Title: Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning
Venue: NeurIPS 2023
Accepted: yes
Keywords: neuro-symbolic

visual reasoning

human activity understanding
Abstract: Human reasoning can be understood as a cooperation between the intuitive, associative "System-1'' and the deliberative, logical "System-2''. For existing System-1-like methods in visual activity understanding, it is crucial to integrate System-2 processing to improve explainability, generalization, and data efficiency. One possible path of activity reasoning is building a symbolic system composed of symbols and rules, where one rule connects multiple symbols, implying human knowledge and reasoning abilities.
Previous methods have made progress, but are defective with limited symbols from handcraft and limited rules from visual-based annotations, failing to cover the complex patterns of activities and lacking compositional generalization. 
To overcome the defects, we propose a new symbolic system with two ideal important properties: broad-coverage symbols and rational rules. Collecting massive human knowledge via manual annotations is expensive to instantiate this symbolic system. Instead, we leverage the recent advancement of LLMs (Large Language Models) as an approximation of the two ideal properties, i.e., Symbols from Large Language Models (Symbol-LLM). 
Then, given an image, visual contents from the images are extracted and
checked as symbols and activity semantics are reasoned out based on rules via fuzzy logic calculation.
Our method shows superiority in extensive activity understanding tasks. Code and data are available at https://mvig-rhos.com/symbol_llm.

Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning

Abstract

Human reasoning can be understood as a cooperation between the intuitive, associative 'System-1' and the deliberative, logical 'System-2'. For existing System1-like methods in visual activity understanding, it is crucial to integrate System-2 processing to improve explainability, generalization, and data efficiency. One possible path of activity reasoning is building a symbolic system composed of symbols and rules, where one rule connects multiple symbols, implying human knowledge and reasoning abilities. Previous methods have made progress, but are defective with limited symbols from handcraft and limited rules from visual-based annotations, failing to cover the complex patterns of activities and lacking compositional generalization. To overcome the defects, we propose a new symbolic system with two ideal important properties: broad-coverage symbols and rational rules. Collecting massive human knowledge via manual annotations is expensive to instantiate this symbolic system. Instead, we leverage the recent advancement of LLMs (Large Language Models) as an approximation of the two ideal properties, i . e ., Symbols from Large Language Models ( Symbol-LLM ). Then, given an image, visual contents from the images are extracted and checked as symbols and activity semantics are reasoned out based on rules via fuzzy logic calculation. Our method shows superiority in extensive activity understanding tasks. Code and data are available at https://mvig-rhos.com/symbol_llm .

1 Introduction

Human reasoning can be understood as a cooperation between two cognitive systems: the intuitive, associative 'System-1' and the deliberative, logical 'System-2' [25]. An example of System-2 reasoning is illustrated in Fig. 1a. A feather and an iron ball are dropped at the same height in a vacuum, which one will land first? Human intelligence derives the answer from the Universal Gravitation Law, i . e ., reasoning in a physical symbolic system to avoid intuitive errors. As a critical component in building AI systems, visual activity understanding urgently needs System-2 reasoning. Previous works [26, 17, 30, 19] typically establish the mapping between visual inputs and natural language, where knowledge is embedded into some non-symbolic form such as the weights of language models [24]. Though effective, these System-1-like methods depend on large-scale visual data, thus suffering from diminishing marginal utility. Also, it fails in explainability and generalization due to black-box computing. Thus, it is important to integrate System-2 reasoning.

With one glance at an image, we can effortlessly ground visual inputs to symbols/concepts and apply commonsense reasoning to imagine the world beyond the pixels, a process similar to the gravity case. Fig. 1b shows a possible path for System-2 visual reasoning for human activity: a symbolic system composed of symbols and rules. One rule ' Hip seated in a boat ∧ Hands control an engine ∧ Hands hold onto the side of a boat | = Human ride a boat ' connects symbols

System-2 Reasoning

𝐹 = 𝐺

𝑚0𝑚

𝑟 2

𝐹 = 𝑚𝑎

𝑎 = 𝐺

𝑚0

𝑟 2

𝑣1

= 𝑣2

……

…

𝒄

:

Human

ride a boat

𝒎𝟑

:

Hands hold onto

the side of a boat

𝒎𝟐

:

Hands control

an engine

𝒎𝟏

:

Hip seated in a boat

Rule:

𝒎𝟏 ∧ 𝒎𝟐 ∧ 𝒎𝟑

⊨ 𝒄

Abstract

Abstract

Answer

Activity

Semantic

𝑎1

= 𝑎2

Which will

land first?

feather

a)

b)

17

( e . g ., ' Hands control an engine ' ' Human ride a boat ') and implies human commonsense. Then, given an image, symbols are first extracted from visual inputs and then activity semantics are deduced based on rules. In this paper, we focus on this line of System-2 reasoning and zoom in from general visual understanding to human activities in various complex contexts, which is a challenging problem in visual understanding.

Current activity reasoning methods are not well equipped with System-2 reasoning. For instance, HAKE [21, 20] adopts a similar path as Fig. 1b from the perspective of human body part states and makes progress toward activity reasoning, but is defective with limited symbols from handcraft (93 primitives) and limited rules from visual-based annotations (100 K+ images). For example, Fig. 1b shows several rules related to the activity ' Human ride a boat ', e . g .,

(R1, green) Hip seated in a boat ∧ Hand hold onto the side of the boat ∧ Hand operate the steering wheel | = Human ride a boat

(R2, orange) A rope is attached to a boat ∧ Hand hold the rope ∧ Hand adjust a sail ∧ Feet hold onto the rope ∧ Leg stretched out | = Human ride a boat

However, the rules summarized by [20] are '(R3) Hip sit in ∧ Hand hold | = Human ride a boat ' '(R4) Hand hold ∧ Feet close with | = Human ride a boat ' , which fail to cover the complex patterns of human activities. Thus, it lacks compositional generalization, i . e ., a generalized understanding of visual concepts, their relationships, and their novel combinations.

In view of this problem, we propose a novel symbolic system that implies abundant human knowledge and organizes diverse reasonable rules as in Fig. 1b. To achieve this, the symbols should be with broad semantic coverage to express various activity contexts. Moreover, the rules should be rational and unambiguous . R4 is a failure case, where ' Hand hold ' and ' Feet close with ' do not necessarily lead to ' Human ride a boat ', and ' Human tie a boat '' Human exit a boat ' are other possible conclusions. Ambiguous semantics should be corrected in rules to avoid confusion. Collecting massive human knowledge via manual annotation is expensive to instantiate this symbolic system. Our main insight is Symbols from Large Language Models ( Symbol-LLM ), i . e ., leveraging the recent advancement of Large Language Models (LLMs) to approximate the above two important properties. This is achieved by the symbol-rule loop and entailment check strategy. Then, the symbolic system can be used to reason with visual inputs, where visual contents from the images are extracted and checked as symbols and activity semantics are reasoned out via fuzzy logic calculation .

In conclusion, our main contributions are: 1) We point out that current System-2 reasoning [20] suffers from a defective symbolic system with hand-crafted symbols and limited, ambiguous rules. 2) To overcome the defects, we propose a novel symbolic system with broad-coverage symbols and rational rules. Further, we propose Symbol-LLM to instantiate it and show how it helps visual reasoning. 3) In extensive activity understanding tasks, our method shows superiority in explainability, generalization, and data efficiency.

2 Related Work

Activity Understanding [22, 10] is still a difficult task compared with object detection [28] or pose estimation [9]. This is caused by its complex visual patterns and long-tail data distribution [32].

There are mainly image-based [5, 15], video-based [14, 4] and skeleton-based [31, 7] methods. In this paper, we focus on image-based methods.

Vision-Language Models (VLMs) have been intensively studied recently. Trained with web-scale image-text pairs, they can make zero-shot predictions on various recognition tasks [37] including human activities. Though effective, previous System-1-like methods [26, 17, 30, 19] need to be improved in explainability, generalization, and data efficiency. Thus, integrating System-2 reasoning, which is our main focus, is important.

Probabilistic Programming [27, 11] unifies probabilistic modeling and traditional programming. We follow the principles of the standard first-order logic (FoL) in probabilistic programming, e . g ., logical connectives ∧ , ∨ . The standard FoL typically solves simplified relational tasks, where symbols are definitely True/False. However, when adapting FoL in vision reasoning tasks, the symbols are not known to be True/False. Instead, the models are required to predict the symbol probabilities (usually via a neural network). Thus, a hybrid 'neuro-symbolic' method is adopted.

Neuro-Symbolic Reasoning provides a possible way of combining System-1 and System-2 learning, where knowledge is represented in symbolic form and learning and reasoning are computed by a neural network. [13]. Recently, researchers utilize neural-symbolic reasoning in tasks including visual question answering [2, 16, 34, 24, 29], decision-making [6], motion programming [18], scene understanding [23]. Focused on activity reasoning, HAKE [21, 20] propose an intermediate space spanned by human body part states as primitives. To overcome its inherent defect in compositional generalization, we propose a novel symbolic system with broad semantics and rational rules.

3 Method

3.1 Formulating the Symbolic System

3.1.1 Structure

We first analyze the structure of the symbolic system. As aforementioned, the activity symbolic system is composed of rules that connect symbols, which implies human knowledge and reasoning abilities. Formally, one rule involves several symbols, where one of them is the conclusion and the others are premises . Each rule takes the form r : m 1 ∧ m 2 ∧ . . . ∧ m n r | = c , where M = { m i } n r i =1 is the premise set and c is the conclusion. Each conclusion corresponds to more than one rule with varied combinations of premise symbols because one activity typically has different visual patterns. Multiple rules for one conclusion are logically connected with ∨ , i . e ., one conclusion holds if at least one rule is satisfied. A hyper-graph is a proper data structure to handle the complexity of the connection. Therefore, we define the structure of the activity symbolic system as a B -Graph [12].

Definition 1. (Directed Hypergraph [3]) A hypergraph is a generalization of a graph in which an edge can join any number of vertices. A directed hypergraph is a pair ( X , E ) , where X is a set of vertices, and E is a set of pairs of subsets of X . Each of these pairs ( D,C ) ∈ E is a hyperedge; the vertex subset D is its domain, and C is its codomain.

Definition 2. ( B -Graph [12]) A B -graph is a type of directed hypergraph with only B -arcs. A B -arch is a type of a hyperedge that is directed to a single head vertex, and away from all its other vertices.

Definition 3. (Activity Symbolic System) The activity symbolic system ( S , R ) is a B -Graph, where S is a set of vertices/symbols, and R is a set of pairs of subsets of S . Each of these pairs r = ( M,C ) ∈ R is a hyperedge/rule; the vertex subset M = { m i } n r i =1 is its domain/premises, and C = { c } is its codomain/conclusion. Equivalently, a rule takes the form r : ∧ m ∈ M m | = c . Since | C | = 1 , r is a B -arch.

Definition 4. (Decomposition of Activity Symbolic System) One conclusion c ∗ corresponds to one symbolic sub-system ( S c ∗ , R c ∗ ) , where S c ∗ ⊆ S , R c ∗ ⊆ R . ∀ r = ( M,C ) ∈ R c ∗ , C = { c ∗ } . ∀ s ∈ S c ∗ \{ c ∗ } , ∃ r = ( M,C ) ∈ R c ∗ , s ∈ M .

Def. 3,4 is depicted in Fig. 2. Def. 3 is based on Def. 1,2 and analysis above. In applications, we typically judge a specific conclusion with other symbols/rules removed. It is achieved by decomposing the activity symbolic system (graph) into sub-systems (sub-graphs) in Def. 4.

…

…

Symbol

Semantics

Image

Semantics

Semantic Coverage

…

…

Symbol

Semantics

Language

Semantics

Approximation

…

Symbol-Rule Loop

Logical Entailment

Approximation

Entailment

score

𝑒 = 𝜏(𝑟)

0

𝑒ℎ

Entailment Check

b)

c)

In a picture…

Estimate the

probability …

LLM

A rule

𝑟

Entailment  score

𝑒

Queue

head

tail

pop

push

Rule Extension

a)

…

…

…

…

Decomposition

Rules:

∧

⊨

∧

∧

⊨

∧

∧

⊨

∧

⊨

∧

…

√√√√

√√√√

…

…

√√√

√√

×

… …

...................

DIALECT!

10000

LANGUAGE

O

O

3.1.2 Two Ideal Properties

Existing methods [21, 20] can also be formulated under this structure. However, they fail to cover the complex patterns of activities and lack compositional generalization. To overcome the defects, we propose two important ideal properties: 1) symbols should be with broad semantic coverage to express different conditions in the activity image database; 2) rules should satisfy logical entailment [8] to add rationality and avoid ambiguity, i . e ., the premises set should lead to the conclusion without exception. They are formulated in Def. 5,6 and illustrated in Fig. 2b,c.

Definition 5. (Semantic Coverage of Activity Symbolic System) Given a very large-scale activity images database D = { ( I, A I , S I ) } ( I : image, A I : ground-truth activities happening in I , S I : ground-truth symbols happening in I ), then ∀ ( I, A I , S I ) ∈ D , ∀ s ∈ S I , s ∈ S .

Definition 6. (Logical Entailment of Activity Symbolic System) ∀ r = ( M,C ) ∈ R , M = { m i } n r i =1 , we have:

∀ ( I, A I , S I ) ∈ D , if M ⊂ S I , then c ∈ A I ;

∀ 1 ≤ i ≤ n r , ∃ ( I, A I , S I ) ∈ D , M \{ m i } ⊂ S I , but c / ∈ A I .

3.2 Instantiating the Symbolic System

To instantiate the proposed symbolic system, it is expensive to collect massive human knowledge via manual annotation as in HAKE [21, 20]. Instead, we use LLMs which have profoundly reshaped the acquisition of human knowledge and shown impressive language reasoning capabilities, i . e ., Symbol-LLM framework. In Sec. 3.2.1 and Sec. 3.2.2, we discuss the approximation of the two ideal properties with the help of LLMs and provide practical solutions, then a summarized pipeline is presented in Sec. 3.2.3.

3.2.1 Solving Semantic Coverage

Approximation. In Def. 5, the target domain of semantic coverage is a very large-scale activity image database, which is too costly to implement. For instance, HAKE [21] collect symbols from 100 K+ image via manual annotations, which is a heavy workload but still very limited. Here, the target domain can be replaced with semantic coverage of language models since natural language is another carrier of human commonsense knowledge. LLMs profit from large-scale text pre-training and imply abundant text-based human knowledge, thus can handle complex visual activity patterns without extra visual data. The approximation of Def. 5 is:

Definition 7. (Approximation of Semantic Coverage of Activity Symbolic System) Given an LLM L and an activity set A , ∀ A ∈ A , L implies a symbol set S A as premises of A , then ∀ s ∈ S A , s ∈ S .

Symbol-Rule Loop. In the implementation, generating rules connecting the symbols is non-trivial and challenging. Intuitively, we can ask LLMs 'What is the rule of the activity'. However, the instruction is ambiguous for LLMs to generate satisfying answers. More information should be provided to generate rules. Also, it is costly to generate all symbols and exhaustively query their connections. Thus, we propose to adopt symbol-rule loops to generate them alternately. Given a known symbol, we can add another symbol and extend it into a rule via prompting:

Then, the answer for ' [condition] ' is the extended symbol. Also, a candidate rule ' <known symbols> ∧ <extended symbol> | = <activity> ' is generated. Thus, we get a new extended symbol from this known symbol, and they are connected with rules. The new symbol can be repeatedly used as a known symbol to generate new rules. As is shown in Fig. 2b, a known symbol (the brown circle) is first 'popped out' from a queue, and new symbols (the green circle) and rules (the pink edge) are generated by extending the known symbol. Then, the new symbol is 'pushed in' to start the next loop. Thus, LLMs' implicit knowledge is fully exploited to satisfy Def. 7. To get the initial symbols, we ask an LLM to describe hand-related states related to the activity. The prompts mentioned above are determined via a trial-and-error process by human experts on randomly sampled activities (detailed in supplementary).

3.2.2 Solving Logical Entailment

Approximation. In Def. 6, it is costly to verify the logical entailment upon a very large-scale activity image database. Instead, we develop an entailment scoring function from an LLM based on its knowledge and language reasoning capability. As illustrated in Fig. 2c, as the rule extends from m 1 | = c , m 1 ∧ m 2 | = c to m 1 ∧ m 2 ∧ m 3 | = c , new symbols are successively added, and the measured entailment score increases. With the scoring function, the approximation of Def. 6 is:

Definition 8. (Approximation of Logical Entailment of Activity Symbolic System) Given a function τ ( · ) to measure the entailment score of a rule, and an entailment threshold e h , then ∀ r = ( M,C ) ∈ R , M = { m } n r , we have:

Entailment Check. As is shown in Fig. 2c, to implement the scoring function based on an LLM, we rewrite the rule r as a sentence and design the prompt as:

In a picture, <symbol 1 >, <symbol 2 > . . . <symbol n r >. Estimate the probability that he is <activity> at the same time. Choose from: (a)

Then, the output answers can be used as the entailment score e = τ ( r ) , whose credibility depends on the knowledge from the LLM. For one rule, the answering text is sampled five times and their average scores are taken as the final result to add stability. In practice, we set e h = 0 . 9 .

3.2.3 Summarized Pipeline

The pipeline of the symbolic system can be summarized as: given a target activity set A , or equivalently, a conclusion set C , we generate a sub-system ( S c , R c ) for each c ∈ C (Def. 4). The symbolic system is merged as ( S , R ) = ( ⋃ c ∈C S c , ⋃ c ∈C R c ) . Alg. 1 shows how to get the sub-system ( S c , R c ) for a given conclusion c . Details are explained in Alg. 1 comment and the supplementary.

3.3 Reasoning with Visual Inputs

With the activity symbol system constructed, we can utilize it to reason with visual inputs. As is shown in Fig. 3, visual information is extracted and checked based on the defined symbols and

Algorithm 1 Instantiating the Symbolic System

Human ride a boat?

…

𝑝 = 0.9

𝑝 = 0.5

𝑝 = 0.8

𝑝 = 0.7

𝑝 = 0.1

𝑝 = 0.3

Activity Semantic

…

𝑝 = 0.9

𝑝 = 0.7

𝑝 = 0.8

𝑝 = 0.5

𝑝 = 0.1

𝑝 = 0.3

Symbol Extractor

Symbol Checker

Fuzzy-Logic Calculation

𝑚2

(1)

,

𝑚1

(2)

…

𝑚1

(1)

𝑚3

(1)

, 𝑚

2

(2)

𝑚3

(2)

𝑚1

(3)

𝑚2

(3)

𝒎𝟏

(𝟏)

:

Hands operate the steering wheel

𝒎𝟐

(𝟏)

,

𝒎𝟏

(𝟐)

:

Hip seated in a boat

……

∧

∧

(

)

∧

∧

(

)

)

……

……

∧

⇒min(⋅,⋅)

∨

⇒max(⋅,⋅)

organized as a probability distribution on the hyper-graph. Then, the activity semantics can be reasoned out based on the rules and the probability of the symbols. We detail the key steps as follows.

Decomposition. Given the input image and the target activity/conclusion c , we first decompose the symbol system to exclude unrelated symbols and rules based on Def. 4. Thus, we get the rule set R c = { r ( j ) } N c j =1 , where a rule takes the form r ( j ) : m ( j ) 1 ∧ m ( j ) 2 ∧ . . . m ( j ) n r ( j ) | = c .

Extracting Visual Symbols. Given an image, to extract visual content and express it in a symbolic form, we measure the probability of each defined symbol (Fig. 3). In [20], the visual extractor is a customized model trained with annotations, which is costly, especially for our symbolic system with broad-range symbols. Instead, we utilize a simplified visual extractor based on existing System-1-like VLMs. To determine the probability of a symbol, we convert it into a text question and query the answer from a VLM. For example, given a premise symbol m ( j ) i ' Hip seated in a boat ', it is converted into a sentence 'The person's hip is seated in a boat. Yes/No?'. Used as a scoring function [1], the language model outputs the probability of answering 'Yes', 'No' as p ( j ) y,i , p ( j ) n,i . Then the probability p ( j ) i of the symbol can be obtained via normalizing the Yes/No answer as

Checking Visual Symbols. To ensure the reliability of the symbols' probabilities, we developed a checker module to filter out visual symbols with uncertain predictions. Each symbol is paraphrased into several variants, and the predictions of variants should be similar due to semantic consistency. Thus, the standard deviation of the predictions can be used to filter symbols with uncertainty.

Reasoning. With the symbol extractor and checker, the probability p ( j ) i for each premise symbol m ( j ) i in the rule set R c is measured. Then, the probability p c of the conclusion is reasoned based on premise symbols' probabilities and the rules. Premise symbols within a rule are connected with ∧ , while rules for a conclusion are connected with ∨ . We utilize the fuzzy-logic calculation [35], where

1

2

3

4

5

1

2

3

4

5

6

7

8

9

10

Round3 Rules:

6

→

1

→

7

→

8 :

omitted

7

→

1

→

11

8

→

12; 9

→

13; 10:

early stop

1

2

3

4

5

6

7

8

9

10

11

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13

1

2

3

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14

Round4 Rules:

11

→

9

→

14;

12

→

1

→

15;

13

→

9

→

16;

15

16

17

Round2 Rules:

1

→

6

→

7

→

8

2

→

1; 3

→

1

→

9

4

→

10

→

6;  5

→

1

Round1

Round2

Round3

Round4

, 5

Round5 Rules:

14:

early stop

15

→

1

→

17

the operator x ∧ y, x ∨ y are replaced with min ( x, y ) , max ( x, y ) respectively. Thus, we have

The fuzzy-logic calculation is more explainable and lightweight than the logical modules in [20], whose parameters need extra training samples. Benefiting from the sound definition of our symbolic system, it has the potential to achieve better performance even with simplified reasoning calculations.

4 Experiment

4.1 Dataset and Metric

We conduct experiments on image-level activity understanding benchmarks with diverse tasks: HICO [5] (Human-Object Interaction (HOI) recognition), Stanford40 [33] (action recognition), HAKE [21] -Verb (verb recognition), HAKE [21] -PaSta (conditional PaSta Q-A). HAKE [21]-Verb and HAKE [21]-PaSta is the newly constructed benchmark based on HAKE [21] data. We adopt mAP metric for HICO [5], HAKE [21]-Verb and Stanford40 [33], and top-1 accuracy metric for HAKE [21]-PaSta. For more details, please refer to the supplementary.

4.2 Symbolic System Experiment

An example. The LLM used is OpenAI GPT3.5. To demonstrate the instantiation of our symbolic system, we take the activity 'human board an airplane' as an example. Tab. 1 shows the symbols and rules generated, and Fig. 4 illustrates the detailed process. In the 1st round, 5 initial symbols are generated. Then in the 2nd round, they are extended into rules satisfying entailment. Later, the new symbols are used in the next round. Finally, 17 symbols and 12 rules are generated within 5

a)

b)

1

2

3

4

5

0

5

10

15

20

Count

symbols count

rules count

1

2

3

4

#Premise Symbols in the Rule

0.0

0.2

0.4

0.6

0.8

1.0

Entailment Score

Rule 0

Rule 1

Rule 2

Rule 3

Rule 4

Rule 5

Rule 6

Rule 7

Rule 8

Rule 9

Round

rounds. In practice, at most 15 symbols are used for rule extension to avoid semantic redundancy. For example, in Fig. 4, symbols 16 and 17 are not used for rule extension, leading to a stop in the 5th round. From Fig. 4 and Tab. 1, we can find that diverse symbols are mined, including corner cases such as 'adjust seatbelt' 'reach for the airplane door handle', verifying the effectiveness of the proposed symbolic system. Some special cases are shown as gray text. The rule '6 → 1 → 7 → 8' is equivalent to the existing rule '1 → 6 → 7 → 8', thus omitted. Extension from the symbols 10 and 14 stops early because adding premises nevertheless leads to a drop in entailment scores. In practice, multiple rules are extended from a known symbol to increase diversity.

Entailment. Fig. 5 shows statistics of the symbolic system. In Fig. 5a, we randomly sample 50 activities and show the accumulated symbols and rules count in each round, verifying the diversity of the generated symbols and rules. In Fig. 5b, we randomly sample 10 rules and show the entailment scores change as the rule extends with more premise symbols. For example, for rule 0: m 1 ∧ m 2 ∧ m 3 ∧ m 4 | = c , the entailment score for m 1 | = c, m 1 ∧ m 2 | = c, m 1 ∧ m 2 ∧ m 3 | = c, m 1 ∧ m 2 ∧ m 3 ∧ m 4 | = c are 0.28, 0.86, 0.87, and 0.93, respectively. The score is calculated by querying 5 times and averaged. We find a climb up in the entailment score , implying the effectiveness of the entailment check. Once the entailment score e surpasses the threshold e h = 0 . 9 , it is regarded as a rule equipped with logical entailment and updated into the symbolic system.

Performance Evaluation. To evaluate the symbolic system, we construct a SymAct (Symbol Activity) test set. It is a small subset of the HICO test set (120 images for 10 activity classes). For each image and each of its ground-truth activities, we list all of the symbols related to the activity in the symbolic system and annotate whether a symbol happens (0/1) in the image via human judgment. We find the proposed symbolic system has: 1) broader semantic coverage than HAKE. For quantitative measurement, we count the happening symbols for each image-activity pair under different symbolic systems. The average number is 10.8 for ours and 1.8 for HAKE. 2) more rational rules than HAKE. We evaluate confusion by counting different image-activity pairs which share the same symbols. For the HAKE symbolic system, the confusion problem is severe, with 261 confusion pairs over C 2 120 = 7140 pairs (accounting for 3.7%). For our symbolic system, there are no confusion pairs on the test set because of the presentation ability of symbols and entailment check.

Robustness. We analyze the robustness of the generated symbolic system. We find it satisfies: 1) Convergence. After a certain stopping criterion, a new round will bring few or even no new symbols to the sub-system. In practice, the stopping criterion is: At most 15 symbols are used for rule extension to avoid semantic redundancy. After that, new symbols are not used for rule extension, leading to a stop of the loop. 2) Low sensitivity to initial conditions. The symbol-rule loop provides a reliable and stable symbolic sub-system over a range of initial conditions. 3) Low sensitivity to different prompts. Experiment results are less sensitive to prompts with minor differences, i . e ., slightly paraphrasing the prompts. For more details, please refer to the supplementary.

4.3 Visual Reasoning Experiment

Implementation and Settings. For visual reasoning, we use BLIP2 [19] ViT-g FlanT5-XL model to extract visual symbols. BLIP2 [19] is a visual-language pre-trained model with a frozen large language model, thus well equipped with question-answering abilities to effectively extract symbols.

Symbol probability

I

II

III

IV

𝒎𝟏

: talk with seller

0.92

0.87

0.07

0.16

𝒎𝟐

: reach for an orange

0.14

0.68

0.95

0.12

𝒎𝟑

: seller hand over orange

0.94

0.82

0.09

0.13

𝒎𝟒

: stand in front of fruit stand

0.57

0.85

0.04

0.77

𝒎𝟓

: place orange in a bag

0.67

0.90

0.06

0.45

𝒎𝟔

: pick orange from a basket

0.68

0.83

0.03

0.74

𝒎𝟕

: hold a bag of oranges

0.47

0.88

0.05

0.75

𝒎𝟖

: reach for a wallet

0.86

0.83

0.04

0.43

𝒎𝟗

: seller put the orange in bag

0.83

0.86

0.04

0.45

𝒎𝟏𝟎

: give money to seller

0.82

0.87

0.04

0.41

I

II

III

IV

Rules for

𝒄:

buy orange

𝑟 1: 𝑚1 ∧ 𝑚2

⊨ 𝒄,

𝑟 2: 𝑚1 ∧ 𝑚3

⊨ 𝒄,

𝑟 3 : 𝑚 4 ∧ 𝑚5 ∧ 𝑚6

⊨ 𝒄,

𝑟 4: 𝑚4 ∧ 𝑚7 ∧ 𝑚8

⊨ 𝒄,

𝑟 5: 𝑚4 ∧ 𝑚9 ∧ 𝑚10

⊨ 𝒄

𝒎𝒂𝒙(𝒎𝒊𝒏 0.92,0.14

,

𝒎𝒊𝒏 0.92,0.94

,

𝒎𝒊𝒏 0.57,0.67,0.68

,

𝒎𝒊𝒏 0.57,0.47,0.86

,

𝒎𝒊𝒏 0.57,0.83,0.82 )

……

……

……

10元

3

0.92

0.85

0.07

0.45

Table 2: Results on activity benchmarks. More HICO [5] baselines are listed in supplementary.

As a plug-and-play , the reasoning is compatible with existing System-1-like methods. We follow the setting of HAKE [20] to integrate the reasoning result with the prediction of System-1-like methods. We mainly choose 2 typical System-1-like methods: 1) CLIP [26]: a visual-language model pre-trained with large-scale image-text pairs and a contrastive objective, where we use its ViT-L/14 model with a resolution of 336 pixels; 2) BLIP2 [19]: with its image-to-text generation ability, it functions either as a System-1-like baseline, or as the symbol extractor for reasoning. To fully test the ability of the models, we adopt both 'fine-tuned' and 'zero-shot' settings, where the former model is fine-tuned on the training set of the dataset.

Results. The results of visual reasoning are shown in Tab. 2. Comparing the two baselines, we find that with a frozen language model, BLIP2 [19] is more capable of understanding activity semantics and outperforms CLIP [26] in various zero-shot benchmarks. Under the fine-tuned setting, our method achieves 69.73 and 75.27 mAP respectively on HICO [5] and HAKE [21]-Verb, which outperforms the baseline CLIP [26] with 2.61 and 1.45 mAP respectively, verifying its effectiveness. Under the zero-shot setting, our method also outperforms existing System-1-like methods. The improvement compared with CLIP [26] is: 6.13 mAP (HICO [5]), 5.03 mAP (HAKE [20]-Verb), 6.54 mAP (Stanford40 [33]), 1.11 % acc (HAKE [20]-PaSta). The improvement compared with BLIP2 [19] is: 2.54 mAP (HICO [5]), 1.90 mAP (HAKE [20]-Verb), 0.74 mAP (Stanford40 [33]), 0.84 %acc (HAKE [20]-PaSta). In conclusion, our method brings performance improvement and shows significant zero-shot generalization abilities in various activity understanding tasks. Notably, the improvement is achieved by exploiting knowledge instead of adding visual training samples. The exploited knowledge can be reused for future study. Thus, our method is superior in data efficiency . Further, we visualize the results in Fig. 6. The rules and the logic calculation add explainability to the inference process. Also, they help to correct false negative (I, II in Fig. 6) and false positive (III, IV) cases, thus improving compositional generalization.

4.4 Ablation Study

We conduct ablation studies on HAKE [20]-Verb zero-shot setting and list the results in Tab. 3.

Reasoning. We first verify the effectiveness of integrating System-2 reasoning. Results of CLIP [26] baseline can be either combined with reasoning results or trivially combined with other baseline results (CLIP [26]+BLIP2 [19]). We find that the former is superior (48.95 mAP) to the latter (44.76 mAP), though both outperform the baseline CLIP [26] (43.92 mAP). It verifies the necessity of System-2 reasoning other than trivially combining predictions from two models.

Symbolic System. We then verify the effects of the symbolic system on visual reasoning. We adopt the symbolic system in HAKE [20] and find a degradation from 48.95 to 44.98 mAP, as its defects in semantic coverage and rule rationality, and the shortage is amplified in the fuzzy logic calculation without learnable parameters. Next, we construct the symbolic system without logical entailment, where each rule has 3 premise symbols without checking its entailment score. It leads to a dropped performance because of rules with errors or incomplete semantics. Then, the symbol-rule loop is removed, with only rules in the 1st and 2nd rounds updated to the symbolic system. As the diversity of rules decreases, the model suffers from a decreased performance. The importance of diversity is further verified by the performance drop when only 80%, 50%, and 20% randomly sampled rules are used.

Symbols Extractor/Checker. Next, we replace the symbol extractor BLIP2[19] with CLIP [26] and find a performance fall with a weaker ability to extract visual information. Also, reasoning without a symbol

checker suffers from degradation due to the negative effect of inaccurate symbol predictions.

4.5 Bottleneck Analysis

Based on the SymAct test set, we make further analysis of the bottleneck of System-2 reasoning. We investigate the impact of symbol prediction and symbolic system separately. With perfect symbol predictions, we assume the probabilities of symbols are definitely known as 0/1 instead of p ∈ [0 , 1] . The ground-truth symbols are annotated as explained in Sec. 4.2. With a perfect symbolic system, we assume that the generated rules cover all samples (image-activity pairs).

The performance drop (100.00 → 60.79 mAP, 83.11 → 41.52 mAP) indicates the symbolic system errors, i . e ., the generated rules have not covered all samples (image-activity pairs). The drop (100.00 → 83.11 mAP, 60.79 → 41.52 mAP) indicates the errors of symbol predictions, i . e ., symbol probabilities cannot be predicted accurately. In the supplementary, we also provide two failure cases that emerge from symbol prediction errors and symbolic system errors respectively.

5 Conclusion and Discussion

In this work, we rethink the symbolic system in activity reasoning and propose a new one with broad-coverage symbols and rational rules. Thus, enhanced System-2 reasoning is integrated into System-1. We demonstrate how to instantiate it and how it helps to reason with visual inputs. Our method shows superiority in explainability, generalization, and efficiency in extensive experiments.

Computation Cost. Symbol predictions will increase the computational cost as a trade-off for explainability and generalization. It can be eased by discovering the hierarchical and reusable nature of the symbols, which is detailed in the supplementary.

Broader Application. The method could be more general and facilitate real-world applications. We choose activity understanding as a good and important initial test bed because it is a difficult task with complex visual patterns and a compositional nature. To verify the broader application, we provide some initial results on object recognition and VCR [36] tasks in the supplementary.

Limitation. Rules generated from language models mostly boost System-1, but sometimes bring language bias, e . g ., the person only decides to buy an orange despite apples in the background. Further, the approximation is mainly based on pre-trained LLMs and can be improved with more elaborate designs, e . g ., human-in-the-loop, customized LLMs with higher-quality knowledge. Besides, System2 reasoning may be boosted if integrated into the System-1 training process. Also, instance-level activities and temporal encoding are beyond our scope since our main focus is on an overall framework to formulate and construct a novel symbolic system. We leave them to future work.

Acknowledgments and Disclosure of Funding

Some of the drawing materials in Fig. 1,2 are designed by Layerace / Freepik.

This work is supported in part by the National Natural Science Foundation of China under Grants 62306175, National Key R&D Program of China (No.2021ZD0110704), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), Shanghai Qi Zhi Institute, Shanghai Science and Technology Commission (21511101200).

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses  activity reasoning, where the task is to detect human activities given visual inputs. \nTo achiever compositional generalization for activity understanding, the paper propose a new symbolic system that is capable of relational reasoning.\nIn the experiments, the proposed system improved naive CLIP models showing its strong reasoning capability.\n\nSTRENGTHS:\nOverall, the paper addresses an important problem for integrating symbolic reasoning with neural perception models.  The paper demonstrates an instantiation for such an integration by providing formal definitions.  The paper conveys its idea with a good presentation using clear visualizations. The obtained results are interesting. \nThese contributions are beneficial for the community to build agents that can perform complex reasoning and understand the world better beyond simple visual perception.\n\nWEAKNESSES:\nMy major concerns regarding this paper are listed as follows.\n- The notation and semantics of the proposed method are unclear.  The rule is denoted using the logical entailment $\\models$. Is this different from implication $\\rightarrow$, which is commonly used for logic?  In Tab. 2 in the appendix, $\\rightarrow$ is used.  Since the definition of logical entailment is not provided in the paper, it is unclear how the rules can be understood as formulae. The implication and entailment articulate different concepts.\n- Related to the first point, it is not clear how the proposed symbolic system is related to other symbolic reasoners, e.g. standard first-order logic? What are the pros and cons?\n- The paper mainly addresses activity reasoning, but it is not well-described in the paper. Even though some visualizations are provided in the paper,  it is not easy to follow the arguments for readers who are not familiar with this topic. It would be beneficial to provide some simple examples throughout the paper.\n- Related to the previous point, the empirical setting is not easy to follow. E.g. on line 232 , it says, \"We follow the setting of HAKE [20] to integrate the reasoning ...\", however, this explanation is not self-contained. The experimental settings should be articulated more clearer in the paper.\n\n- Other minor issues:\n\t- Definition 5 is not clear. What are activity semantic and ongoing symbols?\n\nQUESTIONS:\n- The rule is denoted using the logical entailment $\\models$. Is this different from implication $\\rightarrow$, which is commonly used for logic?  (In Tab. 2 in the appendix, $\\rightarrow$ is used.)\n- How the proposed symbolic system is related to other symbolic reasoners, e.g. standard first-order logic? What are the pros and cons?\n\nLIMITATIONS:\nThe authors listed some limitations in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work is in the area of human visual activity reasoning and tries to improve System-2 (logic, deliberative reasoning) integrated with System-1 (that deals with intuitivity, associativity, and correlation) to improve explainability, generalization and efficiency which is demonstrated through empirical evaluation. This work attempts to improve over existing methods which use limited symbols, and are therefore is insufficient to cover complex patterns of activities for visual activity understanding. They do this by proposing a new system with broad-coverage symbols and rational rules.\n\nSTRENGTHS:\n-This work attempts to solve the lack of compositional generalization (generalized understanding of visual concepts, their relationships, and novel combinations) by creating a system of symbols with broad semantic coverage and rules that are rational and unambiguous. E.g., an existing work contains the following rule:\nHand hold ^ Feet close with |= human ride a boat. \nThis rule does not cover the complex patterns of human activities.\n\n- This work improves the existing methods by using an inexpensive annotation method by utilizing LLMs (OpenAI GPT3.5 API). This method is able to generate diverse symbols and rules quickly as shown in their symbolic system experiment. For the semantic coverage of the huge image dataset (the target domain), they replace it (the target domain) with the semantic coverage of language models.\n\nWEAKNESSES:\nThe core message of the paper appears to be that more symbols with greater coverage leads to improved performance.  Figure 4 provides a high-level view of how these symbols are generated from LLM output, and Figure 6 notionally shows how they integrate with visual processing, but technical details are missing or unclear. For instance, how are initial symbols \"extended into rules satisfying entailment\"?  In what format are symbols and rules stored?  What is the method for integrating the symbolic reasoning system into the overall visual processing pipeline?\n\nTheir method uses symbol-rule loop to generate new rules whereby they use a known symbol to generate a new symbol and connect it with the rules. The author(s) do not present examples to test the robustness of this method.\n\nQUESTIONS:\nMost questions given above in Weaknesses.\n\nFrom Fig 6, it is not clear if the method will generate the correct set of rules if the images also contained say apples in various circumstances in the background, but the person only decides to buy orange.  Can you please clarify this?\n\nLIMITATIONS:\nOne limitation regarding symbolic representations of continuous activities is mentioned.  The authors have not addressed negative societal biases, and I can think of cases where some societal biases might arise.  Activity-based reasoning is at its core a kind of \"stereotype\"-based reasoning: \"Balls are round.  Balls roll.  Apples are usually round.  Apples probably roll.\" In the domain of common objects, the risks of such stereotype-based reasoning are probably minimal, although if other objects with potentially harmful affordances (e.g., firearms or other weapons) are used without noting that those behaviors are harmful and should be avoided, this presents potential for abuse.  Can things like race, gender, or social class be represented symbolically? Should they?  Would symbolic representation of these bias system outputs in the presence of \"coded\" objects like firearms?  Beyond universal basic behaviors, who decides what the symbolic representations of them are? Without attending to these questions and more, there is a risk of attributing \"typical\" or \"stereotypical\" behaviors to certain individuals or groups of people, and risk generalizing them to other individuals, and hence reproducing patterns of bias and discrimination.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the use of a symbolic system for understanding human activity. The author argues that a good symbolic system for human activity understanding should possess a diverse range of symbols that accurately identify the human activity, and the rules within the symbolic system should be logically correct. The paper proposes the utilization of LLMs to guarantee these two properties and suggests building a comprehensive pipeline incorporating both LLMs and VLMs. Experiments demonstrate that the proposed system outperforms the baseline model, which solely employs VLMs, on multiple datasets.\n\nSTRENGTHS:\n- The paper is well-written and easy to follow.\n- The paper identifies two ideal properties of a good symbolic system: semantic coverage and logical entailment, which is very reasonable.\n- Utilizing the commonsense knowledge of LLMs to achieve these two properties is also promising.\nThe experiments have demonstrated the effectiveness of the model on multiple datasets.\n\nWEAKNESSES:\n- Some crucial details of the method are still missing:\n    - Regarding symbol initialization, it is not explicitly mentioned how the prompt in Appendix 3.3 (Line 79) is generated. It would be helpful to clarify whether it is manually written or generated using a template, and if so, what is the template.\n    - There are two versions of prompts used for rule extension in the main paper (Line 139) and the appendix (Line 84). In the main paper, a single known symbol is used, while in the appendix, all known symbols are used in the prompt. Additionally, in the appendix, the first sentence contains specific information related to the airplane case: \"In a picture, **there is an airplane**\". How this particular prompt is generated?\n    - According to Algorithm 1, symbols that contribute to a rule passing the entailment check should be removed from the candidate symbol list (Line 13). However, in Figure 4, after the 1→6→7→8 rule is generated, there are still rules such as 2→1 and 3→1→9 being generated. It would be useful to provide an explanation for this discrepancy.\n- According to Table 1, some generated symbols are more complex than the target activity, such as \"wave goodbye to loved ones,\" and some do not follow the format of \"<The person's hands/arms/hip/legs/feet> <verb> <object>\", like \"airline staff checking the boarding pass\". Given the complexity of the generated symbols, VLMs might face challenges in recognizing them in the images. Although experiments have shown that the current pipeline performs better than directly using VLMs on multiple datasets, there is no guarantee that VLMs can easily recognize the generated symbols.\n- While the author proposes two ideal properties that should be satisfied, the experiments only focus on the final accuracy of the models on human activity understanding tasks. The accuracy heavily relies on the performance of the VLMs employed. There is no evaluation of how well LLMs ensure these two properties. This makes it unclear what the contribution and bottleneck of the proposed system are. One potential experiment the authors could consider is to have a subset of the dataset and evaluate the pipeline with VLMs replaced by human judgment. But other experiments are also appreciated.\n\nQUESTIONS:\nSee my comments in the weaknesses section.\n\nLIMITATIONS:\nThe authors have a paragraph discussing the limitations of the proposed method in the last section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, a symbolic-System-2 submodule is integrated with a System-1 module to perform human activity recognition. The work follows the intuitive idea, where a visual prediction module extracts visual symbols and a symbolic reasoning module performs logical reasoning to recognize activities. For the visual symbol extraction module, the work applies a general VLM, to predict the truth value of a predicate in a logic clause. The logical system then performs fuzzy logic to predict the final outcome of the rule, ie, conclusion. The logical system is constructed from LLM, without expensive human annotation in existing works. In experiments, it is shown that the model is better than existing baselines.\n\nSTRENGTHS:\n+ The work leverages and extends the intuitive idea of System-1 and System-2 combination. The integration is important compared to System-1 only pure predictive framework and it's really encouraging to see more and more works integrating these two systems in typical vision tasks.\n+ Compared to existing works, the method leverages LLM for knowledge-base extraction. While the cost saving is from LLM, rather than some nice problem structure design, I do appreciate the efforts to construct formal logic base from natural language systems and make it work.\n\nWEAKNESSES:\nI'm not an expert in logical system design so I will refrain from commenting on the system design part on logics. However, I do see some general problems regarding this work.\n\nFor one, designing logical system specifically for a task risks overfitting. While the performance is improved on a specific task, I do note that the slight increase may not be worth the generality from models like CLIP. Besides, the logic system is pretty complicated. And I do not think the system, or the system construction method could be easily adapted for other tasks. On the other hand, such a system is also only demonstrated in a single task. I doubt if the method could be more general. \n\nI'm also interested in seeing more detailed ablation study. In particular, whether it is the System-1 part of the System-2 part that more adversely impacts performance. Would it be possible to assume perfect System-1 and test the System-2 performance or vise versa? Besides, while in general the idea should work. There are many subtleties in telling the truth value of statements, eg, the leg is ``close'' to something. How should such subtleties be addressed? \n\nThose being said, I admit the inherent conflict between logical system that are delicate, explainable, robust, and generalizable and deep learning system that are general but fragile. I do not see these points as weaknesses but rather interested in hearing how the authors see them.\n\nFor my final rating, I'd like to sync with fellow reviewers.\n\nQUESTIONS:\nSee above.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-14"}

comments: SUMMARY:
The paper addresses the problem of visual activity understanding and proposes a way to leverage large visual and language models to i) propose symbols from an image, ii) derive new symbols via logic entailment and perform classification. The results show that the augmented system with symbolic reasoning capabilities performs slightly better than the equivalent large models without the reasoning system.

STRENGTHS:
The paper proposes an interesting way to leverage large visual and language models to derive a set of symbols relevant to a particular picture and the action being executed in it. Constructing an ontology based on the compiled knowledge represented in an LLM seems to be a powerful approach to enable task specific higher level reasoning.

WEAKNESSES:
One of the key improvements over other related work is claimed to be the broad semantic coverage of the derived symbols and the generated rules. However, given the statistical nature of the used large models it is not clear how accurate these symbols and rules actually are. A deeper analysis of the generated ontology is definitely needed in order to assess the applicability of the proposed method.

QUESTIONS:
- How is the set of the initial symbols selected? Isn't this initial set generation prone to the same issues as some of the related works? 
- Given the form of each rule having multiple premises and a single conclusion, why is the hyper-graph structure needed? Wouldn't a simple graph with multiple directed edge be enough?
-

LIMITATIONS:
The core of the proposed work is based on utilising pre-trained large models which require vast amounts of data and computational resources and so the proposed method is as efficient as described only by assuming access to such large models.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2023-07-25

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes to increase coverage and generalization of logic-based methods to model 'system-2' or deliberative reasoning for the task of human activity prediction in images. Specifically, the authors incorporate an LLM (GPT3.5) to obtain a larger number of symbols and inference rules related to human activity prediction and further also utilize an LLM to check logical entailment of generated rules (based on which noisy rules are filtered). Their proposed symbolic system operates upon a VLM which is used for symbol extraction from the image, based on which rules are activated with a fuzzy-logic computation (to obtain a probability score for the inference chain). \n\nAuthors show that adding their method on top of two VLMS (CLIP and BLIP2), enhances their performance on prominent activity recognition benchmarks and also show visualizations of activated rules and predictions besides ablation studies of their method.\n\nSTRENGTHS:\n1. The usage of LLMs to broaden semantic coverage of symbolic/logic-based methods by discovering relevant premises and rules and also for checking/approximating logical entailment is relatively novel. \n\n2. The methodology and formalism of their approach is well described, with appropriate examples and figures/tables to help reader understand the approach.\n\n3. Results on multiple prominent benchmarks (including HAKE, HICO and Stanford-40) for human activity prediction show that the method can benefit both zero-shot (for BLIP2 and CLIP) and finetuned (for CLIP) performances of prominent VLMs. Ablation results also highlight contributions of individual method components/variations.\n\nWEAKNESSES:\n1. The symbolic system's application draws on using a VLM multiple times to extract the probability of individual symbols. It thus seems that the computation will linearly scale with number of symbols, which can be a significant drawback due to the incurred computation cost.  (VLMs such as BLIP2 by themselves have very large computation cost, hence even increasing that 100x for 100 symbols is a very significant additional compute). Further, extending this to further domains beyond human-activity recognition would require many more symbols, and ultimately possibly not be scalable. \n\n2. Evaluation baselines only consider results of the backbone VLMs currently. There are multiple recent works in related direction of human-object interaction and activity prediction in zero- or few-shot scenarios which have not been mentioned or used as baselines (e.g. Weakly-Supervised HOI detection (ICLR 2023); RLIP (NeurIPS 2022)). Currently only RelVIT is mentioned in supplemental. Authors should either compare with such methods or mention why these recent methods are not used/suitable for comparison. \n\n3. Further, since LLMs are employed for much of the 'system-2' reasoning, authors should consider reporting results of alternative methods using VLMs + LLMs directly without any logical system (e.g. VLM generates image caption, based on which LLM predicts possible activity when prompted for each activity in the label space -- similar to PICa in  \"An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA\" AAAI 2022). These results could better highlight the contribution of the proposed logical system and that performance increments are not due to just LLM-VLM combination.\n\nLess important:\n1. Failure cases could be illustrated for a small sample of the questions to better highlight which component is currently leading to errors (e.g. is it the VLM not producing accurate symbols or is it a noisy rule, etc)\n\nQUESTIONS:\nPlease see weaknesses. \n\nMy primary concerns (impacting rating) are regarding:\n1. Does compute scale linearly with number of symbols and would this be a major limitation in practice? If so, can this can be addressed through alternative symbol grounding/identification methods? \n2. The relatively limited evaluation compared to other relevant works for human activity prediction. How does the method compare to related baselines in human-object interaction learning such as ICompass (Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations (ICCV 2021) and RLIP (NeurIPS 2022)) or alternatively multimodal LLM methods (e.g. PICa in \"An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA\" AAAI 2022)?\n\nLIMITATIONS:\nPotential negative societal impact is mentioned to be minimal.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-08-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel method for visual activity understanding using an enriched symbolic system. The authors focus on enhancing the integration of intuitive (System-1) and logical (System-2) reasoning processes in AI systems. They argue that the current System-2 reasoning methods suffer from limitations such as a dearth of symbols and limited, ambiguous rules, inhibiting the system's ability to capture the complex patterns of activities and limiting its explainability, generalization, and data efficiency.\n\nIn response to these shortcomings, the authors introduce a new symbolic system enriched with a broader set of symbols and more rational rules. This system, underpinned by commonsense knowledge, aims to foster a more comprehensive understanding of human activities visually. To support their work, they leverage the advancements in LLMs and techniques like the symbol-rule loop and entailment check.\n\nSTRENGTHS:\nThe concept of developing a new symbolic system that combines aspects of intuitive (System-1) and logical (System-2) reasoning is indeed a creative approach. However, the idea of incorporating commonsense knowledge into AI reasoning is not entirely novel, as there have been previous works exploring similar notions.\n\nThe authors put forward a detailed methodology and apply recent advancements such as Large Language Models (LLMs) in their approach. \n\nWhile the paper is mostly well-written, certain parts, especially the description of the symbolic system and its instantiation, could benefit from more elaboration or clearer explanations.\n\nAlthough the paper targets a significant area in AI - the integration of System-2 reasoning into visual activity understanding - it's unclear how much practical impact this specific method would have on the broader field. The lack of comparison with other methods, especially in various real-world applications, makes it difficult to evaluate the actual significance of their work.\n\nWEAKNESSES:\nThe paper's evaluation approach, while it encompasses multiple datasets, does fall short in providing a comprehensive set of comparisons with current state-of-the-art methods. The selected baselines do not reflect the full breadth of existing approaches, which undermines the claim of superiority of the proposed method. It would be valuable to include comparisons with more recent vision-language models, such as LLaVA, MiniGPT-4, and LLaMA Adapter, to provide a more meaningful assessment of the proposed system's performance.\n\nThe paper does spend a substantial portion of its content introducing the method background. Shifting sections 3.1-3.2 to the appendix could help maintain focus on the main contribution of the paper, while still providing the necessary context for interested readers.\n\nThe explanation of the core methodology is another area that requires improvement. It is not adequately detailed in the introduction, with key aspects of the proposed method encapsulated in a single sentence (lines 57-58). The paper would benefit from a more thorough and step-by-step explanation of the modeling method in the early part of the paper, such as the introduction, which would help readers better understand the unique features and operations of the proposed system.\n\nQUESTIONS:\nMissing related references:\n- Knowledge Aware Semantic Concept Expansion for Image-Text Matching, IJCAI 2019.\n\nIt would be beneficial to include more latest baselines for comparison.\n\nLIMITATIONS:\nThe authors adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-08-02"}