GPUs have proven to be the major accelerator for CNN models. However, modern GPUs are prone to radiation-induced soft errors, which is a serious issue in safety-compliant systems. In this work, we analyze and propose an approach to address the reliability of ResNet on GPUs. We firstly analyze three popular ResNet models, explicitly, ResNet-50, ResNet-101, and ResNet-152 through NVIDIA's fault injector, SASSIFI. We perform an in-depth analysis of the model from the perspective of layer and kernel vulnerability. Then, we experimentally show the vulnerability of ResNet models and identify the most vulnerable portions. Finally, we validate our solution, which is a selective-hardening technique, through hardening the worth-hardening kernels to avoid unnecessary overheads. Our strategy is demonstrated to mask up to 93.38% of the injected errors with performance overhead less than 5.35%. Furthermore, the percentage of the errors causing misclassifications can be reduced from 4.2% to 0.104%, thereby significantly improving the model's reliability.

WHOLE BODY VIBRATION IMPROVES ATTENTION AND MOTOR PERFORMANCE IN MICE DEPENDING ON THE DURATION OF THE WHOLE-BODY VIBRATION SESSION.

Background:Whole body vibration (WBV) is a form of physical stimulation via mechanical vibrations transmitted to a subject. It is assumed that WBV induces sensory stimulation in cortical brain regions through the activation of skin and muscle receptors responding to the vibration. The effects of WBV on muscle strength are well described. However, little is known about the impact of WBV on the brain. Recently, it was shown in humans that WBV improves attention in an acute WBV protocol. Preclinical research is needed to unravel the underlying brain mechanism. As a first step, we examined whether chronic WBV improves attention in mice.Material and Methods:A custom made vibrating platform for mice with low intensity vibrations was used. Male CD1 mice (3 months of age) received five weeks WBV (30 Hz; 1.9 G), five days a week with sessions of five (n=12) or 30 (n=10) minutes. Control mice (pseudo-WBV; n=12 and 10 for the five and 30 minute sessions, respectively) were treated in a similar way, but did not receive the actual vibration. Object recognition tasks were used as an attention test (novel and spatial object recognition – the primary outcome measure). A Balance beam was used for motor performance, serving as a secondary outcome measure.Results:WBV sessions of five (but not WBV sessions of 30 minutes) improved balance beam performance (mice gained 28% in time needed to cross the beam) and novel object recognition (mice paid significantly more attention to the novel object) as compared to pseudo WBV, but no change was found for spatial object performance (mice did not notice the relocation). Although 30 minutes WBV sessions were not beneficial, it did not impair either attention or motor performance.Conclusion:These results show that brief sessions of WBV improve, next to motor performance, attention for object recognition, but not spatial cues of the objects. The selective improvement of attention in mice opens the avenue to unravel the underlying brain mechanisms.

A-03 対象の知覚における特徴処理と空間的注意

A-03 対象の知覚における特徴処理と空間的注意

Colour combination attention for object recognition

Within the bag-of-words (BOWs) framework, the multiple cues fusion methods provide excellent results in object and scene classification. Top-down colour attention (CA) method is developed to use colour to guide attention by means of a top-down category-specific attention map. In this method, more features are taken from category-specific colour regions where objects are more likely to be contained. In CA, the colours on the object are considered separately, so the diversity of object colours and large intra-class colour variation make the discrimination of every colour on the object different. Object could be recognised as a collection of related colours. To enhance the object recognition capability of CA, our colour combination attention method uses mutual information and the colour combination histogram to estimate and combine the colours on the object. Results are presented on three challenging data sets, and the experiments demonstrate that the proposed feature fusion method significantly outperforms the state-of-the-art methods.

Negative priming of unattended part primes: Implications for models of holistic and analytic processing in object recognition

The "hybrid" model of object recognition (Hummel, 2001) proposes that unattended objects are processed holistically, while attended objects are processed both holistically and analytically. Supporting evidence for this claim was reported by Thoma, Hummel, and Davidoff (2004) who showed that, unlike whole object primes, unattended split object parts (presumed to require analytic processing) do not elicit repetition priming. Here we tested the generality of this finding by contrasting priming for whole and part prime stimuli as a function of prime informativeness and by modifying the design so that both unattended whole and part prime displays contained a single perceptual object. Unlike Thoma et al. (2004) the results showed negative (rather than an absence of) priming for unattended half object primes. These findings place new constraints on theoretical models of the role of attention in object recognition.