Zero-shot detection of daily objects in YCB video dataset
Authors: Wanqing Xia
Venue: ICLR 2022 Submitted (2022)
Keywords: zero-shot learning
object detection
multi-label learning
attribute vector

Abstract: To let robots be able to manipulate objects, they have to sense the location of objects. With the development of visual data collecting and processing technology, robots are gradually evolving to localize objects in a greater field of view rather than being limited to a small space where the object could appear. To train such a robot vision system, pictures of all the objects need to be taken under various orientations and illumination. In the traditional manufacturing environment, this is applicable since objects involved in the production process does not change frequently. However, in the vision of smart manufacturing and high-mix-low-volume production, parts and products for robots to handle may change frequently. Thus, it is unrealistic to re-training the vision system for new products and tasks. Under this situation, we discovered the necessity to introduce a hot concept which is zero-shot object detection. Zero-shot object detection is a subset of unsupervised learning, and it aims to detect novel objects in the image with the knowledge learned from and only from seen objects. With zero-shot object detection algorithm, time can be greatly saved from collecting training data and training the vision system. Previous works focus on detecting objects in outdoor scenes, such as bikes, car, people, and dogs. The detection of daily objects is actually more challenging since the knowledge can be learned from each object is very limited. In this work, we explore the zero-shot detection of daily objects in indoor scenes since the objects’ size and environment are closely related to the manufacturing setup. The YCB Video Dataset is used in this work, which contains 21 objects in various categories. To the best of our knowledge, no previous work has explored zero-shot detection in this object size level and on this dataset.

Under review as a conference paper at ICLR 2022
ZERO-SHOT DETECTION OF DAILY OBJECTS IN YCB VIDEO DATASET
Anonymous authors Paper under double-blind review
ABSTRACT
To let robots be able manipulate objects, they have to sense the location of ob- jects. With the development of visual data collecting and processing technology. robots are gradually evolving to localize objects in a greater field of view rather than being limited to a small space where the object could appear. To train such robot vision system, pictures of all the objects need to be taken under various orientations and illumination. In the traditional manufacturing environment, this is applicable since objects involved in the production process does not change fre- quently. However, in the vision of smart manufacturing and gigh-mix-low-volle production, parts and products for robots to handle may change frequently. Thus, it is unrealistic re-training the vision system for products and tasks. Un- der this situation, we discovered the necessity to introduce a hot concept which is zero-shot object detection. Zero-shot object detection is subset of unsupervised learning, and it aims to detect novel objects in the image with the knowledge learned from and only from seen objects. With zero-shot object detection algo- rithm, time can be greatly saved from collecting training data and training the vision system. Previous works focus on detecting objects in outdoor scenes, such as bikes, car, people, and dogs. The detection of daily objects is actually more challenging since the knowledge can be learned from each object very limited. In this work, explore the zero-shot detection of daily objects in indoor scenes since the objects' size and environment are closely related to the manufacturing setup. The YCB Video Dataset is used in this work, which contains 21 objects in various categories. To the best of our knowledge, no previous work has explored zero-shot detection in this object size level and on this dataset.
INTRODUCTION
Industrial robots have received more and more attention the manufacturing industry due to the rising cost of human labour and decreasing cost of industrial robots (Carlisle 2017). Since robots can handle heavy and repetitive jobs better than human, many manufacturing planets have replaced human labours on the production line with robots Gtobla-Gómed turing pattern of mass production, an industrial robot is only in charge of a certain processing step with dedicated parts. This manufacturing scenario does not require the robot change target ob- jects to work with frequently. However, with the recent development of control and communication technologies, the manufacturing industry is gradually evolving to high-mix-low--ollme production that provides personalized product for customers (Lu et al. 2020). This is also known as smart man- ufacturing, in this scenario, the manufacturing system will become more flexible. Instead of tied to specific task, robots will be allocated to various tasks depend on demand. Which requires the robots to be able to recognize a wide range of objects that could be involved during production.
al. 2017). In today's manufac-
With the development over years, today's object detection and recognition algorithms such as Faster- RCNN (Ren et al. 2015), SSD (Liu et al. 2016)), YOLO (Redmon et al. (016)) and DfficientDet(Tan al. 2020) have reached high performance. With enough collected training data, those off- the-shelf algorithms can be easily applied to the robots' vision system to recognize and localize objects involved in the production process. However, the data collection, labelling and training of neural network a tine-conuuning process and requires expertise the machine vision filed. Even there are data generation method that can generate synthetic images and labels from CAD models

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WWolhartt& & Lepetit 2015). training new neural network for additional new parts frequently is still not realistic in personalized production.
Zero-shot learning (ZSL) is learning paradigm that learns knowledge from seen categories and apply the knowledge to new categories in order to recognize objects that is never seen before. In the work carried out by Zhang & Saligrama (2015) and Zhang & Saligrama (016), zero-shot learning algorithms have already achieved a reasonable accuracy on classifying unseen objects. While zero- shot learning only aim to recognize unseen categories, a more realistic problem called generalized zero-shot detection (gZSL) is proposed by Xian et al. (0117), which aims to recognize both seen and unseen categories. However, gZSL still have flaws and cannot be directly applied solve the previously mentioned issues. For both ZSL and gZSL, they only focus on recognizing the object in an image. Thus, a big assumption made before applying those algorithms, which is only one object is appearing in the image and it is always located in the middle of the image. In this setting, ZSL. and g/SL. algorithms only need to analysis the categorical information in the image but not the location information. In other word, it takes the whole image as one object proposal. In real life, cameras attached on robots are always moving with the robot and cannot make sure the target object is always located in the middle of the camera's 's view. Thus, generalized zero-shot detection (gZSD) and gZSL have to be combined to achieve a vision system that can detect and recognize unseen objects. Where gZSD oversees localize seen and unseen objects in the field of view and g/SL. oversees the recognition and categorization of seen and unseen objects.
They are existing works in this field try to solve gSS and gZSL at the same time to make the whole scenario completed. For example, Rahman et al. (2018) tried to combine Faster RCNN with gZSL module to generate object proposals and class predictions. Zhuu al. (2019) worked on YOLOv2, using a single stage detector to generate object proposals. Previous works have all worked on outdoor datasets such as MS COCO (Lin et al.] 2014) and Pascal VOC(Everingham et al. 2010). Different objects in those datasets are categorized in categories. For example, man, woman and children are very different in term of their visual appearance but they are all allocated into the 'people" category. The varieties of objects in one category can increase the generality of the trained algorithm. In the case of detecting daily objects, datasets such as YCB Video (Xiang et al.) 2017) is chosen. 3D objects in this dataset is similar to the size and characteristies of the objects could appear in production pipeline. However, each object is unique and cannot be categorized into categories in 3D object datasets. Which brings the challenge of harder generalization to unseen objects.
Regarding the problems and challenges found in future manufacturing environment and g7SD, we propose to modify the base version of YOLOVv5 (Jocher et al.) 2021) to perform g7SL. on the YCB Video dataset. Compare to two stage detectors such Faster RCNN, one stage detectors such as YOLO and SSD are much faster to process. YOLOv5 as the latest version of YOLO series detectors, has been proved be faster and better than previous versions. Compare to the work done by Zhu et al. (2019) which used a modified YOLOv2 (Redmon & Farhadi (017)) to perform gZSD, YOLOv5 can output objects proposals in three different levels, thus have better coverage on object sizes. For training and testing our algorithm, four objects out of 21 objects in YCB Video dataset are picked as unseen objects. Any image that contains these four unseen objects will never appear during training but will be used during testing. For every object, their class labeling is translated into attribute vectors that's contains the colour and shape information of each object. Thus, we transformed the classical single label problem into a multi-label problem to let the neural network learn attribute labels and apply it to unseen objects. It needs to be notice that, in this work, we only work the gZSD problem but not gZSL. Which mean we only aim to localize seen and unseen object in the images by bounding boxes but not define the class label of the objects.
Our contributions in this paper are in three folds: 1.A novel neural network structure that based on YOLOvS and able to perform generalized zero-shot detection. The output bounding boxes can be further combined with other gZSL algorithm to achieve full zero-shot object detection and recogni- tion. 2.A novel splitting method for YCB Video dataset that splits the dataset by seen and unseen objects. This splitting can be used for both gZSD and gZSL research that related to daily objects. 3.A novel attribute labelling method for objects in YCB Video dataset. Covert the class labelling to 16 attributes that represents colour and shape information of an object for the neural network to learn.
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RELATED WORK
2.1 OBJECT DETECTION
Researches object detection and recognition have been developing rapidly in the past decade. The earliest image classification algorithm can be traced to the work published by Krizhevsky et al.) (2012). Since then, the recognition speed and accuracy of image classification algorithms have been improving continuously. These algorithms can be divided into two categories: two-stage detectors and one-stage detectors. Two-stage detectors such Faster RCNN Ren et al. (2015)and R-FCN Dai et al. (2016]generate object proposals by Region Proposal Network (RPN), then perform object classification based on these proposals. One-stage detectors like (Liu et al. 2016), YOLO (Redmon et al. 2016) and EfficientDet((an et al.] (020) generate object proposals and classify objects at the same time by dividing the image into grids. Thus, one-stage detectors' processing speed is faster than two-stage detectors' and gained more attention recently.
YOLOv5 (Jocher et al., 202T) used in this paper is the fifth version of the classical YOLO detec- tor. However, there is still an argument in this field about whether this algorithm is qualified to be considered as the fifth version. The maintainer of YOLOv5 also has not published a paper to justify the algorithm's 's ability. However, it is tested that on the MS COCO dataset (Lin et al. 2014), YOLOv5's speed and accuracy both outperformed the state-of-the-art algorithm, which is Google's EfficientDet. YOLOv5 is composed of three parts: Backbone, Neck and Head. When an image is passed into the network, it is first processed by the DarkNet (Bochkovskiy et al., (2020) backbone. Then passed into the PANet (Wang et al., 2019) neck, which processes and split the feature map into three different feature levels to have better coverage on objects in different sizes. Finally, the YOLO detector head will output predictions in three levels base on the feature maps. In this work, we will use YOLOv5 as the base algorithm, modify the detectors in the head part of the neural network, enable detect both seen and unseen objects.
2.2 ZERO-SHOE SHOT LEARNING
Given images with class labels, zero-shot learning (ZSL) aims to classify unseen class based on knowledge learned from seen classes (Fu et al. 2018). ZSL works by learning semantic informa- tion from seen classes and reassemble the semantic attributes predict unseen classes (Zhang & Saligrama (R16)Ra Rahman et al. 2018). In other words, the algorithm learns the mapping from the visual domain the semantic domain during learning and make predictions by mapping from the se- mantic domain to the visual domain. However, ZSL algorithms are designed for recognizing unseen classes, their performance degrades when both seen and unseen classes need to be recognized Xian et al. (2017) proposed generalized ZSL (gZSL), which released the constraint of recognition targets and included seen classes as well. However, ZSL and gZSL both assume that only one target exists in an image and it is located right in the middle of the image. They still lack the ability to isolate an object from the background or the occlusion of other objects.
2.3 ZERO- DETECTION AND RECOGNITION
There are several methods proposed recently solve the zero-shot detection and recognition prob- lem. These algorithms can not only detect the location of seen and unseen objects in an image but can also classify them. Most of them took the approach with two-stage detectors. In the research carried out by Bansal et al. (0118), Edge-Bor Box was used to generate regional proposals, and Re- gional Proposal Network (RPN) was used in work done by Rahman al. (2018). Using two-stage detectors is an easier way to achieve generalized zero-shot detection (gZSL) since these proposal generators do not need to be trained to work with unseen objects. They will generate proposals re- gardless of the content inside the bounding box. The confidence and class prediction are handled by following gZSL network. However, these methods are inevitably slow due to the workload of class evaluation that comes with large number of regional proposals. Zhhu al. (2019) proposed to use YOLOv2 as the backbone to detect and classify unseen objects. The use of one-stage detector made an improvement in terms of detection speed compared to two-stage detectors.
The mentioned methods above have all focused on outdoor scenes, and their commonly used datasets MS COCO (Lin al. 2014) and Pascal VOC (Everingham al. 2010). The objects inside these
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two datasets have high variability within each class. For robot that works in indoor environ- ment, recognizing objects at class level is not good enough since objects belong to the same category may have very different usage. For example, a housekeeping robot should be able differentiate the blue cup and the pink cup and hand them to a boy and girl, respectively, rather than recognize SUN RGB-I dataset (Song al.,
them both as cups. (Abdalwhab & Liu (2019) have tried 2015) to perform gZSD in an indoor environment. However, objects in the SUN RGB-D dataset labelled by class and in furniture size. In this work, will use the YCB Video dataset (Xiang et al., 2017), which includes 21 distinctive objects in desktop size. The object size and environ- ment in YCB Video dataset are closer related the setup we may encounter the manufacturing environment.
3 METHODOLOGY
3.1 PROBLEM DEFINITION
Assuming we have objects labelled as gi {b; a. Where the 4-dimensional vector b; {rx, Vi, wt. h,) denotes the ground truth bounding boxes center point location V. width and height w, h. a, 16-dimensional vector describes the feature attribute of the object gi. In the training dataset, all objects are all seen objects, represented by composed by seen objects only. In the test dataset, both seen Oseen objects and unseen objects O Ounseen are present, Osen n mnnene = 0. The goal of this work is to predict {bpred, Cpreds apred) for gpred E Oeen U O unnseen- The extra term Cpred represents the confidence level the existance of object within bounding box bpred-
0eeen- The valadation dataset is also
3.2 NETWORK ARCHITECTURE
910
MU
A0
50
80
63
YOLOvd Bonidens (Dorkhvet und Neek (Pahec)
40
40
40
196
40
336
40
40
20
20
p0
Doundding box and attribute prodiction
Objectness prediction
Extracted feotures
Fecture concatenation
Dutput concatenation
Figure 1: Simplified structure of our YOLOv5-ZS network
The simplified architecture of YOLOv5-ZS network is shown in Figure 1 The network takes an RGB image as input and output predictions in three feature levels. The input image size is 640*640*3 in our network since all images in the YCB Video dataset are 640+480 pixels. They are padded with grey pixels on top and bottom to become a square shape. The image is first processed by the Backbone (DarkNet) and Neck (PANet). The size of DarkNet and PANet changeable in YOLOv5. By changing the number of convolutional layers and feature map depth, four versions of YOLOv5can created, and they are YOLOv5s (small), YOLOv5m (medium), YOLOv5I (large) and YOLOv5x (extra-large), As the network becomes bigger and deeper, their detection accuracy will increase, but the detection speed decreases. In our work, we chose to use YOLOv5s. Tensor T represents the block of are extracted features in three levels, they are passed to the following blocks: bounding box and attribute prediction (T,), feature concatenation (T.). objectness prediction (T,,) and output concatenation (T,). They are detailly explained in the following sections.

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3.2.1 FEATURE EXTRACTION
The T block is composed of three tensors, and its structure inherited from PANet. Path Aggre- gation Network (PANet) proposed by [Wang et al. (2019) is network that has both top-down and bottom-up dataflow. The bi-directional data flow ensures the image features are kept better in the later convolutional layers. By using PANet, I shows, the feature tensors are in size 80*80* 128, 40*40*25,, 20"201.... As the network goes deeper, the feature map gets smaller and deeper. From this block, each data block will consist of three tensors. However, for easier representation and clearer graph, we represent each later block as tensor T.
also get image features in three levels. As Figure
3.2.2 OBJECT LOCALIZATION
The location of bounding boxes predicted in block TG. Each tensor in this block has the same depth of 60. In YOLO series detectors, the width and height of the detection layer represent the number of grids on the image. For example, 80*80 means the image is evenly divided into 6400 grids, and each grid is responsible for predicting bounding boxes that the center point falls on this grid. As the grid number becomes smaller, the size of each grid becomes bigger and hence have a better focus on bigger objects. The three different grid sizes thus allow the network to detect objects in various sizes. Each tensor's 's depth in this block is 60 since the network needs to make three predictions with anchors different width/height ratios for each grid. For each prediction, b, is digits and apred is 16 digits. Thus the tensor has 16)4+16) 60 channels. It needs to be notice that the bounding box prediction b, = {tz, ty. tw. tn. are relative to the location of the grid and size of the anchor. The actual location and size of the bounding box Upred need to be calculated with the following equations.
2 (1x) 0.5 Cz w = Pu. (2+ =)w
y = 2 o 0.5 + Cy h = Ph. * (2 + (t,)))
Where Cz; Cy indicate the location of the top-left corner of grid cell and pw, Ph indicate the width and height of the anchor.
3.2.3 AITRIBUTE PREDICTION
As mentioned in the previous section, block T also needs predict the 16-dimensional attribute vector for each object. Unlike other works describing objects with semantic vectors that learn by Word2Vec or FastText, our attribute vector carried out by human eye evaluation. The elements in the attribute vector are some common colors and shapes that appear in all objects. There are two reasons that we took a different approach. (1) Class names such as "people" in previous works can be easily translated into semantic vectors using existing algorithms. While object names in YCB Video dataset are instance-specific such as "master chef can", cannot be directly translated. (2) There is visual variation for each object, and by human evaluation. The 16-dimensional attribute vector contains: white, blue, red, yellow, silver, black, brown, bottle, cup, can, clamp, slim, circle, cylinder, box, rectangular. Fach object g; will be described by several attributes in the form of one-hot embedding. For example, object 'red cup' has the attributes of red, cup, circle, cylinder will have the attribute vector [0 0 1 0 0 1 0 OJ. The corresponding location of red, cup, circle, cylinder is labelled *1 and the rest are labelled '0', The predicted output apred 16-dimensional vector of floating- point numbers. Each number's value is between 0 and 1, indicate the confidence level of an attribute.
determine the attributes object contains
3.2.4 CONFIDENCE PREDICTION
After bounding box and attribute prediction, block T, and block T are concatenated together to form block T Our objectness prediction layer is learned from the concatenated layer T In the original YOLOv5 detection layer, objectness confidence learned in the same block as Tu. How- ever, learning objectness confidence only from the feature layer will cause the network to only recognize seen objects and treat all unseen objects as background. Thus, we concatenate the T block and T block together to let the network also learns from the bounding box and attribute pre- dictions. In this case, the network will be able to recognize unseen objects by the attributes they have. Detectors in T, only have three channels, each channel of grid cell is the confidence score
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of the corresponding bounding box. The network will make (80~80+40+40++0-20) *3 25200 predictions in total. In the end, the output block To is the concatenation of bounding box, attributes and objectness prediction.
3.3 LOsS FUNCTION DESIGN
The total loss in our algorithm is composed of three parts: localization loss, attribute loss and ob- jectness loss. In the following sections, we will show how the loss functions are designed and implemented.
3.3.1 LOCALIZATION LOSS
In YOLOVS, the localization loss is calculated by cloU loss proposed by Zheng et al. 2021). Com- pare to the original IoU loss, cloU loss more precise and converges much faster. To calculate the cloU loss, need to calculate some parameters first:
IoU Arcupred O Areugt
Areapred U Areagt
(-IoU') +
Wgt arctan
W upred y2
(arctan
π
higt
hppred
When the loss is calculated, not all bounding box predictions are used. A predicted bounding box is used only when center point falls into the same grid cell as the ground truth bounding box's 's center point and has the highest loU among three predictions in the same grid cell. We denote the selection of bounding box predictions using Åo it is set when selected otherwise Since our output has three different feature levels, we define as the level number. 'he total number of predictions m equal to 309200, 40=4003=4800, 20=20=3=1200 when n is equal to 1, 2, respectively. The formal localization loss is defined as the summation of mean cloU loss in each layer, shown in the following function. Where is the distance between two boxes' center and c is the diagonal length of the minimum enclosing box of two boxes.
IoU, + df + az;4)) c
Lioc Σ
11
m
1=1
3.3.2 ATTRIBUTE LOSS
For calculating the attribute loss, we used Binary Cross Entropy (BCE) loss with sigmoid function σ). Since the ground-trutt truth value of an attribute ea either or the predicted attribute value pred need to be passed into sigmoid function first to regulate the number to between and I. A new term 2 is introduced in this function, and it represents the total number of attributes in the vector, which is 16. A bounding box's attribute loss is the summation of BCE loss every attribute term. All other symbols remain the same meaning as in section 3
Latt
(- + (1 (- (- - o
177
jml
3.3.3 OBJECTNESS LOSS
Different from localization loss and attribute loss, the objectness loss is calculated from all predic- tions rather than positive predictions only. Thus, the term X; dropped in the objectness calculation. BCE loss with sigmoid function is also used in the objectness loss calculation. p is the ground truth probability of the presence of an object in the bounding box, which equals when an object is present and otherwise. pred is the predicted confidence score, regulated between and with the sigmoid function.

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Lobb
al P
log(o(p1)
log
-- -log(1 a (vpred)))
m72
4 EXPERIMENTS
4.1 DATASET SETTING
In the following Table I we show how the original YCB Video dataset is splitted idation and test dataset. The YCB Video dataset is consist of 92 videos, and each has thousands of frames. 21 daily objects are included in the dataset, and some of them are placed in the scene of video. Since during the video taking, the setup of objects does not change, objects in a video record remain constant. Thus, once the unseen objects is picked from all objects, all images that contain any of these four objects need to be allocated to the test dataset. We picked four objects as unseen objects in our split, they are gelatin box, mustard bottle, pitcher base and power drill, labelled with bold text in [Tble[]] In terms of detection difficulty, gelating box and mustard bottle are easy, pitcher base is harder, and power drill is hardest. This conclusion carried out based on the attributes they have compared to all the seen objects, it is also proved later with the detecting score.
train, val-
After four unseen objects are picked, videos that only contain seen objects are selected to be used in the train and validation dataset. The rest 61 videos that contain least one unseen object allocated to the test dataset. The 31 videos only contain seen objects have 45272 frames in total. We randomly picked 20% of them (9040 images) and put them into the validation dataset, the rest 80% (36232 images) are placed in the train dataset. For test dataset, all frames in the 61 videos (88664 images) used. In Table 1. also show the number of labels for each object in each dataset.
Table I: Number of images and labels for each object in each set
Number Object name
Train labels 36232 images
Validation labels 9040 images
Test labels 88664 images
master chef can cracker box
8919
2252
18088
1
10073
2552
19771
2
sugar box
12393
3055
16965
3
tomato soup can
13342
3213
23206
4 mustard bottle
0
0
32321
5
tuna fish can pudding box
7534
1868
21234
6
5214
1384
26334
gelatin box
33786
8
potted meat can banana
10247
2549
19354
10060
2493
20275
10 pitcher base
0
0
26478
11
bleach cleanser
11918
2933
15755
12
bowl
7808
1945
4898
13
mug
11051
2797
12858
14
power drill
0
27883
15
wood block
6649
1669
12782
16
scissors
12463
3210
11710
17
large marker
9211
2277
19305
18
large clamp
7342
1804
16661
19
extra large clamp
12841
3186
9575
foam brick
7268
1745
19413
4.2 TESTING RESULT
During testing. all images in the test dataset were used. Since this work focus on detection only, we will only evaluate the recall rate of the algorithm. We define that if an object's ground truth bounding

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box (GT) and the predicted bounding box 's IoU is bigger than 0.5, the object is successfully detected, noted as True Positive (TP). The recall rate is defined as:
TP
Recall
GT
In the following table, the recall for each object is shown.
Table 2: Recall for each object
Number
19
4.3 DISCUSSION
Base on the testing result, we can say that the algorithm works well when there are similar seen objects, but for onjects that very different from objects. From Table 2 the first thing notice is that the recall rates for different objects have a large variation. Some seen objects even have lower recall rate than unseen objects. The main reason causing this the unbalanced number of labels in the train dataset and the test dataset. For example, the number of train labels for sugar box 0.75 times of the number of test labels, and its recall reached 0.83. For wood block, its number of train labels is only half the number of test labels, and its recall rate is only 0.21. Another reason that affects the recall rate is the variation of illumination. Since YCB Video dataset is consists of videos, images in the train dataset can only cover very limited range of illumination conditions. Thus, the algorithm will perform worse on the test images with illumination conditions that have never been met before.
For the four unseen objects, the recall rate for object number and is higher than object number 10 and 14, which is similar to what expected. Especially for object numer 4, its recall rate is even higher than many seen objects. Object number 4 has the highest recall rate among all unseen objects because its color and shape have commonly appeared contained in object number 10 and 14 are hardly seen in the train dataset. Thus, we can conclude that seen objects with similar color shape unseen objects increase the detection rate of unseen objects.
seen objects. However, attributes
5 CONCLUSION
In this paper, we proposed to use modified YOLOv5 neural network to perform generalized zero- shot detection seen and unseen objects. We also proposed novel splitting method for YCB Video datasct to train and test gZSD algorithms. By changing the final detection layers YOLOv5, the YCB Video dataset split with our
have significantly improved its gZSD performance proposal. For industrial robots that works in a flexible and dynamic manufacturing environment, gZSD algorithm for detecting daily objects is more feasible solution than the traditional vision algorithm that requires training for every object. In our experiment, we found that our algorithm is more sensitive color rather than shapes. Thus, in the future, we experiment on RGB-D images rather than RGB images to evaluate the improvement brought by the extra depth channel.
REFERENCES
Abdalwhab Abdalwhab and Huaping Liu. Zero-shot object detection for indoor robots. 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2019.
Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pp- 384-400, 2018.
Alexey Bochkovskiy, Chien-Yas Yao Wang, and Hong- Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004 10934, 2020.
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Under review as a conference paper at ICLR 2022
Brian Carlisle. Pick and place for profit: Using robot labor to save money, Sep 2017.
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. R-fen: Object detection via region-based fully convo- lutional networks. In Advances in neural information processing systems, pp. 379-387, 2016.
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2): 303-338, 2010.
Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong. Recent advances in zero-shot recognition: Toward efficcieff understanding of visual content. JEEE Signal Processing Magazine, 15(1):112-125, 12-125, 2018.
Glenn Jocher, Alex Stoken, and Jirka Borovec. Yolov5, 2021. URL https:///gtth/i com/ u1 ultralytics/yolle
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep con- volutional neural networks. Advances in nearal information processing systems, 15:1009-1105, 2012.
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-755. Springer, 2014.
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng- Cheng--Yagg Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pp. 21-37. Springer, 2016.
Yuqian Lu, Xun Xu, and Lihui Wang. Smart manufacturing process and system automation-a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56:312-325, 2020.
Shafin Rahman, Salman Khan, and Fatih Porikli. Zero-shot object detection: Learning to simul- taneously recognize and localize novel concepts. In Asian Conference on Computer Vision, pp- 547-563. Springer, 2018.
Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271, 2017.
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28: 91-99, 2015.
Sandra Robla-Gómez, Victor M Becerra, José Ramón Llata, Esther Gonzalez-Sarabia, Carlos Torre- Ferrero, and Juan Perez-Oria. Working together: A review on safe human-robot collaboration in industrial environments. IEEE Access, 5:26754-26773, 2017.
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understand- ing benchmark suite. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 567-576, 2015.
Mingxing Tan, Ruoming Pang, and Quoe V Le. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp- 10781-10790, 2020.
Kaixin Wang. Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image semantic segmentation with prototype alignment. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 9197-9206, 2019.

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Under review as a conference paper at ICLR 2022
Paul Wohlhart and Vincent Lepetit. Learning descriptors for object recognition and 3d pose esti- mation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp- 3109-3118, 2015.
Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning-the good, the bad and the ugly. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1582-4591, 2017.
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. Posecnm: convolutional neu- ral network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.0015 2017.
Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via semantic similarity embedding. In Proceedings of the IEEE international conference computer vision, pp. 41644166664 4166-4174, 2015.
Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via joint latent similarity embedding. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6034- 6042, 2016.
Zhaohui Zheng, Ping Wang. Dongwei Ren, Wei Liu, Rongguang Ye, Qinghua Hu, and Wangmeng Zuo. Enhancing geometric factors in model learning and inference for object detection and in- stance segmentation. IEEE Transactions on Cybernetics, 2021.
Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama. Zero shot detection. IEEE Transactions on Circuits and Systems for Video Technology, 30(4):998-1000) 2019.
10


Reviews:
[{"review_id": "vYQ-VNHuQF_", "reviewer": "Reviewer_uZSA", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n1.\tThe network is designed to output the object attribute vectors, which could be useful when the object set is fixed or strictly limited to these attributes. \n\nWeaknesses:\n1.\tThe proposed method is a simple modification of the YOLOv5 network. The proposed object attribute prediction branch is questionable as the attribute slots are predefined, fixed and do not generalize to new attributes. \n2.\tThe proposed method does not give object ID but only bounding boxes. Thus the method is reduced to an objectness detector. There are many possible baselines (e.g. pretrained YOLOv5 as objectness detector) but the paper compares to none. \n3.\tThe paper does not mention or compare to relevant previous works like [1] and [2]\n4.\tThe paper does not conduct any ablation study, with the proposed method empirically unverified. \n5.\tThe paper only evaluates one dataset and uses the recall as the sole evaluation metrics, ignoring the harm of false positives. \n6.\tThe results are not good, only on average around 0.3 in recall on unseen objects. \n\nMinor issues (grammar, typos, etc):\n1.\t“since the knowledge can be learned from each object is very limited” -> “since the knowledge that can be learned from each object is very limited”\n2.\t“ZSL and gZSL algorithms only need to analysis the categorical information” -> “ZSL and gZSL algorithms only need to analyze the categorical information”\n3.\t“Covert the class labelling to 16 attributes that represents” -> “Convert the class labelling to 16 attributes that represents”\n4.\tThere are still many grammar/typo issues in the paper. I suggest the authors thoroughly check them, although this is not considered in the evaluation. \n\n[1] P. Ammirato, C.-Y. Fu, M. Shvets, J. Kosecka, and A. C. Berg, “Target Driven Instance Detection,” arXiv:1803.04610 [cs], Oct. 2019, Accessed: May 19, 2020. [Online]. Available: http://arxiv.org/abs/1803.04610\n[2] J.-P. Mercier, M. Garon, P. Giguère, and J.-F. Lalonde, “Deep Template-based Object Instance Detection,” arXiv:1911.11822 [cs], Nov. 2020, Accessed: Nov. 17, 2020. [Online]. Available: http://arxiv.org/abs/1911.11822\n", "summary_of_the_paper": "This paper tries to tackle the zero-shot object instance detection problem, but without handling object classification, it is essentially a method for objectness detection. The paper uses a modified YOLOv5 network with an object attribute output branch. The object attributes are 16 binary attributes pre-defined and fixed by human evaluation. The method is evaluated on the YCB-Video dataset, where 4 out of 21 objects are held out as unseen objects for zero-shot evaluation. The paper reports unconvincing results, and no comparison with related work or ablation studies. ", "summary_of_the_review": "The proposed method lacks novelty or contribution. The experiments are not complete or convincing. The results do not seem promising. Therefore I recommend rejecting this paper.  ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "lXEzQyenui", "reviewer": "Reviewer_TYzZ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths\n\n1) The overall problem seems to be practically relevant. Most existing object detectors and classifiers are desired not to overfit to the color or size of the object as long as the objects are from the same class. If the neural network saw only black dogs during training, it is desirable to predict white dogs also in deployment. \n\n2) Generalized zero-shot learning is less explored compared to zero-shot learning.\n\nWeaknesses\n\n1) The novelty is not very strong. Attribute-based zero-shot learning is a common paradigm and is discussed in [A,B,C,D]. Attribute-based ZSD is also discussed in [E]. The authors extend it to detection on a dataset with different classes of objects than seen in other papers. Further, they use YOLOv5 instead of YOLOv2 as the backbone. There are some changes to the neural net architecture but there is no empirical evidence to suggest those are beneficial.\n\n2) The empirical results are underwhelming. The recall ratio is very low for 2 classes. If the authors are claiming zero shot detection, then the method should be able to detect unseen objects ideally even if those attributes are not a lot in the training set. Further, just a single dataset is used and there is no comparison to prior art. If none of the prior art is applicable to this problem, the authors need to explain why that is the case.\n\n3) The attribute list is too limiting and will not scale. These attribute lists are just directed towards this single dataset. For general applications, it might be difficult to list out and label all attributes in all images. Further, a hierarchical label might be more useful. Due to this reason, it might be more beneficial to learn embeddings/representations that are more general though not interpretable. \n\nQuestions\n\n1) Why is the mAP which is the standard score for object detection, not reported? The algorithm can still generate false positive.\nIn [E], the authors report mAP over all classes as they do not classify the objects.\n\n2) More papers on ZSD that are not cited: [F,G,H,I].\n\n\nReferences:\n\n[A] https://ieeexplore.ieee.org/document/6571196\n[B] https://ieeexplore.ieee.org/document/6126373\n[C] https://ieeexplore.ieee.org/document/6751374\n[D] https://link.springer.com/chapter/10.1007/978-3-642-15555-0_10\n[E] https://arxiv.org/pdf/1803.07113.pdf\n[F] https://www.sciencedirect.com/science/article/pii/S2667241321000124\n[G]https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Dont_Even_Look_Once_Synthesizing_Features_for_Zero-Shot_Detection_CVPR_2020_paper.html\n[H] https://ojs.aaai.org/index.php/AAAI/article/view/6868\n[I] https://arxiv.org/abs/1805.06157", "summary_of_the_paper": "The paper performs zero shot detection of seen and unseen objects in scenarios with more fine-grained division of objects. For example, in practical computer vision applications such as industrial and indoor environments, it might be necessary to differentiate between the same object in different colors, sizes and shapes. In this paper, unseen objects can be classified based on the attributes: white, blue, red, yellow, silver, black, brown, bottle, cup, can, clamp, slim, circle, cylinder, box, rectangular. The authors perform zero-shot detection using a simple approach of predicting attributes. The attributes are labeled in the training images and the method has to generalize for unseen objects in the test images.", "summary_of_the_review": "The problem the paper seeks to solve is relevant and interesting. However, there is insufficient empirical or logical arguments put forth to justify why this approach is good or better than previous approaches. Empirical results are not convincing, the novelty is mainly in the use of a new dataset and literature survey is not thorough.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "NLzCZveEgr9", "reviewer": "Reviewer_8vns", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper presents an interesting application of ZSL. While in general ZSL has been a fairly academic problem, the author's method is able to be used in (constrained) real-world settings with multiple objects within the scene. They pose it in robotics and smart manufacturing however, can be generalised.\n\nThe approach to extend Yolo to include the attribute makes a lot of sense, however, it raises the question is all you need is the attribute encoding? No additional contrastive learning, hard-mining. In the results/discussion, you comment on the distribution, this would be a large limitation of the method as it isn't possible to have balanced distributions from real-world images of multiple objects\n\nEvaluation is the most weakest part. Given the relatively simple adaption, different backbones could have easily been evaluated R-CNN ... with their varying backbones. Given the authors are trying to motivate a new problem, only showing their own results is not convincing. \n\nAlso, a factor for the evaluation here would be the computation time with the standard time vs accuracy debate in object detection methods.\n\n\n\nIn the related work the claim \"The varieties of objects in one category can increase the generality of the trained algorithm.\", is there evidence behind this (needs citation)? In general, this seems counterintuitive. The more general the class the harder it is to classify as key pixels for decision making are harder to select in a broad setting. \n\n\nTypos / Clarifcations:\nAbstract: \"objects in a greater field of view rather than being limited to a small space where the object could appear.\" isn't clear the use of field of view that doesn't seem to make sense in this context\nRelated work: \"many manufacturing planets have replaced\" planets = plants\nRelated work: \"They are existing works in\" They = There\n", "summary_of_the_paper": "The paper tries to generalise the generalised zero-shot learning (gZSL) problem to full images as an object detection problem instead of an image classification problem. They propose the use of the YoloV5 network as a backbone for the prediction of the attribute encoding of the detected bounding boxes. In such a way they can then use the attributes to suppress background objects/predictions based on the overlap of seen and unseen objects. Therefore they combine the localisation and objections loss with an attribute loss (BCE) to train the network. They evaluate over YCB video dataset on 21 objects for their method only.", "summary_of_the_review": "While the paper presents an interesting application, the method is a very baseline method with little adaption to the prior architecture. The evaluation is missing a benchmark to make this paper competitive and establish the application problem,\n\nIt is also a very application problem and may not be the best fit for ICLR but instead a robotics or computer vision conference as it adapts an ML method to a generalised problem.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "DsIVM7E3I8g", "reviewer": "Reviewer_Ajjn", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strength\n1. The paper tackles on a practically very important problem. The problem is well-motivated from the viewpoint of a practical application.\n\nWeakness\n1. The technical novelty of the proposed method is very limited. Basically, the difference is only in the attribute prediction part, but the modification is very straightforward even in that part.\n1. The experiment is not enough for supporting the effectiveness of the proposed method. It is not compared with any existing methods. There is little analysis.\n1. The second contribution claimed by the authors is weak.\\\n`2.A novel splitting method for YCB Video dataset that splits the dataset by seen and unseen objects.`\\\n Actually, they just provide one of the arbitrary splits, and this cannot be regarded as contribution.\n1. The paper claims that they tackle on generalized zero-shot detection, but I believe “localization” is more appropriate word than “detection” since the proposed method does not recognize object classes.\n1. The manuscript needs careful proof-reading for English style and grammar issues. For example, but not limited to \n    - robots will be allocated to various tasks depend on demand. Which requires the robots\nto be able to…\n    - For every object, their class labeling is translated into attribute vectors that’s contains the colour and shape information of each object.\n    - Tensor $T_f$ represents the block of are extracted features in three levels, they are passed to the following blocks\n    - It needs to be notice that…\n    - our attribute vector is carried out by human eye evaluation\n    - For example, object ‘red cup’ has the attributes of red, cup, circle, cylinder will have the attribute vector [0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0].\n    - The 31 videos only contain seen objects have 45272 frames in total\n    - YCB Video dataset is consists of …\n\n", "summary_of_the_paper": "This paper tackles on a generalized zero-shot localization task. YOLOv5 network is slightly modified to detect pre-defined attributes. The proposed method is evaluated on YCB video dataset.", "summary_of_the_review": "I found the paper deals with a practically very important and motivation of the paper is good. However, the novelty of the paper is very limited, and the experiment is not enough for showing the advantage of the proposed method over prior works.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "09kpT_ytor8", "reviewer": "Reviewer_kFeR", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "-Strengths: The paper is well-organized and the problem is also well-defined.\n-Weakness: \n1) The proposed approach is mainly based on YOLO5 and the novelty is really limited. No new or novel zero shot learning algorithms are proposed to solve this specific problem.\n2) There's no explanation about why the proposed model can detect the unseen objects.\n3) No ablation study are given to validate the effectiveness of each proposed component. There's only on experiment on YCB Video dataset, and the comparison with other state-of-the-arts zero shot learning methods is missing. \n", "summary_of_the_paper": "This paper aims to solve the object detection under the object manipulation scenario. More specifically, it attempts to design a zero-shot object detection algorithm that can detect unseen objects with the knowledge learnt from seen objects. The authors conduct experiments on YCB Video dataset to valid the effectivness of the proposed method.", "summary_of_the_review": "Based on the weakness of this paper mentioned before, I think the quality of this paper is quite low and cannot be accepted.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: All five reviewers unanimously agree that the paper needs to be rejected. One of the main concerns is the lack of technical novelty/originality. The reviewers also point out lacking citation and comparison to prior work, and missing experiments. The authors have not provided any rebuttal.This paper describes an approach for zero shot detection of seen and unseen objects in scenarios. All five reviewers unanimously agree that the paper needs to be rejected. One of the main concerns is the lack of technical novelty/originality. The reviewers also point out lacking citation and comparison to prior work, and underwhelming experiments. The authors have not provided any rebuttal.

We recommend rejecting the paper.
Decision: Reject