{ "paper_id": "C82-1001", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T13:13:14.323289Z" }, "title": "", "authors": [], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "C82-1001", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "When we expect a computer to recognize objects, the lodels of them ~must be given to it, however there are cases where some objects may not be,matchedto the models or there is no model with which object is compared.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "At that time, this system can augment or create new descriptions by being explicitly taught using verbal instructions.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "We have reported the story understanding system which uses both linguistic and pictoriallinformation in order to, resolve the meaning of givenisentences and images.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "In this research, we have believe that a correct meaning of the given sentences is obtained if the relations among noun .phrases, which correspond I to objects in the images, consistent with the relations observed among objects in the images. The Jfact that this identification : of objects and the interpretation ~:of the given sentences supplements each other simplifies both the detection of objects and disamibiguation of word sense or prepositional groups.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "In Spite of these effects, this forlalisn has a defect that it requires additional knowledge sources, the model of objects that will appear inpthe images.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "All of models of objects or actors that are supposed to appear in, the picture must be given to our system in order to achieve its purposes. But it is not easy for us to store all of such models in a computer.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "If a person who does not know. well about the details of this system wants to interact,with it~ he will give up to use the system, as he knows nothing !of the representation~ of models in the computer. To make matters worse, there, are quite many variations in real objects which we will. encounter in the real world.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "For example,we can see various type of houses. ~:In the traditional AI system, :a generic model is utilizedJ to identify such class of objects. But it is not easy for such a system to discriminate idiosyncrasy of varous objects, iFig.l shows a part~ of sample story used to experiment its story understanding capability. Even if the system is supposed to be given a generic model (for example, BOGLE) that represents both OBAQ and OJIRO, the system will not be able to discriminate them.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "The system needs some ,proper model for 0BAQ and OJIRO. But if a new character which has some similar points to;0BAQ and OJIR0 apperes in the story, some modificationsl to the BOGLE model are required., Thus generalization process could not be acomplised in advance, but should be achieved through experiance.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "When, we are asked to do some task, we are usually given informations concerning to the objects of that task and ~their processing method.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "In case where we encounter some~unkown objects ~n the course of the taskL we can construct aJ more generic ,model including them ~together with a,ereation of instance models for those individuals by d~manding an explanation to a person who knows well about those objects.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "In this~real situation, it cannot be expected that a learning process proceeds successfully like the experiment studied :by Winston, as the assumption fails of success that the samples can be arranged conveniently for the learning. We usually augment our knowledge by explicitly being taught about missing or insufficient parts of the known models. In order to realize this type of learning, there are two important problems to be solved. First is an explanation capability. Unless a capability to convey one's obscure points ta his partner:is given to the system, it is difficult for the system to obtain good instructions from its partnerL Second is a::point that fromiwhat kind of levels of knowledge state the system~:sheuld start its learning process. Should an initial state of~knowledge be given in forms of an inner representation or be explained in natural language? We select the former approach by just the following reason. Ve think it quite difficult to give a clear view to unknown object without referring to models. So we restrict a class of objects learned by our system:to the group of objects of which the system can .obtain clear views concerning to their conditions through the comparison with their similar example.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "But the assumption is not required that, examples should be different in only one or two points at most from the unknown object. Many, discrepancies between the object and its models are permitted to exis~ because such differences can be explained explicity in the language, by a teacher.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "And through a cognition of analogical or discrepant points of objects belonging to the same conceptual class, a generalization process is invoked that creates a common concept to them.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "IntrodUction", "sec_num": "1." }, { "text": "The 'model description used in this paper is, the same one shown in the paper[I] except for the usage of the frame representation to describe~ relations among subpartsof the model. Let explan using an example. Fig.2 shows the OBAQ, who is an actor of the 'sample story shown in Fig.li To describe location of subparts of this model, its main part is enclosed by a rectangle as shown in Fig.2 . Then this rectangle is devided into 9 subregions and the location of its subparts, is described in terms of these subregions. Yhen some of these subparts ihas also subparts, they:are hierarchically described in the similar way.", "cite_spans": [], "ref_spans": [ { "start": 210, "end": 215, "text": "Fig.2", "ref_id": null }, { "start": 278, "end": 284, "text": "Fig.li", "ref_id": null }, { "start": 386, "end": 391, "text": "Fig.2", "ref_id": null } ], "eq_spans": [], "section": "~. Description for Object", "sec_num": null }, { "text": "And the relations between these subparts is represented using the frame, structures. The frame structures corresponding to:the DBAQ model is given in Fig.3 (this figure shows a hypothetical model of OJIRO obtained from the copy of OBAQ frame,)", "cite_spans": [], "ref_spans": [ { "start": 150, "end": 155, "text": "Fig.3", "ref_id": null } ], "eq_spans": [], "section": "~. Description for Object", "sec_num": null }, { "text": "The slot AKO means a well-known relation A-KIND-OF, ana the CLASS indicates whether the frame is gneric or instance frame. If the frame is generic, then it has two slot, GEN~ recording its lower clas~ of generic frames and [NST recording its instance frames.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FraRe Representation", "sec_num": "3." }, { "text": "The F[G slot represents a pictorial reration, to its parent frame. This slot means that the part corresponding to this frame is a subpart of the ,frame stored in the PART and that it can be found by looking for the region designated in POS-And the facet 01R describes a relation which this ~ art has to its parent~ There are three relations concerning to the", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FraRe Representation", "sec_num": "3." }, { "text": "In as shown in Fig.2 and concering to the POS, many combinations of subregions are ~permitted which can be expressed with the symbols, L,C,R.and U,C,D.,: Especially the symbols o ** are used to designate the locations shown in Fig.4 . The slot SH~P represent whether the part corresponding to this frame is a region(~EG) or a branch(BR A) The SU~P slot records its subparts and their locations of or reiations to this part are described in three facets as sho~n above. EspeCially when the ~HAP condition is 0~A, th~s frame has a ~BB slot ~nstead of The shape of the model is e pink region, and that of the object is a red branch.", "cite_spans": [], "ref_spans": [ { "start": 15, "end": 20, "text": "Fig.2", "ref_id": null }, { "start": 227, "end": 232, "text": "Fig.4", "ref_id": null } ], "eq_spans": [], "section": "FraRe Representation", "sec_num": "3." }, { "text": "The mouth of the model has a lip, but that of the obJect has not subpart. T: OJIBO'S NOUTH IS A RED LINE. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FraRe Representation", "sec_num": "3." }, { "text": "We would llke to explain how the dialog shown in 5. has been obtained.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "By (I~ the program generate OIJRO model from OBAQ model shown in Fig.3 :~ and constructs a frame tree consisting from a BOGLE frame and the OBAQ frame, is stored as an instance of the BOGLE frame. But it records~in its STM thatlthere is just one hair as the teacher said so. Next, C~looks for a candidate region of OJIRO using the copied model. In.the second frame, OBAQ, TABLE, APPLE, CLOCK and OJIRO are drawn, but as the first four,objects have been appeared in the first frame, in this case C can find OJIRO by looking for new objects. But regrettably a color of the region (yellow) which seems to be OJ-IRO'S. body(J-BODY) being different from that of the model(white), this cause a complaint shown in (2) and by accepting a T's agreement C can believe its correctness and T can also think C in a right state. Consequently, C changes value of C0L in J-BODY into YELLOW.", "cite_spans": [], "ref_spans": [ { "start": 65, "end": 70, "text": "Fig.3", "ref_id": null } ], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Next~ C tries a verificationiof J-HAIR which is the ~first member of Scots'r; where Scou'r={J-HAIR,J-HAND} As C can be aware of the fact that J-HAIR is a hair by its AK0 slot and that there is a note on the hair in STM, it can know that 0JIR0'S ~hair cannot be recognized only by referring~to the copied model.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Since the just one alteration in the number of hairs is recordedf there,, C thinks their location to be same as tl~ model specification, end can find a line in the ((C)U) part of J-B0bY. It ends the ve~ffication of J-HAIR by storing (H1 NIL). into SUBB ~lor io place of:(L1 NIL L2 NIL L3 NIL). In a similar ~ay to this, C begins to identify J-HAND, however C can be aware of that it should 10ok for J-R-HAND and J-L-HAND, as ~here is a CONCEPT slot in J-HAND. So C succeeds in the identification*of them~because.of a perfect match in their locations, colors and substructureS. The result of this steps is reported in (3). Next,. the identification process proceeds to Sin and C starts a verification of J-MOUTH,~where SIxffi(J-MOUTH, J-EYE). As the locational constraint for this part is ((**)C), which means that it occupies ((L)C), ((C),C) and ((R),C) of J-BODY, the check is attempted whether just one candidate can be found for each of.these 3 subregions.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "In this case, nothing is found for ((L)C)land ((R)C) but several parts are found in ((C)C) of J-BODY. So this process is suspende d and identification of other parts (J~R-EYE and J-L-EYE) is attempted, but the same ambiguity as the above occurs and this causes the identification steps to be suspended.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Consequently, for each one of these 3 parts,,their results are just same ~each other; there are 3~parts in the ((C)C)of J-BODY and theylare candiades for J-MOUTH, J-R-EYE and J-L-EYE. Then C avails of the relational constraint onilocations of them in order to clarify their correspondences as far as .possible. It infers, that J-MOUTH ,probably locates in a lower position than J-EYE, because the location*of J-MOUTH is ((**)C) and that of J-R-EYE and J-L-EYE is ((L)U) and ((R)Ut respectively (in this example note that the location of J-EYE, ((**)U) can be also available)~ And it is also.decidable if which black region corresponds to J-L(R)-EYE using, the relation between ((L)U) and ((R)Ut.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "By this assumption on availabilty of the relational constraints, C can discover one possible correspondence between the model and object.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Then other properties are tested: But regrettably, discrepancies are found for both his mouth and eyes. The candidate for his mouth is a line segment, whereas the model says that it is a region and that it has a substructure.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Similary the candidate for his left (right) eye is a black region,but its model description is that it is a white region with. a substructure.. At the present.state of program, any estimation on which is more plausible is.not realized regarding to the accordance of these properties, C simply complains about their disagreements in the order of their discovery.", "cite_spans": [ { "start": 36, "end": 43, "text": "(right)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "Therefore it at first complains of his mouth as shown in (4). Given teacher*s instruction on a. shape of mouth, C is convinced of his decision and add a new slot SUBB in plac e of SUBP and records (H~ NIL) into it becase it has found that his mouth is not a region but a line segment. Here instead of the instructionJ(9), T can say that C should be believe the given image correct. In that case, C suppose its decision to be right and does the same thing as the above. The difference between these two cases is that the latter has a high risk in the correctness of its conclusion. Next, C complains about the discrepancies of his eyes. Note here that nothing is stated, about his left-eye oncean instruction on his right-eye is given to it, because they have the same properties concerning to both their models and object parts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "In case where one of them is not same, a question is asked:about the difference by:C.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Example", "sec_num": "5." }, { "text": "As mentioned in 4., OBAQ frame causes BOGLE frame to~be generated as a generic ene, and OJIRO frame ~is obtained through learning process. At present our program ~ust makes frame, trees in which OJIRO and OBAQ frame are child of BOGLE.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T. Use of Generic Frames", "sec_num": null }, { "text": "A here are many sentence generating and explanation systems, however an explanation system like this research has not been investigated in the point that our system tries to give its partner an explanation or pictorial features of objects to:be modeled by translating sentences not from the case frame, of sentences but from frames corresponding to the pictorial models. Naturallysuch an explanation is on locations, shapes, :colors and relations that models or objects have, and must be given in, the forms that the partner can easily understand what the system knows. 'For this purpose, the explanation on locations is first attempted using the referred things in the dialog, and is finally given in an absolude coordinate based on the 9 subregions if there is no reference or the reference stack becomes empty. (4),(5),(6) in the Scenario~ shows Toyonaka Osaka JAPANIn this paper ae attempt of learning by verbalism isshown inordertocreatethe models for an~identif~cationof unknownobjects.", "html": null }, "TABREF1": { "num": null, "type_str": "table", "text": "", "content": "
this standard and to use other methods such as similarity SUOPslot and a branch structure is recorded here. An example :of in relations branch is shown or shapes or colors of objects. ,As we usually employ these variety in Fig.5. The COL slot records a color of this part and a slot CONCEPT means that this frame is prepared for way to detect analogy and record these experinces into our memory, we the conceptual consistency' of frames and not for pictorial relation. can easily decide what method should be used to compare things. We can In additiontto these slots, there are several slots, not say that the program has learned something untill these mechanisms WAKE, SEX, NUM and so on. These are prepaired ,to generate a sentence for stating a recording a standards to compar things into memory are realized in the
reason why thisl program.frame isrequiredor an explanationaboutwhy
discrepanciesbetween an object and its model can he found out in its
matching:process. 5. Scenario
C: What Is oJtro?Do you need more exp-lanation?(el
4. Basic:Strategy T: OJIRO IS OHA@'S BItOTEER, of Learning The system tries to generate,a C: Does oJiro resemble.obaq? referring to an analogical model and using a teacher's T: YES. model for the unknown object C: The mouth Is all the middle T: YES. BUT OJIRO HAS JUST ONE HAIR. (1) his body, and that of the object instruction, C: A color of his body Is not white. the center of the middle patr of hie* part by is in of
and simultaneously time, the first locations of subparts of objects. it augments the concept trees~of key for a detection When we are told the same location as the refered object\u2022 are many examples that resemblance in a Iocation~ is C: I have found a red branch to the left his hair. l have found his left hand. I have found his r'ight, hand. But I cannot find his mouth. (3) T: WHAT ARE YOU DOING? object in C: l have found hie body. I have found T: YES. of analogy is assumed to be in objects. At that Of course,,there not useful hut an unknown a color of OJIRO'S BODY is yellow) (2) that It is yellow. Is it true ? (note~ that body.
preventscthe of his right.hand program from achieving as a candidate of his a correct detection that case, the teacher should explicilty tell the program of analogy. At to month. But it is different from his model. ignore Do you need another explanation of the
o,/111o AKO location CLASS SUBP WA/~: SEX T: YES. C: Do you want an explanation SVAL of his mouth ? BOCLE SVAL IKSTAffCE 8VAL J-BODy IVAL GIVEN SVAL r/Aft coordinate ? T: NO.(4) J-BODY AKO CLASS FIC in:absoluteSVAL SVAL PAR~ DIRBODY l I~STABCE OJ II10 IN
C: It Is to the rightof hisleftSHAP hand inSVALREG
J-F~gO AKO CLASS the image. sVAL SVAL nation in absolute HAND Do you want another coordinate? I NS T~d~CE F 1C p A/~ J-BODY DIR (OR (coUT) (CII~)) \" POS ((*) C) SHAP $V-~& PEG SUBP SVAL (J-R-HAND J-L-HAffD) COL $'\u00a2AL T-\" YES. C: It is in the~ center:of the middle expla-J-HAIR SUBP COL (5l AKO CLASS part FIC of his body In the imoge. (6) T: EXPLAIN THE DIFFERENCE BETWEEN THE MDDEL AND OBJECT. ~dVAITE ND~ 8VAL TWO CO[~CEP\u00a2 8VAL T SHAP SUBB C: On what part do you want an expler~ation? COL T: IqDUTII. (7) lqUI~ C: The mouth of the model is different from that of the model in the point~ of J-EYE AKO SVAL CLASS SVAL INSTANCE FIG PAR~ DIK POS (Izl SHAP SVAL SUBP SV/~L NU~B COL $VAL. CONCEPt\" $VAL T COL WHITE SUBP IVAL SHAP (j-R-EYE J~'L-EYE) KEG U) FIG IR CLASS J-BODY AKO J-HOUTH EYE location, shade, subpart and color.SVAL SVAL 8VAL $VAL PAP~ DIK POS 8VAL SVAL SVAL .$VAL SVAL \" 8VAL IVAL PO~ DIR PAR~ SVAL $VAL(J-HOU'I~ J-EYE J-HAIR J-HAND) WHITE HAIR I NSTANCE J-BODy COUT ((C) U) BOA (LI NIL L2\"NIL 1,3 NIL) BLACK THREE P INK J-LIP PEG ((Z*) C) IN J-BODY INSTANCE NOUTH
J-LIP AKO CLAS~ FIG SHAP SUBB COLSVAL SVAL pAI~ DIB. pOS SVAL eVAL SVALLIP INSTANCE J -HOUTH IN ((~z) C) BRA (L4 NIL) REDJ-R-EYE AKO CLASS FIC SHAP SUBP COL$VAL 8VAL PAP~ DIP. POS SVAL $VAL 8VAL Fiq 3. Frame copied from OBAO frame. RI CHT~EYE J-R-IDtI~O I[~STANCE kKO RI G~T-HAND SVAL J-BODY CLASS INSTMICE eVAL IN FIG J~BODY PkP~ ((L) U) cOUT Dill KEG ((L) C) POS J-K-pUPIL SHAF $VAL KEG ~I~ I TE . COL SVAL WHITE
Cone of these =((*) u)BS
In/
B2mBranches
B?B9
\u2022B10
allof thes
pu~ it, = ((*-X-)D) He ~lls OJ|RO. HI gives O$IRO the apple. Fig 2. OBAq Fig #. *, Cout ** and 03IROOJl~O takes ~t. (B1 (B~ NIL B3 NIL B4 (B5 NIL B6 NIL B7 A sample (B8 NIL B9 NIL B10 NIL)))story
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