Differentiable architecture search has demonstrated promising results in automatically designing neural network architectures with desired properties, such as high accuracy and low FLOPs. However, it suffers from a cumbersome training process, and the injection of constraints in the search phase often relies on some hand-crafted heuristic regularizers, the design of which typically requires tremendous human effort. In this paper, to address these critical challenges, we present FGA-NAS, an efficient method for resource-constrained architecture search. First, to reduce the computational cost and improve search flexibility, we propose a novel condensed search space that merges multiple parallel-placed candidates into a single one. Second, to enable the gradient-based optimization for neural architecture search (NAS) under multiple combinatorial constraints, we decompose the constrained NAS into a few simple sub-problems without introducing any heuristics by using the ADMM algorithm [1]. Then, the constrained NAS can be resolved by alternately solving the simple sub-problems. Experimental results on ImageNet show that our method can discover efficient and accurate neural network architectures that achieve the state-of-the-art by only using 0.2 GPU days.

Video object detection is a challenging task because of the presence of appearance deterioration in certain video frames. One typical solution is to aggregate neighboring features to enhance per-frame appearance features. However, such a method ignores the temporal relations between the aggregated frames, which is critical for improving video recognition accuracy. To handle the appearance deterioration problem, this paper proposes a temporal context enhanced network (TCENet) to exploit temporal context information by temporal aggregation for video object detection. To handle the displacement of the objects in videos, a novel DeformAlign module is proposed to align the spatial features from frame to frame. Instead of adopting a fixed-length window fusion strategy, a temporal stride predictor is proposed to adaptively select video frames for aggregation, which facilitates exploiting variable temporal information and requiring fewer video frames for aggregation to achieve better results. Our TCENet achieves state-of-the-art performance on the ImageNet VID dataset and has a faster runtime. Without bells-and-whistles, our TCENet achieves 80.3% mAP by only aggregating 3 frames.

Crowd attribute recognition is a challenging task for crowd video understanding because a crowd video often contains multiple attributes from various types. Traditional deep learning-based methods directly treat this recognition problem as a multiple binary classification problem and represent the video by vectorizing and fusing the separately learned spatial and temporal features in the fully connected layers. Therefore, the correlations between these attributes may not be well captured. In this paper, a bidirectional recurrent prediction model with a semantic-aware attention mechanism is proposed to explore the spatio-temporal and semantic relations between the attributes for more accurate recognition. The ConvLSTM is introduced for feature representation to capture the spatio-temporal structure of the crowd videos and facilitate the visual attention. The bidirectional recurrent attention module is proposed for sequential attribute prediction by associating each subcategory attributes to corresponding semantic-related regions iteratively. The experiments and evaluations on the challenging WWW crowd video dataset not only show that our approach significantly outperforms the state-of-the-art methods but also verify that our approach can effectively capture the spatio-temporal and semantic relations of the crowd attributes.

Pedestrian attribute recognition in surveillance is a challenging task due to poor image quality, significant appearance variations and diverse spatial distribution of different attributes. This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem. Our framework contains a spatial graph and a directed semantic graph. By performing reasoning using the Graph Convolutional Network (GCN), one graph captures spatial relations between regions and the other learns potential semantic relations between attributes. An end-to-end architecture is presented to perform mutual embedding between these two graphs to guide the relational learning for each other. We verify the proposed framework on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show superiority of the proposed method over state-of-the-art methods and effectiveness of our joint GCN structures for sequential attribute prediction.

Pedestrian attribute recognition in surveillance is a challenging task in computer vision due to significant pose variation, viewpoint change and poor image quality. To achieve effective recognition, this paper presents a graph-based global reasoning framework to jointly model potential visual-semantic relations of attributes and distill auxiliary human parsing knowledge to guide the relational learning. The reasoning framework models attribute groups on a graph and learns a projection function to adaptively assign local visual features to the nodes of the graph. After feature projection, graph convolution is utilized to perform global reasoning between the attribute groups to model their mutual dependencies. Then, the learned node features are projected back to visual space to facilitate knowledge transfer. An additional regularization term is proposed by distilling human parsing knowledge from a pre-trained teacher model to enhance feature representations. The proposed framework is verified on three large scale pedestrian attribute datasets including PETA, RAP, and PA-100k. Experiments show that our method achieves state-of-the-art results.

In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of the objects in long sequences. The recurrent network is pre-trained offline with auxiliary data and then online optimized to adapt to the target-specific object. A structured SVM is employed to account for the temporally correlated object appearance as well as distinguish the object from background distraction. Experiment results not only show that the appearance and dynamic patterns of the objects can be characterized via temporally correlated feature learning, but also demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods.

In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foreground and background objects respectively. An online dictionary learning method is developed to update the joint sparse dictionary by selecting both positive and negative bases from bags of positive and negative image patches/templates during tracking. A linear classifier is trained with sparse coefficients of image patches in the current frame, which are calculated using the joint sparse dictionary. This classifier is then used to locate the target in the next frame. Experimental results show that our tracking method is robust against object variation, occlusion and illumination change.